Cloud Programming Simplified: A Berkeley View on

Serverless Computing

Eric Jonas
Johann Schleier-Smith
Vikram Sreekanti
Chia-Che Tsai
Anurag Khandelwal
Qifan Pu
Vaishaal Shankar
Joao Menezes Carreira
Karl Krauth
Neeraja Yadwadkar
Joseph Gonzalez
Raluca Ada Popa
Ion Stoica
David A. Patterson
Electrical Engineering and Computer Sciences
University of California at Berkeley

Technical Report No. UCB/EECS-2019-3

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-3.html

February 10, 2019

 Copyright © 2019, by the author(s).
All rights reserved.

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Acknowledgement

The sponsors of the RISELab are Alibaba Group, Amazon Web Services, Ant
Financial, Arm Holdings, Capital One, Ericsson, Facebook, Google, Huawei,
Intel, Microsoft, Scotiabank, Splunk, VMware, and the National Science
Foundation.
 Cloud Programming Simplified:
A Berkeley View on Serverless Computing
Eric Jonas Johann Schleier-Smith Vikram Sreekanti Chia-Che Tsai
Anurag Khandelwal Qifan Pu Vaishaal Shankar Joao Carreira
Karl Krauth Neeraja Yadwadkar Joseph E. Gonzalez Raluca Ada Popa
Ion Stoica David A. Patterson

UC Berkeley
serverlessview@berkeley.edu

Abstract
Serverless cloud computing handles virtually all the system administration operations needed to make it
easier for programmers to use the cloud. It provides an interface that greatly simplifies cloud programming,
and represents an evolution that parallels the transition from assembly language to high-level programming
languages. This paper gives a quick history of cloud computing, including an accounting of the predictions
of the 2009 Berkeley View of Cloud Computing paper, explains the motivation for serverless computing,
describes applications that stretch the current limits of serverless, and then lists obstacles and research
opportunities required for serverless computing to fulfill its full potential. Just as the 2009 paper identified
challenges for the cloud and predicted they would be addressed and that cloud use would accelerate, we
predict these issues are solvable and that serverless computing will grow to dominate the future of cloud
computing.

Contents
1 Introduction to Serverless Computing 3

2 Emergence of Serverless Computing 5

2.1 Contextualizing Serverless Computing . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Attractiveness of Serverless Computing . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3 Limitations of Today’s Serverless Computing Platforms 9

3.1 Inadequate storage for fine-grained operations . . . . . . . . . . . . . . . . . . . . . . 12
3.2 Lack of fine-grained coordination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.3 Poor performance for standard communication patterns . . . . . . . . . . . . . . . . 13
3.4 Predictable Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4 What Serverless Computing Should Become 15

4.1 Abstraction challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.2 System challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.3 Networking challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.4 Security challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.5 Computer architecture challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

5 Fallacies and Pitfalls 20

1
6 Summary and Predictions 21

7 Acknowledgements 23

8 Appendix. More Depth on Five Applications that Stretch Today’s Serverless

Computing 29
8.1 ExCamera: Video encoding in real-time . . . . . . . . . . . . . . . . . . . . . . . . . 29
8.2 MapReduce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
8.3 Numpywren: Linear algebra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
8.4 Cirrus: Machine learning training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
8.5 Serverless SQLite: Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

2
1 Introduction to Serverless Computing

The data center is now the computer.

Luiz Barroso (2007) [1]

In 2009, to help explain the excitement around cloud computing, “The Berkeley View on Cloud
Computing” [2] identified six potential advantages:

1. The appearance of infinite computing resources on demand.

2. The elimination of an up-front commitment by cloud users.
3. The ability to pay for use of computing resources on a short-term basis as needed.
4. Economies of scale that significantly reduced cost due to many, very large data centers.
5. Simplifying operation and increasing utilization via resource virtualization.
6. Higher hardware utilization by multiplexing workloads from different organizations.

The past ten years have seen these advantages largely realized, but cloud users continue to bear a
burden from complex operations and many workloads still do not benefit from efficient multiplexing.
These shortfalls mostly correspond to failures to realize the last two potential advantages. Cloud
computing relieved users of physical infrastructure management but left them with a proliferation of
virtual resources to manage. Multiplexing worked well for batch style workloads such as MapReduce
or high performance computing, which could fully utilize the instances they allocated. It worked
less well for stateful services, such as when porting enterprise software like a database management
system to the cloud.1
In 2009, there were two competing approaches to virtualization in the cloud. As the paper
explained:
Amazon EC2 is at one end of the spectrum. An EC2 instance looks much like physical
hardware, and users can control nearly the entire software stack, from the kernel upward.
... At the other extreme of the spectrum are application domain-specific platforms such
as Google App Engine ... enforcing an application structure of clean separation between a
stateless computation tier and a stateful storage tier. App Engine’s impressive automatic
scaling and high-availability mechanisms ... rely on these constraints.
The marketplace eventually embraced Amazon’s low-level virtual machine approach to cloud com-
puting, so Google, Microsoft and other cloud companies offered similar interfaces. We believe the
main reason for the success of low-level virtual machines was that in the early days of cloud com-
puting users wanted to recreate the same computing environment in the cloud that they had on
their local computers to simplify porting their workloads to the cloud [3–6]. That practical need,
sensibly enough, took priority over writing new programs solely for the cloud, especially as it was
unclear how successful the cloud would be.
The downside of this choice was that developers had to manage virtual machines themselves,
basically either by becoming system administrators or by working with them to set up environments.
Table 1 lists the issues that must be managed to operate an environment in the cloud. The
long list of low-level virtual machine management responsibilities inspired customers with simpler
applications to ask for an easier path to the cloud for new applications. For example, suppose
1
Due to the tight coupling between computation and storage, databases need to reserve instances long term. However,
their workloads can be bursty, which results in low resource utilization.

3
 1. Redundancy for availability, so that a single machine failure doesn’t take down the service.
2. Geographic distribution of redundant copies to preserve the service in case of disaster.
3. Load balancing and request routing to efficiently utilize resources.
4. Autoscaling in response to changes in load to scale up or down the system.
5. Monitoring to make sure the service is still running well.
6. Logging to record messages needed for debugging or performance tuning.
7. System upgrades, including security patching.
8. Migration to new instances as they become available.

Table 1: Eight issues to be addressed in setting up an environment for cloud users. Some issues
take many steps. For example, autoscaling requires determining the need to scale; picking the type
and number of servers to use; requesting the servers; waiting for them to come online; configuring
them with the application; confirming that no errors occurred; instrumenting them with monitoring
tools; and sending traffic at them to test them.

the application wanted to send images from a phone application to the cloud, which should create
thumbnail images and then place them on the web. The code to accomplish these tasks might be
dozens of lines of JavaScript, which would be a trivial amount of development compared to what
it takes to set up the servers with the proper environment to run the code.
Recognition of these needs led to a new option from Amazon in 2015 called the AWS Lambda
service. Lambda offered cloud functions, and drew widespread attention to serverless comput-
ing. Although serverless computing is arguably an oxymoron—you are still using servers to com-
pute—the name presumably stuck because it suggests that the cloud user simply writes the code
and leaves all the server provisioning and administration tasks to the cloud provider. While cloud
functions—packaged as FaaS (Function as a Service) offerings2 —represent the core of serverless
computing, cloud platforms also provide specialized serverless frameworks that cater to specific
application requirements as BaaS (Backend as a Service) offerings [7]. Put simply, serverless com-
puting = FaaS + BaaS.3 In our definition, for a service to be considered serverless, it must scale
automatically with no need for explicit provisioning, and be billed based on usage. In the rest of
this paper, we focus on the emergence, evolution, and future of cloud functions. Cloud functions
are the general purpose element in serverless computing today, and lead the way to a simplified
and general purpose programming model for the cloud.
We next motivate and define serverless computing. Like the original Berkeley View paper on
cloud computing, we then list challenges and research opportunities to be addressed for serverless
computing to fulfill its promise. While we are unsure which solutions will win, we believe all issues
will all be addressed eventually, thereby enabling serverless computing to become the face of cloud
computing.
2
Different cloud platforms have different names for their FaaS offerings—AWS Lambda for Amazon Web Services
(AWS), Google Cloud Functions for Google Cloud Platform, IBM Cloud Functions for IBM Cloud, and Azure
Functions for Microsoft Azure. They all have similar features, and we refer to them as cloud functions or FaaS
offerings interchangeably in this paper.
3
BaaS originated as a term describing mobile-centric cloud frameworks and has grown to encompass any application-
specific serverless cloud service, such as serverless databases and serverless big data processing frameworks.

4
2 Emergence of Serverless Computing
In any serverless platform, the user just writes a cloud function in a high-level language, picks the
event that should trigger the running of the function—such as loading an image into cloud storage
or adding an image thumbnail to a database table—and lets the serverless system handle everything
else: instance selection, scaling, deployment, fault tolerance, monitoring, logging, security patches,
and so on. Table 2 summarizes the differences between serverless and the traditional approach,
which we’ll call serverful cloud computing in this paper. Note that these two approaches repre-
sent the endpoints of a continuum of function-based/server-centered computing platforms, with
containerized orchestration frameworks like Kubernetes representing intermediates.

Characteristic AWS Serverless Cloud AWS Serverful Cloud

When the program is run On event selected by Cloud user Continuously until explicitly stopped
Programming Language JavaScript, Python, Java, Go, C#, etc.4 Any
PROGRAMMER

Program State Kept in storage (stateless) Anywhere (stateful or stateless)

Maximum Memory Size 0.125 - 3 GiB (Cloud user selects) 0.5 - 1952 GiB (Cloud user selects)
Maximum Local Storage 0.5 GiB 0 - 3600 GiB (Cloud user selects)
Maximum Run Time 900 seconds None
Minimum Accounting Unit 0.1 seconds 60 seconds
Price per Accounting Unit $0.0000002 (assuming 0.125 GiB) $0.0000867 - $0.4080000
Operating System & Libraries Cloud provider selects5 Cloud user selects
Server Instance Cloud provider selects Cloud user selects
Scaling6
SYSADMIN

Cloud provider responsible Cloud user responsible

Deployment Cloud provider responsible Cloud user responsible
Fault Tolerance Cloud provider responsible Cloud user responsible
Monitoring Cloud provider responsible Cloud user responsible
Logging Cloud provider responsible Cloud user responsible

Table 2: Characteristics of serverless cloud functions vs. serverful cloud VMs divided into program-
ming and system administration categories. Specifications and prices correspond to AWS Lambda
and to on-demand AWS EC2 instances.

Figure 1 illustrates how serverless simplifies application development by making cloud resources
easier to use. In the cloud context, serverful computing is like programming in low-level assembly
language whereas serverless computing is like programming in a higher-level language such as
Python. An assembly language programmer computing a simple expression such as c = a + b
must select one or more registers to use, load the values into those registers, perform the arithmetic,
and then store the result. This mirrors several of the steps of serverful cloud programming, where
one first provisions resources or identifies available ones, then loads those resources with necessary
code and data, performs the computation, returns or stores the results, and eventually manages
resource release. The aim and opportunity in serverless computing is to give cloud programmers
benefits similar to those in the transition to high-level programming languages.7 Other features
of high-level programming environments have natural parallels in serverless computing as well.
Automated memory management relieves programmers from managing memory resources, whereas
serverless computing relieves programmers from managing server resources.
Put precisely, there are three critical distinctions between serverless and serverful computing:
7
Although several serverless computing providers run binary programs in addition to high-level language programs,
we believe the greatest upside potential for serverless is using high-level languages.

5
 1. Decoupled computation and storage. The storage and computation scale separately and are
provisioned and priced independently. In general, the storage is provided by a separate cloud
service and the computation is stateless.
2. Executing code without managing resource allocation. Instead of requesting resources, the
user provides a piece of code and the cloud automatically provisions resources to execute that
code.
3. Paying in proportion to resources used instead of for resources allocated. Billing is by some
dimension associated with the execution, such as execution time, rather than by a dimension
of the base cloud platform, such as size and number of VMs allocated.

Using these distinctions, we next explain how serverless differs from similar offerings, both past
and current.

Applications Event Data

Web APIs Future Serverless Applications
Processing

Cloud Object Key-Value Mobile Backend

Functions Storage Database Database
Serverless
Degree of Abstraction

Big Data Big Data Future Serverless

Messaging
Query Transform Cloud Services

Base Cloud Block IAM

VM VPC Billing Monitoring
Platform Storage

Hardware Server Network Storage Accelerator

Figure 1: Architecture of the serverless cloud. The serverless layer sits between applications and the
base cloud platform, simplifying cloud programming. Cloud functions (i.e., FaaS) provide general
compute and are complemented by an ecosystem of specialized Backend as a Service (BaaS) offerings
such as object storage, databases, or messaging. Specifically, a serverless application on AWS might
use Lambda with S3 (object storage) and DynamoDB (key-value database), while an application
on Google’s cloud might use Cloud Functions with Cloud Firestore (mobile backend database)
and Cloud Pub/Sub (messaging). Serverless also comprises certain big data services such as AWS
Athena and Google BigQuery (big data query), and Google Cloud Dataflow and AWS Glue (big
data transform). The base underlying base cloud platform includes virtual machines (VM), private
networks (VPC), virtualized block storage, Identity and Access Management (IAM), as well as
billing and monitoring.

2.1 Contextualizing Serverless Computing

What technical breakthroughs were needed to make serverless computing possible? Some have
argued that serverless computing is merely a rebranding of preceding offerings, perhaps a modest

6
generalization of Platform as a Service (PaaS) cloud products such as Heroku [8], Firebase [9],
or Parse [10]. Others might point out that the shared web hosting environments popular in the
1990s provided much of what serverless computing has to offer. For example, these had a stateless
programming model allowing high levels of multi-tenancy, elastic response to variable demand, and
a standardized function invocation API, the Common Gateway Interface (CGI) [11], which even
allowed direct deployment of source code written in high-level languages such as Perl or PHP.
Google’s original App Engine, largely rebuffed by the market just a few years before serverless
computing gained in popularity, also allowed developers to deploy code while leaving most aspects of
operations to the cloud provider. We believe serverless computing represents significant innovation
over PaaS and other previous models.
Today’s serverless computing with cloud functions differs from its predecessors in several essen-
tial ways: better autoscaling, strong isolation, platform flexibility, and service ecosystem support.
Among these factors, the autoscaling offered by AWS Lambda marked a striking departure from
what came before. It tracked load with much greater fidelity than serverful autoscaling techniques,
responding quickly to scale up when needed and scaling all the way down to zero resources, and zero
cost, in the absence of demand. It charged in a much more fine-grained way, providing a minimum
billing increment of 100 ms at a time when other autoscaling services charged by the hour.8 In a
critical departure, it charged the customer for the time their code was actually executing, not for
the resources reserved to execute their program. This distinction ensured the cloud provider had
“skin in the game” on autoscaling, and consequently provided incentives to ensure efficient resource
allocation.
Serverless computing relies on strong performance and security isolation to make multi-tenant
hardware sharing possible. VM-like isolation is the current standard for multi-tenant hardware
sharing for cloud functions [12], but because VM provisioning can take many seconds serverless
computing providers use elaborate techniques to speed up the creation of function execution envi-
ronments. One approach, reflected in AWS Lambda, is maintaining a “warm pool” of VM instances
that need only be assigned to a tenant, and an “active pool” of instances that have been used to
run a function before and are maintained to serve future invocations [13]. The resource lifecycle
management and multi-tenant bin packing necessary to achieve high utilization are key technical
enablers of serverless computing. We note that several recent proposals aim to reduce the overhead
of providing multi-tenant isolation by leveraging containers, unikernels, library OSes, or language
VMs. For example, Google has announced that gVisor [14] has already been adopted by App
Engine, Cloud Functions, and Cloud ML Engine, Amazon released Firecracker VMs [15] for AWS
Lambda and AWS Fargate, and the CloudFlare Workers serverless platform provides multi-tenant
isolation between JavaScript cloud functions using web browser sandboxing technology [16].
Several other distinctions have helped serverless computing succeed. By allowing users to bring
their own libraries, serverless computing can support a much broader range of applications than
PaaS services which are tied closely to particular use cases. Serverless computing runs in modern
data centers and operates at much greater scale than the old shared web hosting environments.
As mentioned in Section 1, cloud functions (i.e., FaaS) popularized the serverless paradigm.
However, it is worth acknowledging that they owe their success in part to BaaS offerings that have
existed since the beginning of public clouds, services like AWS S3. In our view, these services
are domain-specific, highly optimized implementations of serverless computing. Cloud functions
represent serverless computing in a more general form. We summarize this view in Table 3 by
comparing programming interfaces and cost models for several services.
A common question when discussing serverless computing is how it relates to Kubernetes [17],
8
Compare for example, AWS Elastic Beanstalk or Google App Engine.

7
 Service Programming Interface Cost Model
Cloud Functions Arbitrary code Function execution time
BigQuery/Athena SQL-like query The amount of data scanned by the query
DynamoDB puts() and gets() Per put() or get() request + storage
SQS enqueue/dequeue events per-API call

Table 3: Examples of serverless computing services and their corresponding programming interfaces
and cost models. Note that for the serverless compute offerings described here: BigQuery, Athena,
and cloud functions, the user pays separately for storage (e.g., in Google Cloud Storage, AWS S3,
or Azure Blob Storage).

a “container orchestration” technology for deploying microservices. Unlike serverless computing,

Kubernetes is a technology that simplifies management of serverful computing. Derived from years
of development for Google’s internal use [18], it is gaining rapid adoption. Kubernetes can provide
short-lived computing environments, like serverless computing, and has far fewer limitations, e.g.,
on hardware resources, execution time, and network communication. It can also deploy software
originally developed for on-premise use completely on the public cloud with little modification.
Serverless computing, on the other hand, introduces a paradigm shift that allows fully offloading
operational responsibilities to the provider, and makes possible fine-grained multi-tenant multiplex-
ing. Hosted Kubernetes offerings, such as the Google Kubernetes Engine (GKE) and AWS Elastic
Kubernetes Service (EKS) offer a middle ground in this continuum: they offload operational man-
agement of Kubernetes while giving developers the flexibility to configure arbitrary containers. One
key difference between hosted Kubernetes services and serverless computing is the billing model.
The former charges per reserved resources, whereas the latter per function execution duration.
Kubernetes is also a perfect match to hybrid applications where a portion runs on-premise on
local hardware and a portion runs in the cloud. Our view is that such hybrid applications make
sense in the transition to the cloud. In the long term, however, we believe the economies of cloud
scale, faster network bandwidth, increasing cloud services, and simplification of cloud management
via serverless computing will diminish the importance of such hybrid applications.
Edge computing is the partner of cloud computing in the PostPC Era, and while we focus here
on how serverless computing will transform programming within the data center, there is interesting
potential for impact at the edge as well. Several Content Delivery Network (CDN) operators offers
the ability to execute a serverless functions in facilities close to users [19, 20], wherever they might
be, and AWS IoT Greengrass [21] can even embed serverless execution in edge devices.
Now that we’ve defined and contextualized serverless computing, let’s see why it is attractive
to cloud providers, cloud users, and researchers.

2.2 Attractiveness of Serverless Computing

For cloud providers serverless computing promotes business growth, as making the cloud easier
to program helps draw in new customers and helps existing customers make more use of cloud
offerings. For example, recent surveys found that about 24% of serverless users were new to cloud
computing [22] and 30% of existing serverful cloud customers also used serverless computing [23].
In addition, the short run time, small memory footprint, and stateless nature improve statistical
multiplexing by making it easier for cloud providers to find unused resources on which to run these
tasks. The cloud providers can also utilize less popular computers—as the instance type is up to

8
 Percent Use Case
32% Web and API serving
21% Data Processing, e.g., batch ETL (database Extract, Transform, and Load)
17% Integrating 3rd Party Services
16% Internal tooling
8% Chat bots e.g., Alexa Skills (SDK for Alexa AI Assistant)
6% Internet of Things

Table 4: Popularity of serverless computing use cases according to a 2018 survey [22].

the cloud providers—such as older servers that may be less attractive to serverful cloud customers.
Both benefits increase income from existing resources.
Customers benefit from increased programming productivity, and in many scenarios can enjoy
cost savings as well, a consequence of the higher utilization of underlying servers. Even if serverless
computing lets customers be more efficient, the Jevons paradox [24] suggests that they will increase
their use of the cloud rather than cut back as the greater efficiency will increase the demand by
adding users.
Serverless also raises the cloud deployment level from x86 machine code—99% of cloud comput-
ers use the x86 instruction set—to high-level programming languages,9 which enables architectural
innovations. If ARM or RISC-V offer better cost-performance than x86, serverless computing makes
it easier to change instruction sets. Cloud providers could even embrace research in language ori-
ented optimizations and domain specific architectures specifically aimed at accelerating programs
written in languages like Python [25] (see Section 4).
Cloud users like serverless computing because novices can deploy functions without any under-
standing of the cloud infrastructure and because experts save development time and stay focused
on problems unique to their application. Serverless users may save money since the functions are
executed only when events occur, and fine-grained accounting (today typically 100 milliseconds)
means they pay only for what they use versus for what they reserve. Table 4 shows the most
popular uses of serverless computing today.
Researchers have been attracted to serverless computing, and especially to cloud functions,
because it is a new general purpose compute abstraction that promises to become the future of
cloud computing, and because there are many opportunities for boosting the current performance
and overcoming its current limitations.

3 Limitations of Today’s Serverless Computing Platforms

Serverless cloud functions have been successfully employed for several classes of workloads10 includ-
ing API serving, event stream processing, and limited ETL11 (see Table 3). To see what obstacles
prevent supporting more general workloads, we attempted to create serverless versions of applica-
tions that were of interest to us and studied examples published by others. These are not intended
9
Although several serverless computing providers run binary programs in addition to high-level language programs,
we believe the greatest upside potential for serverless is using high-level languages.
10
See “Use Cases” here: https://aws.amazon.com/lambda/.
11
The ETL implemented with today’s cloud functions is typically restricted to Map-only processing.

9
to be representative of the rest of information technology outside of the current serverless com-
puting ecosystem; they are simply examples selected to uncover common weaknesses that might
prevent serverless versions of many other interesting applications.
In this section, we present an overview of five research projects and discuss the obstacles that
prevent existing serverless computing platforms from achieving state-of-the-art performance, i.e.,
matching the performance of serverful clouds for the same workloads. We are focused in particular
on approaches that utilize general purpose cloud functions for compute, rather than relying heavily
on other application-specific serverless offerings (BaaS). However in our final example, Serverless
SQLite, we identify a use case that maps so poorly to FaaS that we conclude that databases and
other state-heavy applications will remain as BaaS. An appendix at the end of this paper goes into
more detail of each application.
Interestingly, even this eclectic mix of applications exposed similar weaknesses, which we list
after describing the applications. Table 5 summarizes the five applications.
ExCamera: Video encoding in real-time. ExCamera [26] aims to provide a real-time
encoding service to users uploading their videos to sites, such as YouTube. Depending on the size
of the video, today’s encoding solutions can take tens of minutes, even hours. To perform encoding
in real time, ExCamera parallelizes the “slow” parts of the encoding, and performs the “fast” parts
serially. ExCamera exposes the internal state of the video encoder and decoder, allowing encoding
and decoding tasks to be executed using purely functional semantics. In particular, each task takes
the internal state along with video frames as input, and emits the modified internal state as output.
MapReduce. Analytics frameworks such as MapReduce, Hadoop, and Spark, have been tra-
ditionally deployed on managed clusters. While some of these analytics workloads are now moving
to serverless computing, these workloads mostly consist of Map-only jobs. The natural next step is
supporting full fledged MapReduce jobs. One of the driving forces behind this effort is leveraging
the flexibility of serverless computing to efficiently support jobs whose resource requirements vary
significantly during their execution.
Numpywren: Linear algebra. Large scale linear algebra computations are traditionally
deployed on supercomputers or high-performance computing clusters connected by high-speed,
low-latency networks. Given this history, serverless computing initially seems a poor fit. Yet there
are two reasons why serverless computing might still make sense for linear algebra computations.
First, managing clusters is a big barrier for many non-CS scientists [27]. Second, the amount of
parallelism can vary dramatically during a computation. Provisioning a cluster with a fixed size
will either slow down the job or leave the cluster underutilized.
Cirrus: Machine learning training. Machine Learning (ML) researchers have traditionally
used clusters of VMs for different tasks in ML workflows such as preprocessing, model training, and
hyperparameter tuning. One challenge with this approach is that different stages of this pipeline
can require significantly different amounts of resources. As with linear algebra algorithms, a fixed
cluster size will either lead to severe underutilization or severe slowdown. Serverless computing can
address this challenge by enabling every stage to scale to meet its resource demands. Further, it
frees developers from managing clusters.
Serverless SQLite: Databases. Various autoscaling database services already exist [28–33],
but to better understand the limitations of serverless computing it is important to understand
what makes database workloads particularly challenging to implement. In this context, we con-
sider whether a third party could implement a serverless database directly using cloud functions, the
general purpose serverless computing building block. A strawman solution would be to run common
transactional databases, such as PostgreSQL, Oracle, or MySQL inside cloud functions. However,
that immediately runs into a number of challenges. First, serverless computing has no built-in
persistent storage, so we need to leverage some remote persistent store, which introduces large la-

10
 Application Description Challenges Workarounds Cost-performance
Real-time On-the-fly Object store too Function-to- 60x faster, 6x
video video slow to support function cheaper versus
compression encoding fine-grained communication VM instances.
(ExCamera) communication; to avoid object
functions too store; a function
coarse grained for executes more
tasks. than one task.
MapReduce Big data Shuffle doesn’t Small storage Sorted 100 TB
processing scale due to object with low-latency, 1% faster than
(Sort stores latency and high IOPS to VM instances,
100TB) IOPS limits speed-up shuffle. costs 15% more.
Linear Large scale Need large Storage with Up to 3x slower
algebra linear problem size to low-latency completion time.
(Numpy- algebra overcome storage high-throughput 1.26x to 2.5x
wren) (S3) latency, hard to handle smaller lower in CPU
to implement problem sizes. resource
efficient broadcast. consumption.
ML ML training Lack of fast Storage with 3x-5x faster than
pipelines at scale storage to low-latency, high VM instances, up
(Cirrus) implement IOPS to to 7x higher total
parameter server; implement cost.
hard to implement parameter server.
efficient broadcast,
aggregation.
Databases Primary Lack of shared Shared file 3x higher cost per
(Serverless state for memory, object system can work transaction than
SQLite) applications store has high if write needs are published TPC-C
(OLTP) latency, lack of low. benchmarks.
support for Reads scale to
inbound match but writes
connectivity. do not.

Table 5: Summary of requirements for new application areas for serverless computing.

tency. Second, these databases assume connection-oriented protocols, e.g., databases are running as
servers accepting connections from clients. This assumption conflicts with existing cloud functions
that are running behind network address translators, and thus don’t support incoming connec-
tions. Finally, while many high performance databases rely on shared memory [34], cloud functions
run in isolation so cannot share memory. While shared-nothing distributed databases [35–37] do
not require shared memory, they expect nodes to remain online and be directly addressable. All
these issues pose significant challenges to running traditional database software atop of serverless
computing, or to implementing equivalent functionality, so we expect databases to remain BaaS.

11
 One of the key reasons these applications hope to use serverless computing is fine-grained
autoscaling, so that resource utilization closely matches each application’s the varying demand.
Table 5 summarizes the characteristics, challenges, and workarounds for these five applications,
which we next use to identify four limits in the current state of serverless computing.

3.1 Inadequate storage for fine-grained operations

The stateless nature of serverless platforms makes it difficult to support applications that have
fine-grained state sharing needs. This is mostly due to the limitations of existing storage services
offered by cloud providers. Table 6 summarizes the properties of the existing cloud storage services.
Object storage services such as AWS S3, Azure Blob Storage, and Google Cloud Storage are
highly scalable and provide inexpensive long-term object storage, but exhibit high access costs and
high access latencies. According to recent tests, all these services take at least 10 milliseconds
to read or write small objects [38]. With respect to IOPS, after the recent limit increase [39], S3
provides high throughput, but it comes with a high cost. Sustaining 100K IOPS costs $30/min [40],
3 to 4 orders of magnitude more than running an AWS ElastiCache instance [41]. Such an Elasti-
Cache instance provides better performance along several axes, with sub-millisecond read and write
latencies, and over 100K IOPS for one instance configured to run the single-threaded Redis server.
Key-value databases, such as AWS DynamoDB, Google Cloud Datastore, or Azure Cosmos
DB provide high IOPS, but are expensive and can take a long time to scale up.12 Finally, while
cloud providers offer in-memory storage instances based on popular open source projects such as
Memcached or Redis, they are not fault tolerant and do not autoscale as do serverless computing
platforms.
As can be seen in Table 5, applications built on serverless infrastructure require storage services
with transparent provisioning, the storage equivalent of compute autoscaling. Different applications
will likely motivate different guarantees of persistence and availability, and perhaps also latency or
other performance measures. We believe this calls for the development of ephemeral and durable
serverless storage options, which we discuss further in Section 4.

3.2 Lack of fine-grained coordination

To expand support to stateful applications, serverless frameworks need to provide a way for tasks
to coordinate. For instance, if task A uses task B’s output there must be a way for A to know
when its input is available, even if A and B reside on different nodes. Many protocols aiming to
ensure data consistency also require similar coordination.
None of the existing cloud storage services come with notification capabilities. While cloud
providers do offer stand-alone notification services, such as SNS [42] and SQS [43], these services
add significant latency, sometimes hundreds of milliseconds. Also, they can be costly when used
for fine grained coordination. There have been some proposed research systems such as Pocket [44]
that do not have many of these drawbacks, but they have not yet been adopted by cloud providers.
As such, applications are left with no choice but to either (1) manage a VM-based system that
provides notifications, as in ElastiCache and SAND [45], or (2) implement their own notification
mechanism, such as in ExCamera [26], that enables cloud functions to communicate with each other
via a long-running VM-based rendezvous server. This limitation also suggests that new variants of
serverless computing may be worth exploring, for example naming function instances and allowing
direct addressability for access to their internal state (e.g., Actors as a Service [46]).
12
Official best practices for scaling Google Cloud Datastore include the “500/50/5” rule: start with 500 operations
per second, then increase by 50% every 5 minutes. https://cloud.google.com/datastore/docs/best-practices.

12
 Elastic
Object Memory
Database
Block Storage Store (e.g., “Ideal”
File System (e.g.,
Storage (e.g., AWS AWS Elas- storage
(e.g., AWS Google
(e.g., AWS S3, Azure tiCache, service for
EFS, Cloud
EBS, IBM Blob Store, Google serverless
Google Datastore,
Block Google Cloud computing
Filestore) Azure
Storage) Cloud Memorys-
Cosmos
Storage) tore)
DB)

Cloud functions access No Yes Yes13 Yes Yes Yes

Transparent Capacity
No Yes Yes15 No Yes
Provisioning only14

Availability and Distributed Distributed Distributed

Local & Local &
persistence guarantees & & & Various
Persistent Ephemeral
Persistent Persistent Persistent
Latency (mean) < 1ms 10 − 20ms 4 − 10ms 8 − 15ms < 1ms < 1ms
Storage capacity
$0.10 $0.023 $0.30 $0.18–$0.25 $1.87 ∼$0.10
(1 GB/month)
Cost16

Throughput (1 $3.15-
$0.03 $0.0071 $6.00 $0.96 ∼$0.03
MB/s for 1 month) $255.1

IOPS
$0.03 $7.1 $0.23 $1-$3.15 $0.037 ∼$0.03
(1/s for 1 month)

Table 6: Characteristics of storage services by cloud providers to the serverless ideal. Costs are
monthly values for storing 1 GB (capacity), transferring 1 MB/s (throughput), and issuing 1 IOPS
(or 2.4 million requests in 30 days). All values reflect a 50/50 read/write balance and a minimum
transfer size of 4 KB. The color codings of entries are green for good, orange for medium, and red
for poor. Persistence and availability guarantees describe how well the system tolerates failures:
local provides reliable storage at one site, distributed ensures the ability to survive site failures,
and ephemeral describes data that resides in memory and is subject to loss, e.g., in the event of
software crashes. The serverless ideal would provide cost and performance comparable to block
storage, while adding transparent provisioning and access for cloud functions.

3.3 Poor performance for standard communication patterns

Broadcast, aggregation, and shuffle are some of the most common communication primitives in
distributed systems. These operations are commonly employed by applications such as machine
learning training and big data analytics. Figure 2 shows the communication patterns for these
primitives for both VM-based and function-based solutions.
13
A shared file system is integrated on Azure Functions, but not on other cloud functions platforms.
14
File system capacity scales automatically on Azure, AWS, IBM, but not on Google. IOPS do not scale independently
of storage space for any provider’s file system.
15
Google Cloud Datastore and DynamoDB provisioned capacity autoscaling can be slow to respond to load spikes.
16
For services that do not charge per operation, such as ElastiCache, we linearly scale down the pricing. For example,
to calculate the cost of 1 IOPS, we take the cost of an instance that can sustain 30K IOPS with 4KB blocks and
divide by 30K.

13
Figure 2: Three common communication patterns for distributed applications: broadcast, aggre-
gation, and shuffle. (a) Shows these communication patterns for VM instances where each instance
runs two functions/tasks. (b) Shows the same communication patterns for cloud function instances.
Note the significantly lower number of remote messages for the VM-based solutions. This is because
VM instances offer ample opportunities to share, aggregate, or combine data locally across tasks
before sending it or after receiving it.

With VM-based solutions, all tasks running on the same instance can share the copy of the data
being broadcast, or perform local aggregation before sending the partial result to other instances.
As such, the communication complexity of the broadcast and aggregation operations is O(N ), where
N is the number of VM-instances in the system. However, with cloud functions this complexity is
O(N × K), where K is the number of functions per VM. The difference is even more dramatic for
the shuffle operation. With VM-based solutions all local tasks can combine their data such that
there is only one message between two VM instances. Assuming the same number of senders and
receivers yields N 2 messages. For comparison, with cloud functions we need to send (N × K)2
messages. As existing functions have a much smaller number of cores than a VM, K typically
ranges from 10 to 100. Since the application cannot control the location of the cloud functions, a
serverless computing application may need to send two and four orders of magnitude more data
than an equivalent VM-based solution.

3.4 Predictable Performance

Although cloud functions have a much lower startup latency than traditional VM-based instances,
the delays incurred when starting new instances can be high for some applications. There are three
factors impacting this cold start latency: (1) the time it takes to start a cloud function; (2) the
time it takes to initialize the software environment of the function, e.g., load Python libraries; and
(3) application-specific initialization in user code. The latter two can dwarf the former. While it
can take less than one second to start a cloud function, it might take tens of seconds to load all

14
application libraries.17
Another obstacle to predictable performance is the variability in the hardware resources that
results from giving the cloud provider flexibility to choose the underlying server. In our experi-
ments [47], we sometimes received CPUs from different hardware generations. This uncertainty
exposes a fundamental tradeoff between the cloud provider desire to maximize the use of their
resources and predictability.

4 What Serverless Computing Should Become

Now that we’ve explained today’s serverless computing and its limitations, let’s look to the future
to understand how to bring its advantages to more applications. Researchers have already begun to
address issues raised above and to explore how to improve serverless platforms and the performance
of workloads that run on them [48, 49]. Additional work done by our Berkeley colleagues and some
of us emphasizes data-centric, distributed systems, machine learning, and programming model chal-
lenges and opportunities for serverless computing [50]. Here we take a broad view on increasing the
types of applications and hardware that work well with serverless computing, identifying research
challenges in five areas: abstractions, systems, networking, security, and architecture.

4.1 Abstraction challenges

Resource requirements: With today’s serverless offerings the developer specifies the cloud func-
tion’s memory size and execution time limit, but not other resource needs. This abstraction hinders
those who want more control on specifying resources, such as the number of CPUs, GPUs, or other
types of accelerators. One approach would be to enable developers to specify these resource require-
ments explicitly. However, this would make it harder for cloud providers to achieve high utilization
through statistical multiplexing, as it puts more constraints on function scheduling. It also goes
against the spirit of serverless by increasing the management overhead for cloud application devel-
opers.
A better alternative would be to raise the level of abstraction, having the cloud provider infer
resource requirements instead of having the developer specify them. To do so, the cloud provider
could use a variety of approaches from static code analysis, to profiling previous runs, to dynamic
(re)compilation to retarget the code to other architectures. Provisioning just the right amount
of memory automatically is particularly appealing but especially challenging when the solution
must interact with the automated garbage collection used by high-level language runtimes. Some
research suggests that these language runtimes could be integrated with serverless platforms [51].
Data dependencies: Today’s cloud function platforms have no knowledge of the data depen-
dencies between the cloud functions, let alone the amount of data these functions might exchange.
This ignorance can lead to suboptimal placement that could result in inefficient communication
patterns, as illustrated in the MapReduce and numpywren examples (see Section 3 and Appendix).
One approach to address this challenge would be for the cloud provider to expose an API
that allows an application to specify its computation graph, enabling better placement decisions
that minimize communication and improve performance. We note that many general purpose
distributed frameworks (e.g., MapReduce, Apache Spark and Apache Beam/Cloud Dataflow [52]),
parallel SQL engines (e.g., BigQuery, Azure Cosmos DB), as well as orchestration frameworks
(e.g., Apache Airflow [53]) already produce such computation graphs internally. In principle, these
systems could be modified to run on cloud functions and expose their computation graphs to the
17
Consider a cloud function that needs to load a few hundred MB worth of Python libraries from object storage.

15
cloud provider. Note that AWS Step Functions represents progress in this direction by providing a
state machine language and API.

4.2 System challenges

High-performance, affordable, transparently provisioned storage: As discussed in Sec-
tion 3 and Table 5, we see two distinct unaddressed storage needs: Serverless Ephemeral Storage
and Serverless Durable Storage.
Ephemeral Storage. The first four applications from Section 3 were limited by the speed and
latency of the storage system used to transfer state between cloud functions. While their capacity
demands vary, all need such storage to maintain application state during the application lifetime.
Once the application finishes, the state can be discarded. Such ephemeral storage might also be
configured as a cache in other applications.
One approach to providing ephemeral storage for serverless applications would be to build
a distributed in-memory service with an optimized network stack that ensures microsecond-level
latency. This system would enable the functions of an application to efficiently store and exchange
state during the application’s lifetime. Such an in-memory service would need to automatically
scale the storage capacity and the IOPS with the application’s demands. A unique aspect of such
a service is that it not only needs to allocate memory transparently, but also free it transparently.
In particular, when the application terminates or fails, the storage allocated to that application
should be automatically released. This management is akin to the OS automatically freeing the
resources allocated by a process when the process finishes (or crashes). Furthermore, such storage
must provide access protection and performance isolation across applications.
RAMCloud [54] and FaRM [55] show that it is possible to build in-memory storage systems that
can provide microsecond level latencies and support hundred of thousands IOPS per instance. They
achieve this performance by carefully optimizing the entire software stack and by leveraging RDMA
to minimize latency. However, they require applications to provision storage explicitly. They also
do not provide strong isolation between multiple tenants. Another recent effort, Pocket [44], aims
to provide the abstraction of ephemeral storage, but also lacks autoscaling, requiring applications
to allocate storage a priori.
By leveraging statistical multiplexing, this ephemeral storage can be more memory-efficient than
today’s serverful computing. With serverful computing, if an application needs less memory than
the aggregated memory of the allocated VM instances, that memory goes to waste. In contrast, with
a shared in-memory service, any memory not used by one serverless application can be allocated
to another. In fact, statistical multiplexing can benefit even a single application: with serverful
computing, the unused memory of a VM cannot be used by the program running on another VM
belonging to the same application, while in the case of a shared in-memory service it can. Of
course, even with serverless computing there can be internal fragmentation if the cloud function
doesn’t use its entire local memory. In some cases, storing the application state of cloud functions
in a shared in-memory service could alleviate the consequences of internal memory fragmentation.
Durable Storage. Like the other applications, our serverless database application experiment was
limited by the latency and IOPS of the storage system, but it also required long term data storage
and the mutable-state semantics of a file system. While it’s likely that database functionality,
including OLTP, will increasingly be provided as a BaaS offering,18 we see this application as
representative of several applications that require longer retention and greater durability than
18
One recent example is Amazon Aurora Serverless (https://aws.amazon.com/rds/aurora/serverless/). Services
such as Google’s BigQuery and AWS Athena are basically serverless query engines rather than fully fledged
databases.

16
the Serverless Ephemeral Storage is suited to provide. To implement high-performance Serverless
Durable Storage, one approach would be to leverage an SSD-based distributed store paired with a
distributed in-memory cache. A recent system realizing many of these goals is the Anna key-value
database that achieves both cost efficiency and high performance by combining multiple existing
cloud storage offerings [56]. A key challenge with this design is achieving low tail latency in the
presence of heavy tail access distributions, given the fact that in-memory cache capacity is likely
to be much lower than SSD capacity. Leveraging new storage technologies [57], which promise
microsecond-level access times, is emerging as a promising approach to address this challenge.
Similar to Serverless Ephemeral Storage, this service should be transparently provisioned and
should ensure isolation across applications and tenants for security and predictable performance.
However, whereas Serverless Ephemeral Storage would reclaim resources when an application ter-
minates, Serverless Durable Storage must only free resources explicitly (e.g., as a result of a “delete”
or “remove” command), just like in traditional storage systems. Additionally, it must of course
ensure durability, so that any acknowledged writes will survive failures.
Coordination/signaling service: Sharing state between functions often uses a producer-
consumer design pattern, which requires consumers to know as soon as the data is available from
producers. Similarly, one function might want to signal another when a condition becomes available,
or multiple functions might want to coordinate, e.g., to implement data consistency mechanisms.
Such signaling systems would benefit from microsecond-level latency, reliable delivery, and broad-
cast or group communication. We also note that since cloud function instances are not individually
addressable they cannot be used to implement textbook distributed systems algorithms such as
consensus or leader election [50].
Minimize startup time: There are three parts of startup time (1) scheduling and start-
ing resources to run the cloud function, (2) downloading the application software environment
(e.g., operating system, libraries) to run the function code, and (3) performing application-specific
startup tasks such as loading and initializing data structures and libraries. Resource scheduling
and initialization can incur significant delays and overheads from creating an isolated execution
environment, and from configuring customer’s VPC and IAM policies. Cloud providers [14, 15],
as well as others [58, 59] have recently focused on reducing the startup time by developing new
lightweight isolation mechanisms.
One approach to reduce (2) is leveraging unikernels [60]. Unikernels obviate the overhead
incurred by traditional operating systems in two ways. First, instead of dynamically detecting the
hardware, applying user configurations, and allocating data structures like traditional operating
systems, unikernels squash these costs by being preconfigured for the hardware they are running on
and statically allocating the data structures. Second, unikernels include only the drivers and system
libraries strictly required by the application, which leads to a much lower footprint than traditional
operating systems. It is worth noting that since unikernels are tailored to specific applications,
they cannot realize some of the efficiencies possible when running many instances of a standardized
kernel, for example sharing kernel code pages between different cloud functions on the same VM,
or reducing the start-up time by pre-caching. Another approach to reduce (2) is to dynamically
and incrementally load the libraries as they are invoked by the application, for example as enabled
by the shared file system used in Azure Functions.
Application-specific initialization (3) is the responsibility of the programmer, but cloud providers
can include a readiness signal in their API to avoid sending work to function instances before they
can start processing it [61]. More broadly, cloud providers can seek to perform startup tasks ahead
of time [59]. This is particularly powerful for customer-agnostic tasks such as booting a VM with
popular operating system and set of libraries, as a “warm pool” of such instances can be shared
between tenants [13].

17
4.3 Networking challenges
As explained in Section 3 and as illustrated in Figure 2, cloud functions can impose significant
overhead on popular communication primitives such as broadcast, aggregation, and shuffle. In
particular, assuming that we can pack K cloud functions on a VM instance, a cloud function
version would send K times more messages than an instance version, and K 2 more messages in the
case of shuffle.
There may be several ways to address this challenge:

• Provide cloud functions with a larger number of cores, similar to VM instances, so multiple
tasks can combine and share data among them before sending over the network or after
receiving it.
• Allow the developer to explicitly place the cloud functions on the same VM instance. Offer
distributed communication primitives that applications can use out-of-the-box so that cloud
providers can allocate cloud functions to the same VM instance.
• Let applications provide a computation graph, enabling the cloud provider to co-locate the
cloud functions to minimize communication overhead (see “Abstraction Challenges” above.)

Note that the first two proposals could reduce the flexibility of cloud providers to place cloud
functions, and consequently reduce data center utilization. Arguably, they also go against the
spirit of serverless computing, by forcing developers to think about system management.

4.4 Security challenges

Serverless computing reshuffles security responsibilities, shifting many of them from the cloud
user to the cloud provider without fundamentally changing them. However, serverless computing
must also grapple with the risks inherent in both application disaggregation multi-tenant resource
sharing.
Scheduling randomization and physical isolation: Physical co-residency is the center
of hardware-level side-channel or Rowhammer [62] attacks inside the cloud. As a first step in
these types of attacks, the adversarial tenant needs to confirm the cohabitation with the victim
on the same physical host, instead of randomly attacking strangers. The ephemerality of cloud
functions may limit the ability of the attacker to identify concurrently-running victims. A ran-
domized, adversary-aware scheduling algorithm [63] might lower the risk of co-locating the attacker
and the victim, making co-residency attacks more difficult. However, deliberately preventing phys-
ical co-residency may conflict with placement to optimize start-up time, resource utilization, or
communication.
Fine-grained security contexts: Cloud functions need fine-grained configuration, includ-
ing access to private keys, storage objects, and even local temporary resources. There will be
requirements for translating security policies from existing serverful applications, and for offering
highly-expressive security APIs for dynamic use in cloud functions. For example, a cloud function
may have to delegate security privileges to another cloud function or cloud service. A capability-
based access control mechanism using cryptographically protected security contexts could be a
natural fit for such a distributed security model. Recent work [64] suggests using information flow
control for cross-function access control in a multi-party setting. Other challenges of providing
distributed management of security primitives, such as non-equivocation [65] and revocation, are
exacerbated if short-lived keys and certificates are dynamically created for cloud functions.
At the system level, users demand more fine-grained security isolation for each function, at least
as an option. The challenge in providing function-level sandboxing is to maintain a short startup

18
time without caching the execution environments in a way that shares state between repeated func-
tion invocations. One possibility would be to locally snapshot the instances so that each function
can start from clean state. Alternatively, light-weight virtualization technologies are starting to be
adopted by serverless providers: library OSes, including gVisor [14], implement system APIs in a
user-space “shim layer,” while unikernels and microVMs, including AWS Firecracker [15], stream-
line the guest kernels and help minimize the host attack surface. These isolation techniques reduce
startup times to as little as tens of milliseconds, as compared to VM startup times measured in
seconds. Whether these solutions achieve parity to traditional VMs in terms of security remains to
be shown, and we expect the search for strong isolation mechanisms with low startup overheads to
be an active area of ongoing research and development. On the positive side, provider management
and short-lived instances in serverless computing can enable much faster patching of vulnerabilities.
For users who want protection against co-residency attacks, one solution would be demanding
physical isolation. Recent hardware attacks (e.g., Spectre [66] and Meltdown [67]) also make re-
serving a whole core or even a whole physical machine appealing for users. Cloud providers may
offer a premium option for customers to launch functions on physical hosts dedicated exclusively
to their use.
Oblivious serverless computing: Cloud functions can leak access patterns and timing in-
formation through communication. For serverful applications, data is usually retrieved in a batch,
and cached locally. In contrast, because cloud functions are ephemeral and widely distributed
across the cloud, the network transmission patterns can leak more sensitive information to a net-
work attacker in the cloud (e.g., an employee), even if the payload is encrypted end-to-end. The
tendency to decompose serverless applications into many small functions exacerbates this security
exposure. While the primary security concern is from external attackers, the network patterns can
be protected from employees by adopting oblivious algorithms. Unfortunately, these tend to have
high overhead [68].

4.5 Computer architecture challenges

Hardware Heterogeneity, Pricing, and Ease of Management: Alas, the x86 microprocessors
that dominate the cloud are barely improving in performance. In 2017, single program performance
improvement only 3% [69]. Assuming the trends continue, performance won’t double for 20 years.
Similarly, DRAM capacity per chip is approaching its limits; 16 Gbit DRAMs are for sale today,
but it appears infeasible to build a 32 Gbit DRAM chip. A silver lining of this slow rate of change
is letting providers replace older computers as they wear out with little disruption to the current
serverless marketplace.
Performance problems for general purpose microprocessors do not reduce the demand for faster
computation. There are two paths forward [70]. For functions written in high-level scripting
languages like JavaScript or Python, hardware-software co-design could lead to language-specific
custom processors that run one to three orders of magnitude faster. The other path forward is
Domain Specific Architectures. DSAs are tailored to a specific problem domain and offer significant
performance and efficiency gains for that domain, but perform poorly for applications outside that
domain. Graphical Processing Units (GPUs) have long been used to accelerate graphics, and we’re
starting to see DSAs for machine learning such as the Tensor Processing Units (TPUs). TPUs can
outperform CPUs by a factor of 30x. These examples are the first of many, as general purpose
processors enhanced with DSAs for separate domains will become the norm.
As mentioned above in Section 4.1, we see two paths for serverless computing to support the
upcoming hardware heterogeneity:

19
 1. Serverless could embrace multiple instance types, with a different price per accounting unit
depending on the hardware used.
2. The cloud provider could select language-based accelerators and DSAs automatically. This
automation might be done implicitly based on the software libraries or languages used in a
cloud function, say GPU hardware for CUDA code and TPU hardware for TensorFlow code.
Alternatively, the cloud provider could monitor the performance of the cloud functions and
migrate them to the most appropriate hardware the next time they are run.

Serverless computing is facing heterogeneity now in a small way for the SIMD instructions of
the x86. AMD and Intel rapidly evolve that portion of the x86 instruction set by increasing the
number of operations performed per clock cycle and by adding new instructions. For programs
that use SIMD instructions, running on a recent Intel Skylake microprocessor with 512-bit wide
SIMD instructions can be much faster than running on the older Intel Broadwell microprocessor
with 128-bit wide SIMD instructions. Today both microprocessors are supplied at the same price
in AWS Lambda, but there is currently no way for serverless computing users to indicate that they
want the faster SIMD hardware. It seems to us that compilers should suggest which hardware
would be the best match.
As accelerators become more popular in the cloud, serverless cloud providers will no longer be
able to ignore the dilemma of heterogeneity, especially since plausible remedies exist.

5 Fallacies and Pitfalls

This section uses the fallacy and pitfall style of Hennessy and Patterson [69].

Fallacy Since an AWS Lambda cloud function instance with equivalent memory capacity of an on-
demand AWS t3.nano instance (0.5 GiB) costs 7.5x as much per minute, serverless cloud computing
is more expensive than serverful cloud computing.

The beauty of serverless computing is all the system administration capability that is included in
the price, including redundancy for availability, monitoring, logging, and scaling. Cloud providers
report that customers see cost savings of 4x-10x when moving applications to serverless [71]. The
equivalent functionality is much more than a single t3.nano instance, which along with being a
single point of failure operates with credit system that limits it to at most 6 minutes of CPU use
per hour (5% of the two vCPUs), so it could deny service during a load spike that serverless would
easily handle. Serverless is accounted for at much finer boundaries, including for scaling up and
down, and so may be more efficient in the amount of computing used. Because there is no charge
when there are no events that invoke cloud functions, it’s possible that serverless could be much
less expensive.

Pitfall Serverless computing can have unpredictable costs.

For some users, a disadvantage of the pure pay-as-you-go model employed by serverless computing
is cost unpredictability, which is at odds with the way many organizations manage their budgets.
When approving the budget, which typically occurs annually, organizations want to know how much
serverless services will cost over the next year. This desire is a legitimate concern, one which cloud
providers might mitigate by offering bucket-based pricing, similar to the way phone companies offer
fixed rate plans for certain amounts of usage. We also believe that as organizations use serverless
more and more, they will be able to predict their serverless computing costs based on history,
similar to the way they do today for other utility services, such as electricity.

20
Fallacy Since serverless computing programming is in high-level languages like Python, it’s easy
to port applications between serverless computing providers.

Not only do function invocation semantics and packaging differ between cloud serverless computing
providers, but many serverless applications also rely upon an ecosystem of proprietary BaaS offer-
ings that lacks standardization. Object storage, key-value databases, authentication, logging, and
monitoring are prominent examples. To achieve portability, serverless users will have to propose
and embrace some kind of standard API, such POSIX tried to do for operating systems. The
Knative project from Google is a step in this direction, aiming to provide a unified set of primitives
for application developers to use across deployment environments [61].

Pitfall Vendor lock-in may be stronger with serverless computing than for serverful computing.

This pitfall is a consequence of the previous fallacy; if porting is hard, then vendor lock-in is likely.
Some frameworks promise to mitigate such lock-in with cross-cloud support [72].

Fallacy Cloud functions cannot handle very low latency applications needing predictable perfor-
mance.

The reason serverful instances handle such low-latency applications well is because they are always
on, so they can quickly reply to requests when they receive them. We note that if the start-up
latency of a cloud function is not good enough for a given application, one could use a similar
strategy: pre-warm cloud functions by exercising them regularly to ensure that there are enough
running at any given time to satisfy the incoming requests.

Pitfall Few so called “elastic” services match the real flexibility demands of serverless computing.

The word “elastic” is a popular term today, but it is being applied to services that do not scale
nearly as well as the best serverless computing services. We are interested in services which can
change their capacity rapidly, with minimal user intervention, and can potentially “scale to zero”
when not in use. For example, despite its name, AWS ElastiCache only allows you to instantiate an
integral number of Redis instances. Other “elastic” services require explicit capacity provisioning,
with some taking many minutes to respond to changes in demand, or scaling over only a limited
range. Users lose many of the benefits of serverless computing when they build applications that
combine highly-elastic cloud functions with databases, search indexes, or serverful application tiers
that have only limited elasticity. Without a quantitative and broadly accepted technical definition
or metric—something that could aid in comparing or composing systems—“elastic” will remain an
ambiguous descriptor.

6 Summary and Predictions

By providing a simplified programming environment, serverless computing makes the cloud much
easier to use, thereby attracting more people who can and will use it. Serverless computing com-
prises FaaS and BaaS offerings, and marks an important maturation of cloud programming. It
obviates the need for manual resource management and optimization that today’s serverful com-
puting imposes on application developers, a maturation akin to the move from assembly language
to high-level languages more than four decades ago.
We predict that serverless use will skyrocket. We also project that hybrid cloud on-premises
applications will dwindle over time, though some deployments might persist due to regulatory
constraints and data governance rules.

21
 While already a success, we identified a few challenges that if overcome will make serverless
popular for an even broader range of applications. The first step is Serverless Ephemeral Storage,
which must provide low latency and high IOPS at reasonable cost, but need not provide economical
long term storage. A second class of applications would benefit from Serverless Durable Storage,
which does demand long term storage. New non-volatile memory technologies may help with such
storage systems. Other applications would benefit from a low latency signaling service and support
for popular communication primitives.
Two challenges for the future of serverless computing are improved security and accommodat-
ing cost-performance advances that are likely to come from special purpose processors. In both
cases, serverless computing has features that may help in addressing these challenges. Physical
co-residency is a requirement for side-channel attacks, but it is much harder to confirm in serverless
computing, and steps could be easily taken to randomize cloud function placement. The pro-
gramming of cloud functions in high-level languages like JavaScript, Python, or TensorFlow [73]
raises the level of programming abstraction and makes it easier to innovate so that the underlying
hardware can deliver improved cost-performance.
The Berkeley View of Cloud Computing paper [2] projected that the challenges facing the cloud
in 2009 would be addressed and that it would flourish, which it has. The cloud business is growing
50% annually and is proving highly profitable for cloud providers.19
We conclude this paper with the following predictions about serverless computing in the next
decade:

• We expect new BaaS storage services to be created that expand the types of applications that
run well on serverless computing. Such storage will match the performance of local block
storage and come in ephemeral and durable variants. We will see much more heterogeneity of
computer hardware for serverless computing than the conventional x86 microprocessor that
powers it today.
• We expect serverless computing to become simpler to program securely than serverful comput-
ing, benefiting from the high level of programming abstraction and the fine-grained isolation
of cloud functions.
• We see no fundamental reason why the cost of serverless computing should be higher than
that of serverful computing, so we predict that billing models will evolve so that almost
any application, running at almost any scale, will cost no more and perhaps much less with
serverless computing.
• The future of serverful computing will be to facilitate BaaS. Applications that prove to be
difficult to write on top of serverless computing, such as OLTP databases or communication
primitives such as queues, will likely be offered as part of a richer set of services from all cloud
providers.
• While serverful cloud computing won’t disappear, the relative importance of that portion of
the cloud will decline as serverless computing overcomes its current limitations.
• Serverless computing will become the default computing paradigm of the Cloud Era, largely
replacing serverful computing and thereby bringing closure to the Client-Server Era.
19
Amazon Web Services (AWS) and Microsoft Azure are the largest cloud providers. According to
a recent report in 2018 AWS had 41.5% of “application workloads” in the public cloud, while
Azure had 29.4% (https://www.skyhighnetworks.com/cloud-security-blog/microsoft-azure-closes-iaas-
adoption-gap-with-amazon-aws/). In term of revenue, Azure had a $37.6 billion run rate while AWS had a
$29.5 billion run rate (https://techcrunch.com/2019/02/01/aws-and-microsoft-reap-most-of-the-benefits-
of-expanding-cloud-market/).

22
7 Acknowledgements
We’d like to thank people who gave feedback on early drafts of this paper: Michael Abd-El-Malek
(Google), Aditya Akella (Wisconsin), Remzi H. Arpaci-Dusseau (Wisconsin), Bill Bolosky (Mi-
crosoft), Forrest Brazeal (Trek10), Eric Brewer (Google/UC Berkeley), Armando Fox (UC Berke-
ley), Changhua He (Ant Financial), Joseph M. Hellerstein (UC Berkeley), Mike Helmick (Google),
Marvin Theimer (Amazon Web Services), Keith Winstein (Stanford), and Matei Zaharia (Stan-
ford). The sponsors of the RISELab are Alibaba Group, Amazon Web Services, Ant Financial, Arm
Holdings, Capital One, Ericsson, Facebook, Google, Huawei, Intel, Microsoft, Scotiabank, Splunk,
VMware, and the National Science Foundation.

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28
8 Appendix. More Depth on Five Applications that Stretch To-
day’s Serverless Computing
8.1 ExCamera: Video encoding in real-time
ExCamera [26] aims to provide a real-time encoding service to users uploading their videos to sites,
such as YouTube. Depending on the size of the video, today’s encoding solutions can take tens of
minutes, even hours. To perform encoding in real time, ExCamera parallelizes the “slow” parts of
the encoding, and performs the “fast” parts serially. To do so, ExCamera exposes the internal state
of the video encoder and decoder, allowing encoding and decoding tasks to be executed using purely
functional semantics. In particular, each task takes the internal state along with video frames as
input, and emits the modified internal state as output.
ExCamera leverages the AWS Lambda platform to exploit of the new algorithm’s parallelism
so as to quickly scale up the computation. Unlike VM-based instances that take minutes to start
and need to be explicitly managed, cloud functions start in seconds and require no management.
Unfortunately, just using cloud functions out of the box is insufficient. The ExCamera tasks are
fine-grained, taking as little as a few seconds, and operate on a non-trivial amount of state. Thus,
using S3 to exchange the intermediate state incurs a significant overhead. Moreover, cloud functions
are behind network address translators (NATs); they can initiate connections, but not accept
them. This makes direct communication between cloud functions challenging. To get around this
issue, ExCamera uses a rendezvous server to relay packets between cloud functions. Furthermore,
ExCamera employs a coordinator to orchestrate the tasks and the communication between tasks
across cloud functions. This support enables one function invocation to run multiple tasks, thereby
amortizing its startup time. By doing so, ExCamera offers much lower latency than existing
encoding (60x faster than Google’s multithreaded vpxenc encoder on 128 cores) while still remaining
cost effective (6.17x cheaper than encoding using vpxenc on an 128-core x1.32xlarge EC2 instance).

8.2 MapReduce
Analytics frameworks such as MapReduce, Hadoop, and Spark, have been traditionally deployed on
managed clusters. While some of these analytics workloads are now moving to serverless computing,
these workloads mostly consist of Map-only jobs. The natural next step is supporting full fledged
MapReduce jobs. One of the driving forces behind this effort is leveraging the flexibility of serverless
computing to efficiently support jobs whose resource requirements vary significantly during their
execution. For example, query 95 in the TPC-DS benchmark [74] consists of eight stages, which
process from 0.8 MB to 66 GB of data per stage, an almost five order of magnitude difference!
The main challenge in supporting MapReduce jobs on top of the existing cloud functions plat-
forms is the shuffle operation. With shuffle, every Map task sends data to every reduce task.
Assuming there are M mappers and R reducers, a shuffle will generate M × R transfers (see Fig-
ure 3(a)). Since cloud functions don’t communicate directly with each other, all these transfers
must take place via an external storage. The number of transfers can be large.
As an example, assume we need to shuffle 100 TB of data with AWS cloud functions using S3
as the external storage. Given the existing resource constraints, a cloud function can process data
blocks no larger than 3 GB, which is the largest memory capacity of a cloud function today (see
Table 2).20 Hence, we need to partition the input into 33,000 blocks. If we were to have one Map
function per block, and an equal number of reduce function (i.e., M = R = 33, 000), we would
need to perform 1.11 billion transfers, or 2.22 billion IO operations (i.e., one write and one read per
20
This assumes the cloud function needs to read all data before creating the output.

29
Figure 3: (a) Shuffle operation with M mappers and N receivers. Each transfer happens via external
storage, which is shown by a large blue rectangle. The data corresponding to each transfer is shown
by small squares. (b) Multi-stage shuffle with S stages.

transfer)! This number is significant for systems like S3 that limit the number of IO operations/sec
(IOPS) and charge a premium per IO request. Thus, shuffling 100 TB can take tens of hours and
cost $12,000 for S3 IOPS alone.
One alternative is using high performance storage, such as ElastiCache21 instead of S3. However,
this high performance storage is expensive. Using 100 TB of such storage to shuffle 100 TB of data
would be far more expensive than a VM-based solution. Fortunately, dividing the shuffle in stages
(as in Figure 3(b)) reduces the needed amount of high performance storage significantly. For
example, if we use S = 50 stages for a 100 TB shuffle, we need only 2 TB of high-performance
storage. By appropriately choosing the size of this storage, we come close to matching the existing
VM-based frameworks in performance and cost.
For example, the current record of 100 TB CloudSort benchmark was 2,983 seconds for $144
using a cluster of 395 VMs, each with 4vCPU cores and 8GB memory. Our solution runs the same
task in 2,945 seconds for $163 using cloud functions ($117 with AWS Lambda) and a hybrid of
cloud object storage ($14 for AWS S3 IOPS cost) and the ElastiCache service ($32 for Redis cost).

8.3 Numpywren: Linear algebra

Large scale linear algebra computations are traditionally deployed on supercomputers or high-
performance computing clusters connected by high-speed, low-latency networks. Given this history,
serverless computing initially seems a poor fit.
Yet there are two reasons why serverless computing might still make sense for linear algebra
computations. First, managing clusters is a big barrier for many non-CS scientists. Second, the
amount of parallelism can vary dramatically during a computation. Figure 4 shows the working
set and the maximum degree of parallelism for Cholesky decomposition, one of the most popular
methods for solving linear equation on a large matrix. Provisioning a cluster with a fixed size will
either slow down the job or leave the cluster underutilized.
21
Elasticache is based on Redis. A single single-threaded Redis instance can handle 100K+ IOPS. 100 instances can
handle 1.3B transfers in just 130 seconds.

30
Figure 4: Theoretical profile of task parallelism and working set size over time in a distributed
Cholesky decomposition.

Recent work on the numpywren project shows that serverless computing may be a good match
for large scale linear algebra [47]. The key insight is that, for many linear algebra operations,
computation time often dominates communication for large problem sizes. For example, Cholesky,
LU, and Singular Value decompositions, all exhibit O(n3 ) computation and O(n2 ) communication
complexities. Numpywren leverages this observation, and shows for many popular algorithms that
even when using high latency storage systems such as S3, a serverless computing implementation
can achieve comparable performance to an optimized MPI implementation (ScaLAPACK) running
on a dedicated cluster. For certain linear algebra algorithms such as matrix multiply and Cholesky
decomposition and singular value decomposition, numpywren’s performance (completion time) is
only 1.3x (for all algorithms) of ScaLAPACK, and its CPU consumption (total CPU-hours) is 1.3x
for matrix multiply, 0.77x for Cholesky, and 0.44x for SVD. Currently cloud providers charge a 2x
premium in CPU-core-seconds pricing for their serverless offerings, so numpywren is cheaper for the
SVD algorithm. With increased efficiencies and greater competition, we anticipate the premium
ratio will fall further, expanding the cost-effectiveness of numpywren.
While these results are promising, using serverless computing for linear algebra has several
limitations. First, numpywren is only able to compete with dedicated implementations for large
problem sizes (generally larger than 256K × 256K dense matrix operations); the high latency of the
external storage makes the system uncompetitive for smaller problem instances. A more serious
limitation is that existing cloud functions platforms cannot efficiently implement the broadcast
communication pattern (Figure 2), employed by several popular algorithms such as QR decompo-
sition. This inefficiency arises because each cloud function provides very few cores (typically no
more than 1 or 2), and because the application has no control function placement.
As an example of communication inefficiency, consider N large machines or VMs, each of them
having K cores. A broadcast operation need only send N copies of the data over the network,
one to each machine, where all cores can share the same copy. If we assume instead an equivalent
deployment using cloud functions, where each function has a single core on a separate computer,
then we need to send N × K data copies over the network to reach the same number of cores. As
such the network traffic in this case is K times higher, a non-trivial overhead. Figure 2(a) illustrates
this example for N = K = 2, by assuming each node has 2 cores, and each function uses a single
core, and hence can run two functions.

31
8.4 Cirrus: Machine learning training
Machine learning researchers have traditionally used clusters of VMs for different tasks in ML
workflows such as preprocessing, model training, and hyperparameter tuning. One challenge with
this approach is that different stages of a pipeline can require significantly different amounts of
resources. As with linear algebra algorithms, a fixed cluster size will either lead to severe under-
utilization or to severe slowdown. Serverless computing can address this challenge by scaling each
stage independently scale to meet its resource demands. Further, it frees developers from managing
these servers.
Recent work on the Cirrus project offers promise for ML training pipelines on serverless com-
puting. Cirrus leverages three main observations. First, existing serverless offerings provide linear
scalability of compute (cloud functions) and storage throughput (e.g., to S3) up to thousands of
cores. Second, caching and prefetching training data can saturate the CPU. Third, a relatively small
amount of high-performance storage can significantly improve the performance of these pipelines,
and, in particular, of training. In the case of Cirrus, we provide this high-performance storage
using a few VM instances to implement an in-memory parameter server [75].
Using serverless computing for ML workloads is still challenging. First, the gradient needs to
be broadcast to every cloud function. As noted in the previous section and as Figure 2 illustrates,
using cloud functions incurs a much higher communication overhead versus large VM instances.
Second, the parameter server must handle asynchronous fine grain updates. This can significantly
strain its network connection, both on account of bandwidth and number of packets.
We’ve evaluated Cirrus against three VM-based ML frameworks: Tensorflow, Bosen and Spark.
On a Sparse Logistic Regression workload Cirrus with 10 lambda workers converges 3x faster than
Tensorflow (1 x m5d.4xlarge, 32 cores) and 5x faster than Bosen (2 x m5d.4xlarge, 16 cores). On
a Collaborative filtering workload Cirrus converges in less time to a lower loss (RMSE of 0.83 vs.
0.85). While Cirrus can outperform serverful in terms of training completion time, it does not
outperform on cost, which may be up to 7x higher.

8.5 Serverless SQLite: Databases

Stateful workloads, such as databases, are particularly challenging for serverless computing. At
first glance, these services embody the antithesis of the stateless nature of serverless computing.
While cloud providers offer many managed database services with some elasticity [28–32], an in-
triguing question is whether a third party could implement a serverless database directly on top of
a serverless computing platform.
A strawman solution would be to run common transactional databases, such as PostgreSQL,
Oracle, or MySQL inside serverless functions. However, that immediately runs into a number
of challenges. First, serverless computing has no built-in persistent storage, so we need to use a
remote persistent store, which introduces large latency. Second, these databases assume connection-
oriented protocols, i.e., databases run as services accepting connections from clients. This assump-
tion conflicts with existing cloud functions that are running behind network address translators,
and thus don’t support incoming connections. Finally, while many high performance databases
rely on shared memory [34], cloud functions run in isolation so cannot share memory. Shared-
nothing distributed databases [35–37] do not require shared memory, but they expect nodes to be
directly addressable on the network and they expect cluster membership to change only slowly. All
these issues pose significant challenges to running traditional database software atop of serverless
computing, or to implementing equivalent functionality.
Despite these challenges, we did succeed in running the SQLite embedded database on a server-

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less computing platform. SQLite runs as an application library, so it doesn’t need to support
inbound network connections. Also, SQLite doesn’t require shared memory and instead relies on
access to a fast, cached, shared file system. Our approach to providing such a file system in a
serverless environment, is to interpose an in-memory transactional caching buffering layer between
SQLite and the cloud provider’s network file system (e.g., AWS EFS or Azure Files). By maintain-
ing a change log in a hidden file, we provide both transactional isolation and effective caching for
shared file system access. This scales as does serverless compute, supporting hundreds of concurrent
functions and sub-millisecond latencies to achieve over 10 million tpmC (transactions per minute)
on a modified read-only TPC-C benchmark [76], performance comparable to that which commer-
cial RDBMS systems report on the unmodified benchmark. Whereas these unmodified benchmarks
comprise about 70% writes, our scalability with writes only reaches a tpmC of roughly 100 due to
reliance on database-level locking.
Our conclusion is that most database-like applications will continue to be provided as BaaS,
unless the application naturally makes writes to the database rare.

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