SOSP

This paper presents three building blocks for enabling the efficient and safe design of persistent data stores for emerging non-volatile memory technologies. Taking the fullest advantage of the low latency and high bandwidths of emerging memories such ...

How to satisfy the on-demand environment while providing highly dependable services with minimum cost is a challenging issue. Improvements in the reusability of virtualization technology have enabled operating system's adaptability, which helps users ...

Today, many system designers try to fit the entire data set of an application in RAM to avoid the cost of accessing magnetic disk. However, for many data-centric applications this is not an option due to the capacity and high $/GB constraints of RAM. As ...

Multicore platforms are moving from small numbers of homogeneous cores to 'scale out' designs with multiple tiles or 'islands' of cores residing on a single chip, each with different resources and potentially controlled by their own resource managers. ...

Dynamic Voltage and Frequency Scaling (DVFS) has been widely deployed and proven to reduce energy consumption at low CPU utilization levels; however, our measurements of the n-tier application benchmark (RUBBoS) performance showed significant ...

Services such as Facebook and Twitter host and disseminate data on behalf of billions of users. Because these services often manage personal data, they allow users to specify access policies controlling how their data is shared with others within the ...

Modern scalable distributed systems are designed to be partition-tolerant. They are often required to support increasing load in service requests elastically, and to provide seamless services even when some servers malfunction. Partition-tolerance ...

Today's large-scale services generally exploit loosely-coupled architectures that restrict functionality requiring tight cooperation (e.g., leader election, synchronization, and reconfiguration) to a small subset of nodes. In contrast, this work ...

Distributed graph analytics frameworks must offer low and balanced communication and computation, low preprocessing overhead, low memory footprint, and scalability. We present LFGraph, a fast, scalable, distributed, in-memory graph analytics engine ...

The big-data community generally favors a two stage methodology whereby data is first collected, then uploaded for analysis using tools like MapReduce. During analysis the data won't change; this simplifies fault-tolerance and makes it worthwhile to ...