research-article

Auto-scaling to minimize cost and meet application deadlines in cloud workflows

Authors:

Marty HumphreyAuthors Info & Claims

SC '11: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis

Article No.: 49, Pages 1 - 12

https://doi.org/10.1145/2063384.2063449

Published: 12 November 2011 Publication History

Abstract

A goal in cloud computing is to allocate (and thus pay for) only those cloud resources that are truly needed. To date, cloud practitioners have pursued schedule-based (e.g., time-of-day) and rule-based mechanisms to attempt to automate this matching between computing requirements and computing resources. However, most of these "auto-scaling" mechanisms only support simple resource utilization indicators and do not specifically consider both user performance requirements and budget concerns. In this paper, we present an approach whereby the basic computing elements are virtual machines (VMs) of various sizes/costs, jobs are specified as workflows, users specify performance requirements by assigning (soft) deadlines to jobs, and the goal is to ensure all jobs are finished within their deadlines at minimum financial cost. We accomplish our goal by dynamically allocating/deallocating VMs and scheduling tasks on the most cost-efficient instances. We evaluate our approach in four representative cloud workload patterns and show cost savings from 9.8% to 40.4% compared to other approaches.

References

[1]

Amazon EC2. http://aws.amazon.com/ec2/

[2]

Z. Hill, J. Li, M. Mao, A. Ruiz-Alvarez and M. Humphrey. 2010. Early Observations on the Performance of Windows Azure. In Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing. Chicago, Illinois, June 21, 2010. DOI=http://dx.doi.org/10.1145/1851476.1851532

Digital Library

[3]

M. Mao, J. Li and M. Humphrey. 2010. Cloud Auto-Scaling with Deadline and Budget Constraints. In Proceedings of 11th ACM/IEEE International Conference on Grid Computing. Brussels, Belgium, Oct 25-28, 2010.

[4]

H. Lim, S. Babu, J. Chase, and S. Parekh. 2009. Automated Control in Cloud Computing: Challenges and Opportunities. In 1st Workshop on Automated Control for Datacenters and Clouds. June 2009. DOI=http://dx.doi.org/10.1145/1555271.1555275

Digital Library

[5]

P. Padala, K. Shin, X. Zhu, M. Uysal, Z. Wang, S. Singhal, A. Merchant, and K. Salem. 2007. Adaptive Control of Virtualized Resources in Utility Computing Environments. EuroSys, 2007. DOI=http://dx.doi.org/10.1145/1272998.1273026

Digital Library

[6]

B. Urgaonkar, P. Shenoy, A. Chandra, and P. Goyal. 2005. Dynamic provisioning of multi-tier internet applications. In 2 ^nd International Conference on Autonomic Computing, Seattle, WA, USA, June 2005. DOI=http://dx.doi.org/10.1109/ICAC.2005.27

Digital Library

[7]

Q. Zhang, L. Cherkasova, E. Smirni. 2007. A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications. In Proceedings of the Fourth International Conference on Autonomic Computing, Jacksonville, Florida, USA, June 2007. DOI=http://dx.doi.org/10.1109/ICAC.2007.1

Digital Library

[8]

A. Chandra, W. Gong and P. Shenoy. 2003. Dynamic Resource Allocation for Shared data centers using online measurements. In Proceedings of the 11th International Workshop on Quality of Service, 2003. DOI=http://dx.doi.org/10.1145/885651.781067

Digital Library

[9]

J. Chase, D. Irwin, L. Grit, J. Moore, and S. Sprenkle. 2003. Dynamic Virtual Clusters in A Grid Site Manager. In Proceedings of the 12th High Performance Distributed Computing, Seattle, Washington, June 22--24, 2003.

Digital Library

[10]

S. Park and M. Humphrey. 2008. Feedback-Controlled Resource Sharing for Predictable eScience. In Proceedings IEEE/ACM International Conference for High Performance Computing, Networking, Storage and Analysis (SC08), Austin, Texas, 2008.

Digital Library

[11]

S. Park and M. Humphrey. 2010. Predictable High Performance Computing using Feedback Control and Admission Control. IEEE Transactions on Parallel and Distributed Systems(TPDS), Mar, 2010. DOI=http://dx.doi.org/10.1109/TPDS.2010.100

Digital Library

[12]

P. Ruth, P. McGachey and D. Xu. 2005. VioCluster: Virtualization for Dynamic Computational Domains. Cluster Computing, 2005. IEEE International, pages 1--10, Sep 2005.

[13]

P. Marshall, K. Keahey and T. Freeman. 2010. Elastic Site Using Clouds to Elastically Extend Site Resources. In the 10 ^th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, Melbourne, Victoria, Australia, 2010. DOI=http://dx.doi.org/10.1109/CCGRID.2010.80

Digital Library

[14]

M. D. Assuncao, A. D. Costanzo, and R. Buyya. 2009. Evaluating the Cost-benefit of Using Cloud Computing to Extend the Capacity of Clusters. In Proceedings of the 18th ACM international symposium on High performance distributed computing, Munich, Germany, June 11-13, 2009. DOI=http://dx.doi.org/10.1145/1551609.1551635

Digital Library

[15]

D. Menasc and E. Casalicchio. 2004. A Framework for Resource Allocation in Grid Computing. In Proc. of the 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, pp. 259--267, 2004.

Digital Library

[16]

J. Yu, R. Buyya and C. Tham. 2005. A Cost-based Scheduling of Scientific Workflow Applications on Utility Grids. In Proceedings of the First IEEE International Conference on e-Science and Grid Computing. Melbourne, Australia, Dec. 2005, pp. 140--147. DOI=http://dx.doi.org/10.1109/E-SCIENCE.2005.26

Digital Library

[17]

R. Sakellariou and H. Zhao. 2005. Scheduling Workflows with Budget Constraints. Integrated Research in Grid Computing. CoreGrid series, Springer-Verlag, 2005.

[18]

J. Yu and R. Buyya. 2006. Scheduling Scientific Workflow Applications with Deadline and Budget Constraints using Genetic Algorithms. Sci. Program, 2006, 14(3-4), 217--230,

Digital Library

[19]

J. Yu and R. Buyya. 2006. A Budget Constraint Scheduling of Workflow Applications on Utility Grids using Genetic Algorithms. In Proceedings of the Workshop on Workflows in Support of Large-Scale Science (WORKS06). Paris, France, 2006.

[20]

Y. Yazir, C. Matthews, R. Farahbod, S. Neville, etc. 2010. Dynamic Resource Allocation in Computing Clouds using Distributed Multiple Criteria Decision Analysis. In 3 ^rd International Conference on Cloud Computing, Miami, Florida, USA, 2010. DOI=http://dx.doi.org/10.1109/CLOUD.2010.66

Digital Library

[21]

M. Mazzucco, D. Dyachuk and R. Deters. 2010. Maximizing Cloud Providers Revenues via Energy Aware Allocation Policies. In 3 ^rd International Conference on Cloud Computing, Miami, Florida, USA, 2010. DOI=http://dx.doi.org/10.1109/CLOUD.2010.68

Digital Library

[22]

I. Goiri, J. Guitart and J. Torres. 2010. Characterizing Cloud Federation for Enhancing Providers' Profit. In 3 ^rd International Conference on Cloud Computing, Miami, Florida, USA, 2010. DOI=http://dx.doi.org/10.1109/CLOUD.2010.32

Digital Library

[23]

F. Chang, J. Ren and R. Viswanathan. 2010. Optimal Resource Allocation in Clouds. In 3 ^rd International Conference on Cloud Computing, Miami, Florida, USA 2010. DOI= http://dx.doi.org/10.1109/CLOUD.2010.38

Digital Library

[24]

E. Deelman, G. Singh, M. Livny, B Berriman, and J. Good. 2008. The Cost of Doing Science on the Cloud: The montage example. In Proceeding SC '08 Proceedings of the 2008 ACM/IEEE conference on Supercomputing. pages 1--12. 2008.

Digital Library

[25]

N. Chohan, C. Castillo, M. Spreitzer, M. Steinder, A. Tantawi and C. Krintz. 2010. See Spot Run: Using Spot Instances for MapReduce Workflows. In 2nd USENIX Workshop on Hot Topics in Cloud Computing, HotCloud 2010. Boston, MA. June 2010.

Digital Library

[26]

R. Bossche, K. Vanmechelen and J. Broeckhove. 2010. Cost-Optimal Scheduling in Hybrid IaaS Clouds for Deadline Constrained Workloads. In 3 ^rd International Conference on Cloud Computing, Miami, Florida, USA, 2010. DOI=http://dx.doi.org/10.1109/CLOUD.2010.58

Digital Library

[27]

A. Oprescu and T. Kielmann. 2009. Bag-of-Tasks Scheduling under Budget Constraints. In 2nd International Conference on Cloud Computing, Los Angeles, USA, 2009.

Digital Library

[28]

RightScale. http://rightscale.com

[29]

enStratus. http://www.enstratus.com

[30]

Scalr. https://www.scalr.net

[31]

AzureScale. http://archive.msdn.microsoft.com/azurescale

[32]

Windows Azure. http://www.microsoft.com/windowsazure

[33]

G. Nudd, D. Kerbyson, E. Papaefstathiou, S Perry, J. Harper and D. Wilcox. 2000. PACE- A Toolset for the performance Prediction of Parallel and Distributed Systems. In International Journal of High Performance Computing Applications, Volume 14 Issue 3, August 2000. DOI=http://dx.doi.org/10.1177/109434200001400306

Digital Library

[34]

K. Cooper et al. 2004. New Grid Scheduling and Rescheduling Methods in the GrADS Project. In NSF Next Generation Software Workshop, International Parallel and Distributed Processing Symposium, Santa Fe, IEEE CS Press, Los Alamitos, CA, USA, April 2004. DOI=http://dx.doi.org/10.1007/s10766-005-3584-4

[35]

S. Jang et al. 2004. Using Performance Prediction to Allocate Grid Resources. Technical Report 2004-25, GriPhyN Project, USA. 2004.

[36]

W. Smith, I. Foster, and V. Taylor. 1998. Predicting Application Run Times Using Historical Information. In Workshop on Job Scheduling Strategies for Parallel Processing, 12th International Parallel Processing Symposium & 9th Symposium on Parallel and Distributed Processing (IPPS/SPDP '98), IEEE Computer Society Press, Los Alamitos, CA, USA, 1998.

Digital Library

[37]

Earliest Deadline First Scheduling. http://en.wikipedia.org-/wiki/Earliest_deadline_first_scheduling

[38]

Earliest Deadline First Scheduling. http://retis.sssup.it-/~lipari/courses/str06/10.edf.pdf

[39]

Cloud Workload Patterns. http://bramveen.com/2010/07/13/-workload-patterns-for-cloud-computing/

[40]

Cloud Workload Patterns. http://www.microsoftpdc.com-/2009/SVC54

[41]

Amazon EC2 Price. http://aws.amazon.com/ec2/#pricing

[42]

Windows Azure Diagnostics. http://archive.msdn.microsoft-.com/WADiagnostics

[43]

AWS CloudWatch. http://aws.amazon.com/cloudwatch/

[44]

J. Dean and S. Ghemawat. 2008. MapReduce: simplified data processing on large clusters. In Communications of the ACM - 50th anniversary issue: 1958 - 2008, Volume 51 Issue 1, January 2008. DOI=http://dx.doi.org/10.1145/1327452.1327492

Digital Library

[45]

Hadoop. http://hadoop.apache.org/

[46]

M. Isard, M. Budiu, Y. Yu, A. Birrell and D. Fetterly. 2007. In Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007, Lisbon, Portugal, 2007. DOI=http://dx.doi.org/10.1145/1272998.1273005

Digital Library

Cited By

Maxenti SD’Oro SBonati LPolese MCapone AMelodia T(2024)ScalO-RAN: Energy-aware Network Intelligence Scaling in Open RANIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621159(891-900)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621159
Song SPan LLiu S(2024)A Q-learning based auto-scaling approach for provisioning big data analysis services in cloud environmentsFuture Generation Computer Systems10.1016/j.future.2024.01.003154:C(140-150)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.future.2024.01.003
Wang YShu ZChen SLin JZhang Z(2024)A cost and demand sensitive adjustment algorithm for service function chain in data center networkComputer Networks10.1016/j.comnet.2024.110254242(110254)Online publication date: Apr-2024
https://doi.org/10.1016/j.comnet.2024.110254
Show More Cited By

Index Terms

Auto-scaling to minimize cost and meet application deadlines in cloud workflows
1. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles
        Organizing principles for web applications

Recommendations

Auto-Scaling Web Applications in Clouds: A Taxonomy and Survey

Web application providers have been migrating their applications to cloud data centers, attracted by the emerging cloud computing paradigm. One of the appealing features of the cloud is elasticity. It allows cloud users to acquire or release computing ...
Auto-scaling techniques in container-based cloud and edge/fog computing: Taxonomy and survey
Abstract
The long-held dream of computing as a service was realized with the emergence of cloud computing. Recently, fog and edge computing have been introduced as extensions of cloud networks, providing networking, processing, data management, ...
A model driven method to deploy auto-scaling configuration for cloud services
RELENG 2016: Proceedings of the 4th International Workshop on Release Engineering

Vendor lock-in is the issues in auto-scaling configuration; scaling configuration of a service cannot automatically transfer when the service is migrated from one cloud to another cloud. To facilitate fast service deployment, there is a need to ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

SC '11: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis

November 2011

866 pages

ISBN:9781450307710

DOI:10.1145/2063384

Conference Chair:
Scott Lathrop
University of Chicago
,
Program Chairs:
Jim Costa
Sandia National Laboratories
,
William Kramer
National Center for Supercomputing Applications

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGARCH: ACM Special Interest Group on Computer Architecture
IEEE-CS: Computer Society

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 November 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SC '11

Sponsor:

SIGARCH
IEEE-CS

SC '11: International Conference for High Performance Computing, Networking, Storage and Analysis

November 12 - 18, 2011

Washington, Seattle

Acceptance Rates

SC '11 Paper Acceptance Rate 74 of 352 submissions, 21%;

Overall Acceptance Rate 1,516 of 6,373 submissions, 24%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

358
Total Citations
View Citations
2,483
Total Downloads

Downloads (Last 12 months)81
Downloads (Last 6 weeks)3

Reflects downloads up to 09 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Maxenti SD’Oro SBonati LPolese MCapone AMelodia T(2024)ScalO-RAN: Energy-aware Network Intelligence Scaling in Open RANIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621159(891-900)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621159
Song SPan LLiu S(2024)A Q-learning based auto-scaling approach for provisioning big data analysis services in cloud environmentsFuture Generation Computer Systems10.1016/j.future.2024.01.003154:C(140-150)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.future.2024.01.003
Wang YShu ZChen SLin JZhang Z(2024)A cost and demand sensitive adjustment algorithm for service function chain in data center networkComputer Networks10.1016/j.comnet.2024.110254242(110254)Online publication date: Apr-2024
https://doi.org/10.1016/j.comnet.2024.110254
Saeedizade EAshtiani M(2024)Scientific workflow scheduling algorithms in cloud environments: a comprehensive taxonomy, survey, and future directionsJournal of Scheduling10.1007/s10951-024-00820-1Online publication date: 28-Oct-2024
https://doi.org/10.1007/s10951-024-00820-1
Sukhoroslov OGorokhovskii M(2024)Benchmarking DAG Scheduling Algorithms on Scientific Workflow InstancesSupercomputing10.1007/978-3-031-49435-2_1(3-20)Online publication date: 5-Jan-2024
https://doi.org/10.1007/978-3-031-49435-2_1
Rajendran PMaloo SMitra RChanchal AAburukba R(2023)Comparison of Cloud-Computing Providers for Deployment of Object-Detection Deep Learning ModelsApplied Sciences10.3390/app13231257713:23(12577)Online publication date: 22-Nov-2023
https://doi.org/10.3390/app132312577
Rubak ATaheri JFu SCarnevale LRana OVillari M(2023)Machine Learning for Predictive Resource Scaling of Microservices on Kubernetes PlatformsProceedings of the IEEE/ACM 16th International Conference on Utility and Cloud Computing10.1145/3603166.3632165(1-8)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1145/3603166.3632165
Moradi HWang WZhu D(2023)Online Performance Modeling and Prediction for Single-VM Applications in Multi-Tenant CloudsIEEE Transactions on Cloud Computing10.1109/TCC.2021.307869011:1(97-110)Online publication date: 1-Jan-2023
https://doi.org/10.1109/TCC.2021.3078690
Chen GQi JSun YHu XDong ZSun Y(2023)A collaborative scheduling method for cloud computing heterogeneous workflows based on deep reinforcement learningFuture Generation Computer Systems10.1016/j.future.2022.11.032141(284-297)Online publication date: Apr-2023
https://doi.org/10.1016/j.future.2022.11.032
Imran MHasan FAhmad FShahid M(2023)A Workflow Allocation Strategy Using Elitist Teaching–Learning-Based Optimization Algorithm in Cloud ComputingInternational Conference on Innovative Computing and Communications10.1007/978-981-99-3010-4_53(667-676)Online publication date: 1-Aug-2023
https://doi.org/10.1007/978-981-99-3010-4_53
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents