Article

Autonomic resource provisioning for cloud-based software

Authors:

Pooyan Jamshidi,

Claus PahlAuthors Info & Claims

SEAMS 2014: Proceedings of the 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

Pages 95 - 104

https://doi.org/10.1145/2593929.2593940

Published: 02 June 2014 Publication History

Abstract

Cloud elasticity provides a software system with the ability to maintain optimal user experience by automatically acquiring and releasing resources, while paying only for what has been consumed. The mechanism for automatically adding or removing resources on the fly is referred to as auto-scaling. The state-of-the-practice with respect to auto-scaling involves specifying threshold-based rules to implement elasticity policies for cloud-based applications. However, there are several shortcomings regarding this approach. Firstly, the elasticity rules must be specified precisely by quantitative values, which requires deep knowledge and expertise. Furthermore, existing approaches do not explicitly deal with uncertainty in cloud-based software, where noise and unexpected events are common. This paper exploits fuzzy logic to enable qualitative specification of elasticity rules for cloud-based software. In addition, this paper discusses a control theoretical approach using type-2 fuzzy logic systems to reason about elasticity under uncertainties. We conduct several experiments to demonstrate that cloud-based software enhanced with such elasticity controller can robustly handle unexpected spikes in the workload and provide acceptable user experience. This translates into increased profit for the cloud application owner.

References

[1]

M. Armbrust and e. al., "A view of cloud computing," Communications of the ACM, vol. 53, no. 4, pp. 50-58, 2010.

Digital Library

[2]

P. Jamshidi, A. Ahmad and C. Pahl, "Cloud Migration Research: A Systematic Review," IEEE Transactions on Cloud Computing, 2013.

Digital Library

[3]

N. R. Herbst, S. Kounev and R. Reussner, "Elasticity in Cloud Computing: What It Is, and What It Is Not," in ICAC, 2013.

[4]

S. Islam, K. Lee, A. Fekete and A. Liu, "How a consumer can measure elasticity for cloud platforms," in ICPE, 2012.

Digital Library

[5]

"Animoto's Facebook Scale-Up," {Online}. Available: http://tinyurl.com/qdk7om4.

[6]

G. Linden, "Make Data Useful," Amazon, 2009.

[7]

H. C. Lim, S. Babu, J. S. Chase and S. S. Parekh, "Automated control in cloud computing: challenges and opportunities," in ACDC, 2009.

Digital Library

[8]

H. C. Lim, S. Babu and J. S. Chase, "Automated control for elastic storage," in ICAC, 2010.

Digital Library

[9]

L. M. Vaquero, L. Rodero-Merino and R. Buyya, "Dynamically scaling applications in the cloud," Computer Communication Review, 2011.

Digital Library

[10]

H. Ghanbari, B. Simmons, M. Litoiu, C. Barna and G. Iszlai, "Optimal autoscaling in a IaaS cloud," in ICAC, 2012.

Digital Library

[11]

Z. Shen, S. Subbiah, X. Gu and J. Wilkes, "Cloudscale: elastic resource scaling for multi-tenant cloud systems," in SCC, 2011.

Digital Library

[12]

A. Gambi, G. Toffetti and M. Pezzè, "Assurance of self-adaptive controllers for the cloud," in Assurances for Self-Adaptive Systems, 2013.

[13]

A. Gambi, W. Hummer, H.-L. Truong and S. Dustdar, "Testing Elastic Computing Systems," Internet Computing, 2013.

Digital Library

[14]

N. N. Karnik, J. M. Mendel and Q. Liang, "Type-2 fuzzy logic systems," IEEE Transactions on Fuzzy Systems, vol. 7, no. 6, pp. 643-658, 1999.

Digital Library

[15]

D. Betts, Developing Multi-tenant Applications for the Cloud, Microsoft, 2012.

[16]

D. Garlan and e. al., "Rainbow: Architecture-based selfadaptation with reusable infrastructure," Computer, vol. 37, no. 10, pp. 46-54, 2004.

Digital Library

[17]

H. Ghanbari, B. Simmons, M. Litoiu and G. Iszlai, "Exploring alternative approaches to implement an elasticity policy," in ICCC, 2011.

Digital Library

[18]

T. Lorido-Botrán, J. Miguel-Alonso and J. A. Lozano, "Autoscaling Techniques for Elastic Applications in Cloud Environments," University of Basque Country, Tech. Rep. EHUKAT-IK-09-12, 2012.

[19]

X. Dutreilh, N. Rivierre, A. Moreau, J. Malenfant and I. Truck, "From data center resource allocation to control theory and back," in ICCC, 2010.

Digital Library

[20]

M. Maurer, I. Brandic and R. Sakellariou, "Enacting SLAs in clouds using rules," in Euro-Par, 2011.

Digital Library

[21]

P. Marshall, K. Keahey and T. Freeman, "Elastic site: Using clouds to elastically extend site resources," in ICCCGC, 2010.

Digital Library

[22]

E. Caron, L. Rodero-Merino, F. Desprez and A. Muresan, "Autoscaling, load balancing and monitoring in commercial and opensource clouds," 2012.

[23]

B. Wilder, Cloud Architecture Patterns: Using Microsoft Azure, O'Reilly, 2012.

[24]

M. Mao and M. Humphrey, "A performance study on the vm startup time in the cloud," in CLOUD, 2012.

Digital Library

[25]

J. O. Kephart and D. M. Chess, "The vision of autonomic computing," Computer, vol. 36, no. 1, p. 41–50, 2003.

Digital Library

[26]

L. A. Zadeh, "The concept of a linguistic variable and its application to approximate reasoning—I," IS, 1975.

[27]

J. M. Mendel, "Type-2 fuzzy sets and systems: an overview," Computational Intelligence Magazine, vol. 2, no. 1, 2007.

Digital Library

[28]

J. M. Mendel, R. I. John and F. Liu, "Interval type-2 fuzzy logic systems made simple," IEEE TFS, 2006.

Digital Library

[29]

D. Wu, "On the fundamental differences between Type-1 and interval Type-2 fuzzy logic controllers," IEEE Transactions on Fuzzy Systems, 2012.

Digital Library

[30]

J. M. Mendel, H. Hagras and R. I. John, "Standard background material about interval type-2 fuzzy logic systems that can be used by all authors," CIS, 2010.

[31]

D. Weyns, S. Malek and J. Andersson, "FORMS: a formal reference model for self-adaptation," in ICAC, 2010.

Digital Library

[32]

Q. Liang and J. M. Mendel, "Interval type-2 fuzzy logic systems: theory and design," IEEE TFS, vol. 8, no. 5, pp. 535-550, 2000.

Digital Library

[33]

J. M. Mendel, Uncertain rule-based fuzzy logic system: introduction and new directions, Prentice Hall, 2001.

[34]

J. M. Mendel, "Computing with words, when words can mean different things to different people," in ICSC Sympusium Fuzzy Logic Application, 1999.

[35]

Q. Liang, N. N. Karnik and J. M. Mendel, "Connection admission control in ATM networks using survey-based type-2 fuzzy logic systems," Transactions on Applications and Systems, Man, and Cybernetics, 2000.

Digital Library

[36]

N. N. Karnik and J. M. Mendel, "Centroid of a type-2 fuzzy set," Information Sciences, vol. 132, no. 1, pp. 195-220, 2001.

Digital Library

[37]

P. S. Kalekar, "Time series forecasting using Holt-Winters exponential smoothing," Kanwal Rekhi School of Information Technology, 2004.

[38]

"Anonymized access logs," National Laboratory for Applied Network Research, 2001. {Online}. Available: ftp://ftp.ircache.net/Traces/.

[39]

A. Gandhi, M. Harchol and R. Raghunathan, "Autoscale: Dynamic, robust capacity management for multi-tier data centers," TOCS, 2012.

Digital Library

[40]

P. Bodik, A. Fox, M. J. Franklin, M. I. Jordan and a. D. A. Patterson, "Characterizing, modeling, and generating workload spikes for stateful services," in SCC, 2010.

Digital Library

[41]

G. Galante and L. C. E. d. Bona, "A survey on cloud computing elasticity," in UCC, 2012.

Digital Library

[42]

E. Barrett, E. Howley and J. Duggan, "Applying reinforcement learning towards automating resource allocation and application scalability in the cloud," Concurrency and Computation: Practice and Experience, 2012.

[43]

B. Urgaonkar, P. Shenoy, A. Chandra, P. Goyal and T. Wood, "Agile dynamic provisioning of multi-tier internet applications," ACM TAAS, 2008.

Digital Library

[44]

A. Ali-Eldin, J. Tordsson and E. Elmroth, "An adaptive hybrid elasticity controller for cloud infrastructures," in NOMS, 2012.

[45]

J. Xu, M. Zhao, J. Fortes, R. Carpenter and M. Yousif, "On the use of fuzzy modeling in virtualized data center management," in ICAC, 2007.

Digital Library

[46]

J. Rao, Y. Wei, J. Gong and C. Z. Xu, "DynaQoS: model-free self-tuning fuzzy control of virtualized resources for QoS provisioning," in IWQoS, 2011.

Digital Library

[47]

C. Klein, M. Maggio, K. E. Årzén and F. Hernández-Rodriguez, "Brownout: Building More Robust Cloud Applications," in ICSE, 2014.

Digital Library

Cited By

Iqbal MZhong ZAhmad IRay BJamshidi P(2023)CAMEOProceedings of the 2023 ACM Symposium on Cloud Computing10.1145/3620678.3624791(555-571)Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1145/3620678.3624791
Ma XZong KRezaeipanah A(2023)Auto-scaling and computation offloading in edge/cloud computing: a fuzzy Q-learning-based approachWireless Networks10.1007/s11276-023-03486-330:2(637-648)Online publication date: 28-Sep-2023
https://doi.org/10.1007/s11276-023-03486-3
Kanakamedala PReddy MKumar GSai MReddy P(2023)Optimal and Virtual Multiplexer Resource Provisioning in Multiple Cloud Service Provider SystemInternational Conference on Innovative Computing and Communications10.1007/978-981-99-3010-4_48(587-597)Online publication date: 1-Aug-2023
https://doi.org/10.1007/978-981-99-3010-4_48
Show More Cited By

Index Terms

Autonomic resource provisioning for cloud-based software

Recommendations

Towards Understanding Uncertainty in Cloud Computing Resource Provisioning

In spite of extensive research of uncertainty issues in different fields ranging from computational biology to decision making in economics, a study of uncertainty for cloud computing systems is limited. Most of works examine uncertainty phenomena in ...
Optimal resource provisioning for cloud computing environment

The paper presents an efficient cloud resource provisioning approach. The Software as a Service (SaaS) provider leases resources from cloud providers and also leases software as services to SaaS users. The SaaS providers aim at minimizing the payment of ...
Resource provisioning for cloud applications: a 3-D, provident and flexible approach

The scalability feature of cloud computing attracts application service providers (ASPs) to use cloud application hosting. In cloud environments, resources can be dynamically provisioned on demand for ASPs. Autonomic resource provisioning for the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SEAMS 2014: Proceedings of the 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

June 2014

174 pages

ISBN:9781450328647

DOI:10.1145/2593929

General Chair:
Gregor Engels
University of Paderborn, Germany
,
Program Chair:
Nelly Bencomo
Aston University, UK

Copyright © 2014 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

SIGSOFT: ACM Special Interest Group on Software Engineering

In-Cooperation

TCSE: IEEE Computer Society's Tech. Council on Software Engin.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 June 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

ICSE '14

Sponsor:

SIGSOFT

ICSE '14: 36th International Conference on Software Engineering

June 2 - 3, 2014

Hyderabad, India

Acceptance Rates

Overall Acceptance Rate 17 of 31 submissions, 55%

Upcoming Conference

ICSE 2025

2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

Ottawa , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

112
Total Citations
View Citations
911
Total Downloads

Downloads (Last 12 months)46
Downloads (Last 6 weeks)1

Reflects downloads up to 30 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Iqbal MZhong ZAhmad IRay BJamshidi P(2023)CAMEOProceedings of the 2023 ACM Symposium on Cloud Computing10.1145/3620678.3624791(555-571)Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1145/3620678.3624791
Ma XZong KRezaeipanah A(2023)Auto-scaling and computation offloading in edge/cloud computing: a fuzzy Q-learning-based approachWireless Networks10.1007/s11276-023-03486-330:2(637-648)Online publication date: 28-Sep-2023
https://doi.org/10.1007/s11276-023-03486-3
Kanakamedala PReddy MKumar GSai MReddy P(2023)Optimal and Virtual Multiplexer Resource Provisioning in Multiple Cloud Service Provider SystemInternational Conference on Innovative Computing and Communications10.1007/978-981-99-3010-4_48(587-597)Online publication date: 1-Aug-2023
https://doi.org/10.1007/978-981-99-3010-4_48
Han DMa GCai YWang BLi A(2022)ProbaSAS: Modeling and Decision-Making Approach for Self-Adaptive Software Systems under Uncertainty2022 41st Chinese Control Conference (CCC)10.23919/CCC55666.2022.9901985(5871-5876)Online publication date: 25-Jul-2022
https://doi.org/10.23919/CCC55666.2022.9901985
Ghari SSchmerl BMaggio MCámara J(2022)Devops for digital businessProceedings of the 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems10.1145/3524844.3528069(53-57)Online publication date: 18-May-2022
https://dl.acm.org/doi/10.1145/3524844.3528069
Zhao JGao XLi Y(2022)Research on Elastic Extension of Multi Type Resources for OpenMP Program2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00155(971-978)Online publication date: Dec-2022
https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00155
Hadded LHamrouni T(2022)Optimal autonomic management of service-based business processes in the cloudSoft Computing10.1007/s00500-022-07124-6Online publication date: 2-May-2022
https://doi.org/10.1007/s00500-022-07124-6
Dong HQin SXu YQiao BZhou SYang XLuo CZhao PLin QZhang HAbuduweili ARamanujan SSubramanian KZhou ARajmohan SZhang DMoscibroda TSpinellis DGousios GChechik MDi Penta M(2021)Effective low capacity status prediction for cloud systemsProceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3468264.3473917(1236-1241)Online publication date: 20-Aug-2021
https://dl.acm.org/doi/10.1145/3468264.3473917
Hadded LCharrada FTata S(2021)Optimizing Autonomic Resources for the Management of Large Service-Based Business ProcessesIEEE Transactions on Services Computing10.1109/TSC.2018.284336614:3(779-790)Online publication date: 1-May-2021
https://doi.org/10.1109/TSC.2018.2843366
Ayimba CCasari PMancuso V(2021)SQLR: Short-Term Memory Q-Learning for Elastic ProvisioningIEEE Transactions on Network and Service Management10.1109/TNSM.2021.307561918:2(1850-1869)Online publication date: Jun-2021
https://doi.org/10.1109/TNSM.2021.3075619
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