research-article

Decentralized and optimal control of shared resource pools

Authors:

Emerson Loureiro,

Simon DobsonAuthors Info & Claims

ACM Transactions on Autonomous and Adaptive Systems (TAAS), Volume 7, Issue 1

Article No.: 14, Pages 1 - 31

https://doi.org/10.1145/2168260.2168274

Published: 04 May 2012 Publication History

Abstract

Resource pools are collections of computational resources (e.g., servers) which can be used by different applications in a shared way. A crucial aspect in these pools is to allocate resources so as to ensure their proper usage, taking into account workload and specific requirements of each application. An interesting approach, in this context, is to allocate the resources in the best possible way, aiming at optimal resource usage. Workload, however, varies over time, and in turn, resource demands will vary too. To ensure that optimal resource usage is always in place, resource shares should be defined dynamically and over time. It has been claimed that utility functions are the main tool for enabling such self-optimizing behavior. Whereas many solutions with this characteristic have been proposed to date, none of them presents true decentralization within the context of shared pools. In this article, we then propose a decentralized model for optimal resource usage in shared resource pools, providing practical and theoretical evidence of its feasibility.

References

[1]

Aguilar, J., Cerrada, M., and Hidrobo, F. 2007. A methodology to specify multiagent systems. In Proceedings of the 1st KES International Symposium on Agent and Multi-Agent Systems. Springer, 92--101.

Digital Library

[2]

Aron, M., Druschel, P., and Zwaenepoel, W. 2000. Cluster reserves: a mechanism for resource management in cluster-based network servers. In Proceedings of the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems. Vol. 28. ACM Press, New York, 90--101.

Digital Library

[3]

Babaoglu, O. and Jelasity, M. 2008. Self-* properties through gossiping. Philos. Trans. Roy. Soc. 366, 3747--3757.

[4]

Bai, X., Marinescu, D. C., Bölöni, L., Siegel, H. J., Daley, R. A., and Wang, I. J. 2008. A macroeconomic model for resource allocation in large-scale distributed systems. J. Parallel Distrib. Comput. 68, 2, 182--199.

Digital Library

[5]

Banga, G., Druschel, P., and Mogul, J. C. 1999. Resource containers: A new facility for resource management in server systems. In Proceedings of the 3rd Symposium on Operating Systems Design and Implementation. USENIX Association, 45--58.

Digital Library

[6]

Batouma, N. and Sourrouille, J.-L. 2010. Decentralized resource management using a borrowing schema. In ACS/IEEE International Conference on Computer Systems and Applications.

Digital Library

[7]

Bennani, M. N. and Menascé, D. A. 2005. Resource allocation for autonomic data centers using analytic performance models. In Proceedings of the 2nd International Conference on Autonomic Computing. IEEE Computer Society, Washington, DC, 229--240.

Digital Library

[8]

Boutilier, C., Das, R., Kephart, J., Tesauro, G., and Walsh, W. 2003. Cooperative netotiation in autonomic systems using incremental utility elicitation. In Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence. 89--97.

Digital Library

[9]

Byde, A., Sallé, M., and Bartolini, C. 2003. Market-Based resource allocation for utility data centers. Tech. rep., Hewlett-Packard. September.

[10]

Chechetka, A. and Sycara, K. 2006. No-commitment branch and bound search for distributed constraint optimization. In Proceedings of the 5th International Joint Conference on Autonomous Agents and Multiagent Systems. ACM, New York, 1427--1429.

Digital Library

[11]

Chen, M., Ponec, M., Sengupta, S., Li, J., and Chou, P. A. 2008. Utility maximization in peer-to-peer systems. In Proceedings of the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems. ACM, New York, 169--180.

Digital Library

[12]

Demers, A., Greene, D., Hauser, C., Irish, W., Larson, J., Shenker, S., Sturgis, H., Swinehart, D., and Terry, D. 1987. Epidemic algorithms for replicated database maintenance. In Proceedings of the 6th Annual ACM Symposium on Principles of Distributed Computing. ACM Press, New York, 1--12.

Digital Library

[13]

Gmach, D., Rolia, J., Cherkasova, L., Belrose, G., Turicchi, T., and Kemper, A. 2008. An integrated approach to resource pool management: Policies, efficiency and quality metrics. In Proceedings of the 3rd Symposium on Operating Systems Design and Implementation (OSDI '99). Proceedings of the IEEE International Conference on Dependable Systems and Networks. 326--335.

[14]

Guitart, J., Carrera, D., Beltran, V., Torres, J., and Ayguadé, E. 2008. Dynamic CPU provisioning for self-managed secure web applications in smp hosting platforms. Comput. Netw. 52, 7, 1390--1409.

Digital Library

[15]

Jelasity, M., Montresor, A., and Babaoglu, O. 2005. Gossip-based aggregation in large dynamic networks. ACM Trans. Comput. Syst. 23, 3, 219--252.

Digital Library

[16]

Johansson, B., Adam, C., Johansson, M., and Stadler, R. 2006. Distributed resource allocation strategies for achieving quality of service in server clusters. In Proceedings of the 45th Conference on Decision and Control. IEEE Computer Society, 1990--1995.

[17]

Kempe, D., Dobra, A., and Gehrke, J. 2003. Gossip-based computation of aggregate information. In Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science. IEEE Computer Society, Washington, DC.

Digital Library

[18]

Kephart, J. O. and Chess, D. M. 2003. The vision of autonomic computing. Comput. 36, 1, 41--50.

Digital Library

[19]

Kephart, J. O. and Das, R. 2007. Achieving self-management via utility functions. IEEE Internet Comput. 11, 1, 40--48.

Digital Library

[20]

Kermarrec, A. M. and Van Steen, M. 2007. Gossiping in distributed systems. Oper. Syst. Rev. 41, 5, 2--7.

Digital Library

[21]

Lewis, P. R., Marrow, P., and Yao, X. 2008. Evolutionary market agents for resource allocation in decentralised systems. In Proceedings of the 10th International Conference on Parallel Problem Solving from Nature. Springer, 1071--1080.

[22]

Loureiro, E., Nixon, P., and Dobson, S. 2008. A fine-grained model for adaptive on-demand provisioning of CPU shares in data centers. In Proceedings of the 3rd International Workshop on Self-Organizing Systems. Springer, 57--108.

Digital Library

[23]

Loureiro, E., Nixon, P., and Dobson, S. 2009. Decentralized utility maximization for adaptive management of shared resource pools. In International Conference on Intelligent Networking and Collaborative Systems.

Digital Library

[24]

Loureiro, E., Nixon, P., and Dobson, S. 2010. Adaptive management of shared resource pools with decentralized optimization and epidemics. In Proceedings of the 18th Euromicro Conference on Parallel, Distributed and Network-Based Processing. IEEE Computer Society, Washington, DC, 51--58.

Digital Library

[25]

Maheswaran, R. and Başar, T. 2003. Nash equilibrium and decentralized negotiation in auctioning divisible resources. Group Decis. Negot. 12, 5, 361--395.

[26]

Masuishi, T., Kuriyama, H., Ooki, Y., and Mori, K. 2005. Autonomous decentralized resource allocation for tracking dynamic load change. In Proceedings of the International Symposium on Autonomous Decentralized Systems. IEEE Computer Society, 277--283.

[27]

Nedic, A. and Ozdaglar, A. 2009. Distributed subgradient methods for multi-agent optimization. IEEE Trans. Autom. Control 54, 1, 48--61.

[28]

Nowicki, T., Squillante, M. S., and Wu, C. W. 2005. Fundamentals of dynamic decentralized optimization in autonomic computing systems. In Self-Star Properties in Complex Information Systems. Springer, 204--218.

Digital Library

[29]

Padala, P., Shin, K. G., Zhu, X., Uysal, M., Wang, Z., Singhal, S., Merchant, A., and Salem, K. 2007. Adaptive control of virtualized resources in utility computing environments. In Proceedings of the European Conference on Computer Systems. ACM Press, New York, 289--302.

Digital Library

[30]

Padgham, L. and Winikoff, M. 2002. Prometheus: a methodology for developing intelligent agents. In Proceedings of the 1st International Joint Conference on Autonomous Agents and Multiagent Systems. ACM, New York, 37--38.

Digital Library

[31]

Palomar, D. P. and Chiang, M. 2006. A tutorial on decomposition methods for network utility maximization. IEEE J. Selected Areas Comm. 24, 8, 1439--1451.

Digital Library

[32]

Paton, N. W., de Aragão, M. A. T., Lee, K., Fernandes, A. A. A., and Sakellariou, R. 2009. Optimizing utility in cloud computing through autonomic workload execution. Bull. Tech. Committee Data Engin. 32, 1, 51--58.

[33]

Petcu, A. and Faltings, B. 2005. A scalable method for multiagent constraint optimization. In Proceedings of the 19th International Joint Conference on Artificial Intelligence. Morgan Kaufmann, San Francisco, CA, 266--271.

Digital Library

[34]

Piovesan, J. L., Abdallah, C. T., and Tanner, H. G. 2008. A hybrid framework for resource allocation among multiple agents moving on discrete environments. Asian J. Control 10, 2, 171--186.

[35]

Raghavan, B., Vishwanath, K., Ramabhadran, S., Yocum, K., and Snoeren, A. C. 2007. Cloud control with distributed rate limiting. Comput. Comm. Rev. 37, 4, 337--348.

Digital Library

[36]

Rolia, J., Cherkasova, L., Arlitt, M., and Machiraju, V. 2006. Supporting application quality of service in shared resource pools. Comm. ACM 49, 3, 55--60.

Digital Library

[37]

Samaan, N. 2008. Achieving self-management in a distributed system of autonomic but social entities. In Proceedings of the 3rd IEEE International Workshop on Modelling Autonomic Communications Environments. Springer, 90--101.

Digital Library

[38]

Tesauro, G. and Kephart, J. O. 2004. Utility functions in autonomic systems. In Proceedings of the 1st International Conference on Autonomic Computing. IEEE Computer Society, Washington, DC, 70--77.

Digital Library

[39]

Tesauro, G., Walsh, W. E., and Kephart, J. O. 2005. Utility-function-driven resource allocation in autonomic systems. In Proceedings of the 2nd International Conference on Autonomic Computing. IEEE Computer Society, Washington, DC, 342--343.

Digital Library

[40]

Urgaonkar, B., Shenoy, P., Chandra, A., Goyal, P., and Wood, T. 2008. Agile dynamic provisioning of multi-tier internet applications. ACM Trans. Auton. Adap. Syst. 3, 1, 1--39.

Digital Library

[41]

Wang, X., Du, Z., Chen, Y., and Li, S. 2008. Virtualization-based autonomic resource management for multi-tier web applications in shared data center. J. Syst. Softw. 81, 9, 1591--1608.

Digital Library

[42]

Wooldridge, M., Jennings, N. R., and Kinny, D. 2000. The gaia methodology for agent-oriented analysis and design. Auton. Agents Multi-Agent Syst. 3, 3, 285--312.

Digital Library

[43]

Xu, J., Zhao, M., Fortes, J., Carpenter, R., and Yousif, M. 2008. Autonomic resource management in virtualized data centers using fuzzy logic-based approaches. Cluster Comput. 11, 3, 213--227.

Digital Library

Cited By

Zhang DZhang SWang ZLu B(2019)Dynamic Control Allocation Algorithm for a Class of Distributed Control SystemsInternational Journal of Control, Automation and Systems10.1007/s12555-017-9768-zOnline publication date: 23-Sep-2019
https://doi.org/10.1007/s12555-017-9768-z
Melekhova OMalenfant JMescheriakov RChueshev A(2018)A decentralised solution for coordinating decisions in large-scale autonomic systemsMATEC Web of Conferences10.1051/matecconf/201816103024161(03024)Online publication date: 18-Apr-2018
https://doi.org/10.1051/matecconf/201816103024
Melekhova OMalenfant J(2017)A Token-Based Scheme for Coordinating Decisions in Large-Scale Autonomic Systems2017 IEEE 26th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)10.1109/WETICE.2017.11(60-65)Online publication date: Jun-2017
https://doi.org/10.1109/WETICE.2017.11
Show More Cited By

Index Terms

Decentralized and optimal control of shared resource pools

Recommendations

Adaptive Management of Shared Resource Pools with Decentralized Optimization and Epidemics
PDP '10: Proceedings of the 2010 18th Euromicro Conference on Parallel, Distributed and Network-based Processing

Shared resource pools are facilities featuring a certain amount of resources which can be used by different applications. For managing resources in such pools, the demand of each application can be used. Such a demand, however, is driven by the workload,...
Decentralized Utility Maximization for Adaptive Management of Shared Resource Pools
INCOS '09: Proceedings of the 2009 International Conference on Intelligent Networking and Collaborative Systems

Resource pools are collections of computational resources which can be shared by different applications. The goal with that is to accommodate the workload of each application, by splitting the total amount of resources in the pool among them. In this ...
Self-adaptive resource management for large-scale shared clusters

In a shared cluster, each application runs on a subset of nodes and these subsets can overlap with one another. Resource management in such a cluster should adaptively change the application placement and workload assignment to satisfy the dynamic ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Autonomous and Adaptive Systems

ACM Transactions on Autonomous and Adaptive Systems Volume 7, Issue 1

Special section on formal methods in pervasive computing, pervasive adaptation, and self-adaptive systems: Models and algorithms

April 2012

365 pages

ISSN:1556-4665

EISSN:1556-4703

DOI:10.1145/2168260

Issue’s Table of Contents

Copyright © 2012 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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 May 2012

Accepted: 01 June 2011

Revised: 01 December 2010

Received: 01 January 2010

Published in TAAS Volume 7, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

University College Dublin

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
372
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)0

Reflects downloads up to 18 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zhang DZhang SWang ZLu B(2019)Dynamic Control Allocation Algorithm for a Class of Distributed Control SystemsInternational Journal of Control, Automation and Systems10.1007/s12555-017-9768-zOnline publication date: 23-Sep-2019
https://doi.org/10.1007/s12555-017-9768-z
Melekhova OMalenfant JMescheriakov RChueshev A(2018)A decentralised solution for coordinating decisions in large-scale autonomic systemsMATEC Web of Conferences10.1051/matecconf/201816103024161(03024)Online publication date: 18-Apr-2018
https://doi.org/10.1051/matecconf/201816103024
Melekhova OMalenfant J(2017)A Token-Based Scheme for Coordinating Decisions in Large-Scale Autonomic Systems2017 IEEE 26th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)10.1109/WETICE.2017.11(60-65)Online publication date: Jun-2017
https://doi.org/10.1109/WETICE.2017.11
Mencagli GVanneschi MVespa E(2014)A Cooperative Predictive Control Approach to Improve the Reconfiguration Stability of Adaptive Distributed Parallel ApplicationsACM Transactions on Autonomous and Adaptive Systems (TAAS)10.1145/25679299:1(1-27)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1145/2567929
Chen Song (2014)Decentralized optimal control in shared resource pools of video service nodesProceeding of the 11th World Congress on Intelligent Control and Automation10.1109/WCICA.2014.7053354(3820-3825)Online publication date: Jun-2014
https://doi.org/10.1109/WCICA.2014.7053354
Song CZhang ZXi H(2014)Decentralized optimal control in shared resource pools of video service nodes in network convergenceProceedings of the 33rd Chinese Control Conference10.1109/ChiCC.2014.6896859(1551-1556)Online publication date: Jul-2014
https://doi.org/10.1109/ChiCC.2014.6896859

View Options

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 Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents