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Decentralized and optimal control of shared resource pools

Published:04 May 2012Publication History
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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. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Babaoglu, O. and Jelasity, M. 2008. Self-* properties through gossiping. Philos. Trans. Roy. Soc. 366, 3747--3757.Google ScholarGoogle ScholarCross RefCross Ref
  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. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Byde, A., Sallé, M., and Bartolini, C. 2003. Market-Based resource allocation for utility data centers. Tech. rep., Hewlett-Packard. September.Google ScholarGoogle Scholar
  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. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital 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.Google ScholarGoogle Scholar
  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. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital 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.Google ScholarGoogle Scholar
  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. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Kephart, J. O. and Chess, D. M. 2003. The vision of autonomic computing. Comput. 36, 1, 41--50. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Kephart, J. O. and Das, R. 2007. Achieving self-management via utility functions. IEEE Internet Comput. 11, 1, 40--48. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Kermarrec, A. M. and Van Steen, M. 2007. Gossiping in distributed systems. Oper. Syst. Rev. 41, 5, 2--7. Google ScholarGoogle ScholarDigital LibraryDigital 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.Google ScholarGoogle Scholar
  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. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Maheswaran, R. and Başar, T. 2003. Nash equilibrium and decentralized negotiation in auctioning divisible resources. Group Decis. Negot. 12, 5, 361--395.Google ScholarGoogle ScholarCross RefCross Ref
  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.Google ScholarGoogle Scholar
  27. Nedic, A. and Ozdaglar, A. 2009. Distributed subgradient methods for multi-agent optimization. IEEE Trans. Autom. Control 54, 1, 48--61.Google ScholarGoogle ScholarCross RefCross Ref
  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. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital 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.Google ScholarGoogle Scholar
  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. Google ScholarGoogle ScholarDigital LibraryDigital 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.Google ScholarGoogle ScholarCross RefCross Ref
  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. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital Library

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            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

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 4 May 2012
            • Accepted: 1 June 2011
            • Revised: 1 December 2010
            • Received: 1 January 2010
            Published in taas Volume 7, Issue 1

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