Abstract
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 sense, utility functions have been pointed as the main tool for enabling self-optimizing behaviour in such pools. The goal with that is to allow resources from the pool to be split among applications, in a way that the best outcome is obtained. Whereas different solutions in this context exist, it has been found that none of them tackles the problem we deal with in a total decentralized way. In this paper, we then present a decentralized and self-optimizing approach for resource management in shared resource pools.
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References
Rolia, J., Cherkasova, L., Arlitt, M., Machiraju, V.: Supporting application quality of service in shared resource pools. Communications of the ACM 49(3), 55–60 (2006)
Banga, G., Druschel, P., Mogul, J.C.: Resource containers: A new facility for resource management in server systems. In: Proceedings of the Third Symposium on Operating Systems Design and Implementation. USENIX Association, pp. 45–58 (1999)
Wang, X., Du, Z., Chen, Y., Li, S.: Virtualization-based autonomic resource management for multi-tier web applications in shared data center. Journal of Systems and Software 81(9), 1591–1608 (2008)
Guitart, J., Carrera, D., Beltran, V., Torres, J., Ayguade, E.: Dynamic CPU provisioning for self-managed secure web applications in smp hosting platforms. Computer Networks 52(7), 1390–1409 (2008)
Padala, P., Shin, K.G., Zhu, X., Uysal, M., Wang, Z., Singhal, S., Merchant, A., Salem, K.: Adaptive control of virtualized resources in utility computing environments. In: Proceedings of the 2007 European Conference on Computer Systems, pp. 289–302. ACM Press, New York (2007)
Kephart, J.O., Das, R.: Achieving self-management via utility functions. IEEE Internet Computing 11(1), 40–48 (2007)
Kephart, J.O., Chess, D.M.: The vision of autonomic computing. Computer 36(1), 41–50 (2003)
Tesauro, G., Kephart, J.O.: Utility functions in autonomic systems. In: Proceedings of the First International Conference on Autonomic Computing, pp. 70–77. IEEE Computer Society, Washington (2004)
Bennani, M.N., Menasce, D.A.: Resource allocation for autonomic data centers using analytic performance models. In: Proceedings of the Second International Conference on Autonomic Computing, pp. 229–240. IEEE Computer Society, Washington (2005)
Tesauro, G., Walsh, W.E., Kephart, J.O.: Utility-function-driven resource allocation in autonomic systems. In: Proceedings of the Second International Conference on Autonomic Computing, pp. 342–343. IEEE Computer Society, Washington (2005)
Johansson, B., Adam, C., Johansson, M., Stadler, R.: Distributed resource allocation strategies for achieving quality of service in server clusters. In: Proceedings of the 45th Conference on Decision and Control, pp. 1990–1995. IEEE Computer Society, Washington (2006)
Bai, X., Marinescu, D.C., Boloni, L., Siegel, H.J., Daley, R.A., Wang, I.J.: A macroeconomic model for resource allocation in large-scale distributed systems. Journal of Parallel Distributed Computing 68(2), 182–199 (2008)
Kermarrec, A.M., Van Steen, M.: Gossiping in distributed systems. SIGOPS Oper. Syst. Rev. 41(5), 2–7 (2007)
Jelasity, M., Kowalczyk, W., van Steen, M.: An approach to massively distributed aggregate computing on peer-to-peer networks. In: Proceedings 12th Euromicro Conference on Parallel, Distributed and Network-Based Processing, pp. 200–207. IEEE Computer Society, Washington (2004)
Kempe, D., Dobra, A., Gehrke, J.: Gossip-based computation of aggregate information. In: Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science. IEEE Computer Society, Washington (2003)
Palomar, D.P., Chiang, M.: A tutorial on decomposition methods for network utility maximization. IEEE Journal on Selected Areas in Communications 24(8), 1439–1451 (2006)
Nowicki, T., Squillante, M.S., Wu, C.W.: Fundamentals of dynamic decentralized optimization in autonomic computing systems. In: Babaoğlu, Ö., Jelasity, M., Montresor, A., Fetzer, C., Leonardi, S., van Moorsel, A., van Steen, M. (eds.) SELF-STAR 2004. LNCS, vol. 3460, pp. 204–218. Springer, Heidelberg (2005)
Lewis, P.R., Marrow, P., Yao, X.: Evolutionary market agents for resource allocation in decentralised systems. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 1071–1080. Springer, Heidelberg (2008)
Maheswaran, R., Basar, T.: Nash equilibrium and decentralized negotiation in auctioning divisible resources. Group Decision and Negotiation 12(5), 361–395 (2003)
Raghavan, B., Vishwanath, K., Ramabhadran, S., Yocum, K., Snoeren, A.C.: Cloud control with distributed rate limiting. SIGCOMM Comput. Commun. Rev. 37(4), 337–348 (2007)
Masuishi, T., Kuriyama, H., Ooki, Y., Mori, K.: Autonomous decentralized resource allocation for tracking dynamic load change. In: Proceedings of the 7th International Symposium on Autonomous Decentralized Systems, pp. 277–283 (2005)
Nedic, A., Ozdaglar, A.: Distributed Subgradient methods for multi-agent optimization. IEEE Transactions on Automatic Control 54(1), 48–61 (2009)
Chen, M., Ponec, M., Sengupta, S., Li, J., Chou, P.A.: Utility maximization in peer-to-peer systems. In: Proceedings of the 2008 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, pp. 169–180. ACM, New York (2008)
Kuhn, M.: The Karush-Kuhn-Tucker theorem, http://webrum.uni-mannheim.de/vwl/mokuhn/public/KarushKuhnTucker.pdf
Weisstein, E.W.: Concave function, http://mathworld.wolfram.com/ConcaveFunction.html
Weisstein, E.W.: Convex function, http://mathworld.wolfram.com/ConvexFunction.html
Gallini, A.: Affine function, http://mathworld.wolfram.com/A_neFunction.html
Aron, M., Druschel, P., Zwaenepoel, W.: Cluster reserves: a mechanism for resource management in cluster-based network servers. In: Proceedings of the 2000 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, pp. 90–101. ACM Press, New York (2000)
Loureiro, E., Nixon, P., Dobson, S.: A fine-grained model for adaptive on-demand provisioning of CPU shares in data centers. In: Hummel, K.A., Sterbenz, J.P.G. (eds.) IWSOS 2008. LNCS, vol. 5343, pp. 57–108. Springer, Heidelberg (2008)
Lysne, O., Reinemo, S.A., Skeie, T., Solheim, A.G., Sodring, T., Huse, L.P., Johnsen, B.D.: Interconnection networks: Architectural chalenges for utility computing data centers. Computer 41(9), 62–69 (2008)
Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., Zaharia, M.: Above the clouds: A Berkeley view of cloud computing. Technical report, University of California at Berkeley (2009)
Dikaiakos, M.D., Katsaros, D., Mehra, P., Pallis, G., Vakali, A.: Cloud computing: Distributed internet computing for it and scientific research. IEEE Internet Computing 13, 10–13 (2009)
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Loureiro, E., Nixon, P., Dobson, S. (2010). Decentralized Self-optimization in Shared Resource Pools. In: Caballé, S., Xhafa, F., Abraham, A. (eds) Intelligent Networking, Collaborative Systems and Applications. Studies in Computational Intelligence, vol 329. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16793-5_7
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DOI: https://doi.org/10.1007/978-3-642-16793-5_7
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