A Weighted Sum Technique for the Joint Optimization of Performance and Power Consumption in Data Centers

Authors: Samee Ullah Khan, C.Ardil

Abstract:

With data centers, end-users can realize the pervasiveness of services that will be one day the cornerstone of our lives. However, data centers are often classified as computing systems that consume the most amounts of power. To circumvent such a problem, we propose a self-adaptive weighted sum methodology that jointly optimizes the performance and power consumption of any given data center. Compared to traditional methodologies for multi-objective optimization problems, the proposed self-adaptive weighted sum technique does not rely on a systematical change of weights during the optimization procedure. The proposed technique is compared with the greedy and LR heuristics for large-scale problems, and the optimal solution for small-scale problems implemented in LINDO. the experimental results revealed that the proposed selfadaptive weighted sum technique outperforms both of the heuristics and projects a competitive performance compared to the optimal solution.

Keywords: Meta-heuristics, distributed systems, adaptive methods, resource allocation.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1057673

References:

[1] United States Air Force Satellite Control Network Data, available on-line at: http://www.cs.colostate.edu/sched/ index.html.
[2] T. F. Abdelzaher and C. Lu. Schedulability analysis and utilization bounds for highly scalable real-time services. In 7th Real-Time Technology and Applications Symposium, p. 15, 2001.
[3] R. Bianchini and R. Rajamony. Power and energy management for server systems. IEEE Computer, 37(11):68-74, 2004.
[4] J. Chen, M. Dubois, and P. Stenstr¨om. Simwattch: Integrating complete-system and user-level performance and power simulators. IEEE Micro, 27(4):34-48, 2007.
[5] E.-Y. Chung, L. Benini, A. Bogiolo, and G. De Micheli. Dynamic power management for non-stationary service requests. In Conference on Design, Automation and Test in Europe, p. 18, 1999.
[6] T. Heath, B. Diniz, E. V. Carrera, W. M. Jr., and R. Bianchini. Energy conservation in heterogeneous server clusters. In 10th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, pp. 186-195, 2005.
[7] C. L. Hwang and A. S. M. Masud. Multiple Objective Decision MakingÔÇöMethods and Applications: A State-pfthe- Art Survey. Springer Verlag, Berlin, 1979.
[8] I. Y. Kim and O. L. de Weck. Adaptive weightedsum method for bi-objective optimization: Pareto front generation. Structural and Multidisciplinary Optimization, 29:149-158, 2005.
[9] J. Lin. Multiple objective problems: Pareto-optimal solutions by methods of proper equality constraints. IEEE Transactions on Automatic Control, 21:641-650, 1976.
[10] R. Nathuji, C. Isci, and E. Gorbatov. Exploiting platform heterogeneity for power efficient data centers. In 4th International Conference on Autonomic Computing, p. 5, 2007.
[11] E. Pinheiro, R. Bianchini, E. V. Carrera, and T. Heath. Load balancing and unbalancing for power and performance in cluster-based systems. In Workshop on Compilers and Operating Systems for Low Power, 2001.
[12] C. Rusu, A. Ferreira, C. Scordino, and A. Watson. Energy-efficient real-time heterogeneous server clusters. In 12th IEEE Real-Time and Embedded Technology and Applications Symposium, pp. 418-428, 2006.
[13] L. Schrage. Linear, Integer, and Quadratic Programming with LINDO. Scientific Press, 1986.
[14] M. Weiser, B. Welch, A. Demers, and S. Shenker. Scheduling for reduced CPU energy. In 1st USENIX conference on Operating Systems Design and Implementation, p. 2, 1994.
[15] Y. Yu and V. K. Prasanna. Power-aware resource allocation for independent tasks in heterogeneous real-time systems. In 9th International Conference on Parallel and Distributed Systems, p. 341, 2002.