On the Joint Optimization of Performance and Power Consumption in Data Centers
We model the process of a data center as a multi- objective problem of mapping independent tasks onto a set of data center machines that simultaneously minimizes the energy consump¬tion and response time (makespan) subject to the constraints of deadlines and architectural requirements. A simple technique based on multi-objective goal programming is proposed that guarantees Pareto optimal solution with excellence in convergence process. The proposed technique also is compared with other traditional approach. The simulation results show that the proposed technique achieves superior performance compared to the min-min heuristics, and com¬petitive performance relative to the optimal solution implemented in UNDO for small-scale problems.
[1] 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.
[2] N. Bansal, T. Kimbrel, and K. Pruhs. Dynamic speed scaling to manage
energy and temperature. In 45th Annual IEEE Symposium on Foundations
of Computer Science, pp. 520–529, 2004.
[3] R. Bianchini and R. Rajamony. Power and energy management for server
systems. IEEE Computer, 37(11):68–74, 2004.
[4] D. P. Bunde. Power-aware scheduling for makespan and flow. In 8th ACM
Symposium on Parallelism in Algorithms and Architectures, pp. 190–196,
2006.
[5] J. Chen, M. Dubois, and P. Stenstrom. Simwattch: Integrating complete- ¨
system and user-level performance and power simulators. IEEE Micro,
27(4):34–48, 2007.
[6] J. S. Dyer. Interactive goal programming. Operations Research, 19:62–
70, 1972.
[7] 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.
[8] C. L. Hwang and A. S. M. Masud. Multiple Objective Decision Making—
Methods and Applications: A State-pf-the-Art Survey. Springer Verlag,
berlin, 1979.
[9] S. Irani, R. Gupta, and S. Shukla. Competitive analysis of dynamic power
management strategies for systems with multiple power savings states. In
Conference on Design, Automation and test in Europe, p. 117, 2002.
[10] L. Li and K. K. Lai. A fuzzy approach to the multiobjective transportation
problem. Computers and Operations Research, 27(1):43–57,
2000.
[11] T.-F. Liang. Fuzzy multi-objective production/distribution planning
decisions with multi-product and multi-time period in a supply chain.
Computers in Industrial Engineering, 55(3):676–694, 2008.
[12] J. R. Lorch and A. J. Smith. Improving dynamic voltage scaling algorithms
with pace. In 2001 ACM SIGMETRICS International Conference
on Measurement and Modeling of Computer Systems, pp. 50–61, 2001.
[13] D. Luenberger. Linear and Nonlinear Programming. Addison-Wesley,
1984.
[14] P. Mejia-Alvarez, E. Levner, and D. Moss´e. Adaptive scheduling
server for power-aware real-time tasks. IEEE Transactions on Embedded
Computing Systems, 3(2):284–306, 2004.
[15] 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.
[16] P. A. Laplante. Real-Time System Design and Analysis. John Wiley &
Sons, 2004.
[17] 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.
[18] 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.
[19] L. Schrage. Linear, Integer, and Quadratic Programming with LINDO.
Scientific Press, 1986.
[20] A. Stefanescu and M. Stefanescu. The arbitrated solution for multiobjective
convex programming. Revue Roumaine de Mathematical Pures
et Appliquees, 29:593–598, 1984.
[21] J. Wallenius. Comparative evaluation of some interactive approaches to
multicriterion optimization. Management Sciences, 21:1387–1396, 1975.
[22] 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.
[23] 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.
[24] M. Zangiabadi and H. R. Maleki. Fuzzy goal programming for
multiobjective transportation problems. Journal of Applied Mathematical
Computing, 24(1):449–460, 2007.
[1] 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.
[2] N. Bansal, T. Kimbrel, and K. Pruhs. Dynamic speed scaling to manage
energy and temperature. In 45th Annual IEEE Symposium on Foundations
of Computer Science, pp. 520–529, 2004.
[3] R. Bianchini and R. Rajamony. Power and energy management for server
systems. IEEE Computer, 37(11):68–74, 2004.
[4] D. P. Bunde. Power-aware scheduling for makespan and flow. In 8th ACM
Symposium on Parallelism in Algorithms and Architectures, pp. 190–196,
2006.
[5] J. Chen, M. Dubois, and P. Stenstrom. Simwattch: Integrating complete- ¨
system and user-level performance and power simulators. IEEE Micro,
27(4):34–48, 2007.
[6] J. S. Dyer. Interactive goal programming. Operations Research, 19:62–
70, 1972.
[7] 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.
[8] C. L. Hwang and A. S. M. Masud. Multiple Objective Decision Making—
Methods and Applications: A State-pf-the-Art Survey. Springer Verlag,
berlin, 1979.
[9] S. Irani, R. Gupta, and S. Shukla. Competitive analysis of dynamic power
management strategies for systems with multiple power savings states. In
Conference on Design, Automation and test in Europe, p. 117, 2002.
[10] L. Li and K. K. Lai. A fuzzy approach to the multiobjective transportation
problem. Computers and Operations Research, 27(1):43–57,
2000.
[11] T.-F. Liang. Fuzzy multi-objective production/distribution planning
decisions with multi-product and multi-time period in a supply chain.
Computers in Industrial Engineering, 55(3):676–694, 2008.
[12] J. R. Lorch and A. J. Smith. Improving dynamic voltage scaling algorithms
with pace. In 2001 ACM SIGMETRICS International Conference
on Measurement and Modeling of Computer Systems, pp. 50–61, 2001.
[13] D. Luenberger. Linear and Nonlinear Programming. Addison-Wesley,
1984.
[14] P. Mejia-Alvarez, E. Levner, and D. Moss´e. Adaptive scheduling
server for power-aware real-time tasks. IEEE Transactions on Embedded
Computing Systems, 3(2):284–306, 2004.
[15] 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.
[16] P. A. Laplante. Real-Time System Design and Analysis. John Wiley &
Sons, 2004.
[17] 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.
[18] 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.
[19] L. Schrage. Linear, Integer, and Quadratic Programming with LINDO.
Scientific Press, 1986.
[20] A. Stefanescu and M. Stefanescu. The arbitrated solution for multiobjective
convex programming. Revue Roumaine de Mathematical Pures
et Appliquees, 29:593–598, 1984.
[21] J. Wallenius. Comparative evaluation of some interactive approaches to
multicriterion optimization. Management Sciences, 21:1387–1396, 1975.
[22] 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.
[23] 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.
[24] M. Zangiabadi and H. R. Maleki. Fuzzy goal programming for
multiobjective transportation problems. Journal of Applied Mathematical
Computing, 24(1):449–460, 2007.
@article{"International Journal of Information, Control and Computer Sciences:65803", author = "Samee Ullah Khan and C. Ardil", title = "On the Joint Optimization of Performance and Power Consumption in Data Centers", abstract = "We model the process of a data center as a multi- objective problem of mapping independent tasks onto a set of data center machines that simultaneously minimizes the energy consump¬tion and response time (makespan) subject to the constraints of deadlines and architectural requirements. A simple technique based on multi-objective goal programming is proposed that guarantees Pareto optimal solution with excellence in convergence process. The proposed technique also is compared with other traditional approach. The simulation results show that the proposed technique achieves superior performance compared to the min-min heuristics, and com¬petitive performance relative to the optimal solution implemented in UNDO for small-scale problems.
", keywords = "Energy-efficient computing, distributed systems, multi-objective optimization.", volume = "6", number = "6", pages = "853-7", }
