Abstract
We consider the speed scaling problem where the quality of service objective is deadline feasibility and the power objective is temperature. In the case of batched jobs, we give a simple algorithm to compute the optimal schedule. For general instances, we give a new online algorithm, and obtain an upper bound on the competitive ratio of this algorithm that is an order of magnitude better than the best previously known bound upper bound on the competitive ratio for this problem.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Albers, S.: Algorithms for energy saving. In: Albers, S., Alt, H., Näher, S. (eds.) Efficient Algorithms. LNCS, vol. 5760, pp. 173–186. Springer, Heidelberg (2009)
Albers, S.: Energy-efficient algorithms. Commun. ACM 53(5), 86–96 (2010)
Bansal, N., Bunde, D.P., Chan, H.L., Pruhs, K.: Average rate speed scaling. In: Laber, E.S., Bornstein, C., Nogueira, L.T., Faria, L. (eds.) LATIN 2008. LNCS, vol. 4957, pp. 240–251. Springer, Heidelberg (2008)
Bansal, N., Chan, H.L., Pruhs, K., Katz, D.: Improved bounds for speed scaling in devices obeying the cube-root rule. In: Albers, S., Marchetti-Spaccamela, A., Matias, Y., Nikoletseas, S., Thomas, W. (eds.) ICALP 2009. LNCS, vol. 5555, pp. 144–155. Springer, Heidelberg (2009)
Bansal, N., Kimbrel, T., Pruhs, K.: Speed scaling to manage energy and temperature. J. ACM 54(1), 1–39 (2007)
Brooks, D.M., Bose, P., Schuster, S.E., Jacobson, H., Kudva, P.N., Buyuktosunoglu, A., Wellman, J.D., Zyuban, V., Gupta, M., Cook, P.W.: Power-aware microarchitecture: Design and modeling challenges for next-generation microprocessors. IEEE Micro 20(6), 26–44 (2000)
Chrobak, M., Dürr, C., Hurand, M., Robert, J.: Algorithms for temperature-aware task scheduling in microprocessor systems. In: Fleischer, R., Xu, J. (eds.) AAIM 2008. LNCS, vol. 5034, pp. 120–130. Springer, Heidelberg (2008)
Irani, S., Pruhs, K.R.: Algorithmic problems in power management. SIGACT News 36(2), 63–76 (2005)
Li, M., Yao, A.C., Yao, F.F.: Discrete and continuous min-energy schedules for variable voltage processors. Proceedings of the National Academy of Sciences of the United States of America 103(11), 3983–3987 (2006)
Rao, R., Vrudhula, S.: Performance optimal processor throttling under thermal constraints. In: Proceedings of the 2007 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems, CASES 2007, pp. 257–266. ACM, New York (2007)
Snowdon, D.C., Ruocco, S., Heiser, G.: Power management and dynamic voltage scaling: Myths and facts. In: Proceedings of the 2005 Workshop on Power Aware Real-time Computing, New Jersey, USA (September 2005)
Yao, F., Demers, A., Shenker, S.: A scheduling model for reduced cpu energy. In: FOCS 1995: Proceedings of the 36th Annual Symposium on Foundations of Computer Science, p. 374. IEEE Computer Society Press, Washington, DC (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Atkins, L., Aupy, G., Cole, D., Pruhs, K. (2011). Speed Scaling to Manage Temperature. In: Marchetti-Spaccamela, A., Segal, M. (eds) Theory and Practice of Algorithms in (Computer) Systems. TAPAS 2011. Lecture Notes in Computer Science, vol 6595. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19754-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-19754-3_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19753-6
Online ISBN: 978-3-642-19754-3
eBook Packages: Computer ScienceComputer Science (R0)