Abstract
In large data centers, determining the right number of operating machines is often non-trivial, especially when the workload is unpredictable. Using too many machines would waste energy, while using too few would affect the performance. This paper extends the traditional study of online flow-time scheduling on multiple machines to take sleep management and energy into consideration. Specifically, we study online algorithms that can determine dynamically when and which subset of machines should wake up (or sleep), and how jobs are dispatched and scheduled. We consider schedules whose objective is to minimize the sum of flow time and energy, and obtain O(1)-competitive algorithms for two settings: one assumes machines running at a fixed speed, and the other allows dynamic speed scaling to further optimize energy usage.
Like the previous work on the tradeoff between flow time and energy, the analysis of our algorithms is based on potential functions. What is new here is that the online and offline algorithms would use different subsets of machines at different times, and we need a more general potential analysis that can consider different match-up of machines.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Albers, S.: Energy-efficient algorithms. CACM 53(5), 86–96 (2010)
Albers, S., Fujiwara, H.: Energy-efficient algorithms for flow time minimization. ACM Transactions on Algorithms 3(4), 49 (2007)
Andrew, L., Wierman, A., Tang, A.: Optimal speed scaling under arbitrary power functions. ACM SIGMETRICS Performance Evaluation Review 37(2), 39–41 (2009)
Avrahami, N., Azar, Y.: Minimizing total flow time and total completion time with immediate dispatching. In: Proc. SPAA, pp. 11–18 (2003)
Bansal, N., Chan, H.L., Pruhs, K.: Speed scaling with an arbitrary power function. In: Proc. SODA, pp. 693–701 (2009)
Chekuri, C., Goel, A., Khanna, S., Kumar, A.: Multi-processor scheduling to minimize flow time with ε resource augmentation. In: Proc. STOC, pp. 363–372 (2004)
Gandhi, A., Gupta, V., Harchol-Balter, M., Kozuch, M.: Optimality analysis of energy-performance trade-off for server farm management. Performance Evaluation 67(11), 1155–1171 (2010)
Gupta, A., Krishnaswamy, R., Pruhs, K.: Scalably scheduling power-heterogeneous processors. In: Abramsky, S., Gavoille, C., Kirchner, C., Meyer auf der Heide, F., Spirakis, P.G. (eds.) ICALP 2010. LNCS, vol. 6198, pp. 312–323. Springer, Heidelberg (2010)
Greiner, G., Nonner, T., Souza, A.: The bell is ringing in speed-scaled multiprocessor scheduling. In: Proc. SPAA, pp. 11–18 (2009)
Khuller, S., Li, J., Saha, B.: Energy efficient scheduling via partial shutdown. In: Proc. SODA, pp. 1360–1372 (2010)
Lam, T.W., Lee, L.K., Ting, H.F., To, I., Wong, P.: Sleep with guilt and work faster to minimize flow plus energy. In: Albers, S., Marchetti-Spaccamela, A., Matias, Y., Nikoletseas, S., Thomas, W. (eds.) ICALP 2009. LNCS, vol. 5555, pp. 665–676. Springer, Heidelberg (2009)
Lam, T.W., Lee, L.K., To, I., Wong, P.: Competitive non-migratory scheduling for flow time and energy. In: Proc. SPAA, pp. 256–264 (2008)
Lam, T.W., Lee, L.K., To, I., Wong, P.: Speed scaling functions for flow time scheduling based on active job count. In: Halperin, D., Mehlhorn, K. (eds.) Esa 2008. LNCS, vol. 5193, pp. 647–659. Springer, Heidelberg (2008)
Pruhs, K., Sgall, J., Torng, E.: Online scheduling. In: Handbook of Scheduling: Algorithms, Models and Performance Analysis, pp. 15-1–15-41. CRC Press, Boca Raton (2004)
U.S. Environmental Protection Agency. EPA Report on server and data center energy efficiency (2007)
Yao, F., Demers, A., Shenker, S.: A scheduling model for reduced CPU energy. In: Proc. FOCS, pp. 374–382 (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
Chan, SH., Lam, TW., Lee, LK., Liu, CM., Ting, HF. (2011). Sleep Management on Multiple Machines for Energy and Flow Time. In: Aceto, L., Henzinger, M., Sgall, J. (eds) Automata, Languages and Programming. ICALP 2011. Lecture Notes in Computer Science, vol 6755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22006-7_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-22006-7_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22005-0
Online ISBN: 978-3-642-22006-7
eBook Packages: Computer ScienceComputer Science (R0)