Abstract
The evolutions of digital technologies and software applications have introduced a new computational paradigm that involves initially the creation of a large pool of jobs followed by a phase in which all the jobs are executed in systems with limited capacity. For example, a number of libraries have started digitizing their old books, or video content providers, such as YouTube or Netflix, need to transcode their contents to improve playback performances. Such applications are characterized by a huge number of jobs with different requests of computational resources, like CPU and GPU. Due to the very long computation time required by the execution of all the jobs, strategies to reduce the total energy consumption are very important.
In this work we present an analytical study of such systems, referred to as pool depletion systems, aimed at showing that very simple configuration parameters may have a non-trivial impact on the performance and especially on the energy consumption. We apply results from queueing theory coupled with the absorption time analysis for the depletion phase. We show that different optimal settings can be found depending on the considered metric.
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- 1.
Providing evidence about which is the best metric between ERP and ERWS is out of the purposes of this paper.
References
Albers, S., Fujiwara, H.: Energy-efficient algorithms for flow time minimization. ACM Trans. Algorithms (TALG) 3(4), 49 (2007)
Andrew, L.L., Lin, M., Wierman, A.: Optimality, fairness, and robustness in speed scaling designs. In: ACM SIGMETRICS Performance Evaluation Review, vol. 38, pp. 37–48. ACM (2010)
Bansal, N., Chan, H.L., Pruhs, K.: Speed scaling with an arbitrary power function. In: Proceedings of the Twentieth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 693–701. Society for Industrial and Applied Mathematics (2009)
Cerotti, D., Gribaudo, M., Piazzolla, P., Pinciroli, R., Serazzi, G.: Multi-class queuing networks models for energy optimization. In: Proceedings of the 8th International Conference on Performance Evaluation Methodologies and Tools, VALUETOOLS 2014, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), ICST, Brussels, Belgium, pp. 98–105 (2014). http://dx.org/10.4108/icst.Valuetools.2014.258214
Chen, D., Goldberg, G., Kahn, R., Kat, R., Meth, K.: Leveraging disk drive acoustic modes for power management. In: 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), pp. 1–9, May 2010
Diaz-Sanchez, D., Marin-Lopez, A., Almenarez, F., Sanchez-Guerrero, R., Arias, P.: A distributed transcoding system for mobile video delivery. In: Wireless and Mobile Networking Conference (WMNC), 2012 5th Joint IFIP, pp. 10–16, September 2012
Fan, X., Weber, W.D., Barroso, L.A.: Power provisioning for a warehouse-sized computer. In: Proceedings of the 34th Annual International Symposium on Computer Architecture, ISCA 2007, pp. 13–23. ACM, New York (2007). http://doi.acm.org/10.1145/1250662.1250665
Gandhi, A., Gupta, V., Harchol-Balter, M., Kozuch, M.A.: Optimality analysis of energy-performance trade-off for server farm management. Perform. Eval. 67(11), 1155–1171 (2010)
Gonzalez, R., Horowitz, M.: Energy dissipation in general purpose microprocessors. IEEE J. Solid-State Circuits 31(9), 1277–1284 (1996)
Hyytiä, E., Righter, R., Aalto, S.: Task assignment in a heterogeneous server farm with switching delays and general energy-aware cost structure. Perform. Eval. 75, 17–35 (2014)
Kang, C.W., Abbaspour, S., Pedram, M.: Buffer sizing for minimum energy-delay product by using an approximating polynomial. In: Proceedings of the 13th ACM Great Lakes Symposium on VLSI, pp. 112–115. ACM (2003)
Kant, K.: A control scheme for batching dram requests to improve power efficiency. In: Proceedings of the ACM SIGMETRICS Joint International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2011, pp. 139–140. ACM (2011)
Kaxiras, S., Martonosi, M.: Computer architecture techniques for power-efficiency. Synth. Lect. Comput. Archit. 3(1), 1–207 (2008)
Muppala, J., Malhotra, M., Trivedi, K.: Markov dependability models of complex systems: analysis techniques. In: Ozekici, S. (ed.) Reliability and Maintenance of Complex Systems, vol. 154, pp. 442–486. Springer, Heidelberg (1996). http://dx.doi.org/10.1007/978-3-662-03274-9_24
Rivoire, S., Ranganathan, P., Kozyrakis, C.: A comparison of high-level full-system power models. HotPower 8, 3 (2008)
Rosti, E., Schiavoni, F., Serazzi, G.: Queueing network models with two classes of customers. In: Proceedings of the Fifth International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, MASCOTS 1997, pp. 229–234. IEEE (1997)
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This work was partially funded by the European Commission under the grant ANTAREX H2020 FET-HPC-671623.
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Cerotti, D., Gribaudo, M., Pinciroli, R., Serazzi, G. (2016). Stochastic Analysis of Energy Consumption in Pool Depletion Systems. In: Remke, A., Haverkort, B.R. (eds) Measurement, Modelling and Evaluation of Dependable Computer and Communication Systems. MMB&DFT 2016. Lecture Notes in Computer Science(), vol 9629. Springer, Cham. https://doi.org/10.1007/978-3-319-31559-1_4
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