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International Conference on Discrete Optimization and Operations Research

DOOR 2016: Discrete Optimization and Operations Research pp 3–14Cite as

Algorithmic Issues in Energy-Efficient Computation

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9869))

Abstract

Energy efficiency has become a crucial issue in Computer Science. New hardware and system-based approaches are explored for saving energy in portable battery-operated devices, personal computers, or large server farms. The main mechanisms that have been developed for saving energy are the ability of transitioning the device among multiple power states, and the use of dynamic voltage scaling (speed scaling).

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References

  1. Albers, S.: Energy efficient algorithms. Commun. ACM 53(5), 86–96 (2010)

    Article  MathSciNet  Google Scholar 

  2. Albers, S.: Algorithms for dynamic speed scaling. In: International Symposium of Theoretical Aspects of Computer Science (STACS 2011), LIPIcs, vol. 9, pp. 1–11. Schloss Dagstuhl (2011)

    Google Scholar 

  3. Albers, S., Antoniadis, A.: Race to idle: new algorithms for speed scaling with a sleep state. In: Symposium on Discrete Algorithms (SODA), pp. 1266–1285. ACM-SIAM (2012)

    Google Scholar 

  4. Albers, S., Antoniadis, A., Greiner, G.: On multi-processor speed scaling with migration. In: Symposium on Parallelism in Algorithms and Architectures (SPAA), pp. 279–288. ACM (2011)

    Google Scholar 

  5. Albers, S., Bampis, E., Letsios, D., Lucarelli, G., Stotz, R.: Scheduling on power-heterogeneous processors. In: Kranakis, E., Navarro, G., Chávez, E. (eds.) LATIN 2016. LNCS, vol. 9644, pp. 41–54. Springer, Heidelberg (2016)

    Chapter  Google Scholar 

  6. Albers, S., Fujiwara, H.: Energy efficient algorithms for flow time minimization. ACM Trans. Algorithms 3(4), 49 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  7. Albers, S., Muller, F., Schmelzer, S.: Speed scaling on parallel processors. In: Symposium on Parallelism in Algorithms and Architectures (SPAA), pp. 289–298. ACM (2007)

    Google Scholar 

  8. Alon, N., Azar, Y., Woeginger, G.J., Yadid, T.: Approximation schemes for scheduling. In: Proceedings of 8th Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans, Louisiana, 5–7 January 1997, pp. 493–500 (1997)

    Google Scholar 

  9. Angel, E., Bampis, E., Chau, V.: Throughput maximization in the speed-scaling setting. CoRR, abs/1309.1732 (2013)

    Google Scholar 

  10. Angel, E., Bampis, E., Chau, V.: Low complexity scheduling algorithms minimizing the energy for tasks with agreeable deadlines. Discret. Appl. Math. 175, 1–10 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  11. Angel, E., Bampis, E., Chau, V.: Throughput maximization in the speed-scaling setting. In: 31st International Symposium on Theoretical Aspects of Computer Science (STACS 2014), Lyon, France, 5–8 March 2014, pp. 53–62 (2014)

    Google Scholar 

  12. Angel, E., Bampis, E., Chau, V., Letsios, D.: Throughput maximization for speed-scaling with agreeable deadlines. In: Chan, T.H.H., Lau, L.C., Trevisan, L. (eds.) TAMC 2013. LNCS, vol. 7876, pp. 10–19. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  13. Angel, E., Bampis, E., Chau, V., Thang, N.K.: Nonpreemptive throughput maximization for speed-scaling with power-down. In: Proceedings of Euro-Par: Parallel Processing 21st International Conference on Parallel and Dis-tributed Computing, Vienna, Austria, 24–28 August 2015, pp. 171–182 (2015)

    Google Scholar 

  14. Angel, E., Bampis, E., Chau, V., Thang, N.K.: Throughput maximization in multiprocessor speed-scaling. Theor. Comput. Sci. 630, 1–12 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  15. Angel, E., Bampis, E., Kacem, F., Letsios, D.: Speed scaling on parallel processors with migration. In: Kaklamanis, C., Papatheodorou, T., Spirakis, P.G. (eds.) Euro-Par 2012. LNCS, vol. 7484, pp. 128–140. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  16. Antoniadis, A., Huang, C.C.: Non-preemptive speed scaling. J. Sched. 16(4), 385–394 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  17. Antoniadis, A., Huang, C.C., Ott, S.: A fully polynomialtime approximation scheme for speed scaling with sleep state. In: Proceedings of 26th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA, San Diego, CA, USA, 4–6 January 2015, pp. 1102–1113 (2015)

    Google Scholar 

  18. Antoniadis, A., Huang, C.-C., Ott, S., Verschae, J.: How to pack your items when you have to buy your knapsack. In: Chatterjee, K., Sgall, J. (eds.) MFCS 2013. LNCS, vol. 8087, pp. 62–73. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  19. Bampis, E., Dürr, C., Kacem, F., Milis, I.: Speed scaling with power down scheduling for agreeable deadlines. Sustain. Comput.: Inform. Syst. 2(4), 184–189 (2012)

    Google Scholar 

  20. Bampis, E., Kononov, A.V., Letsios, D., Lucarelli, G., Nemparis, I.: From preemptive to non-preemptive speed-scaling scheduling. Discret. Appl. Math. 181, 11–20 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  21. Bampis, E., Kononov, A.V., Letsios, D., Lucarelli, G., Sviridenko, M.: Energy efficient scheduling and routing via randomized rounding. In: IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, FSTTCS, Guwahati, India, 12–14 December 2013, pp. 449–460 (2013)

    Google Scholar 

  22. Bampis, E., Letsios, D., Lucarelli, G.: A note on multiprocessor speed scaling with precedence constraints. In: 26th ACM Symposium on Parallelism in Algorithms and Architectures, SPAA 2014, Prague, Czech Republic, 23–25 June 2014, pp. 138–142 (2014)

    Google Scholar 

  23. Bampis, E., Letsios, D., Lucarelli, G.: Speed-scaling with no preemptions. In: Ahn, H.-K., Shin, C.-S. (eds.) ISAAC 2014. LNCS, vol. 8889, pp. 259–269. Springer, Heidelberg (2014)

    Google Scholar 

  24. Bampis, E., Letsios, D., Lucarelli, G.: Green scheduling, flows and matchings. Theoret. Comput. Sci. 579, 126–136 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  25. Bampis, E., Letsios, D., Milis, I., Zois, G.: Speed scaling for maximum lateness. Theor. Comput. Syst. 58(2), 304–321 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  26. Baptiste, P.: Scheduling unit tasks to minimize the number of idle periods: a polynomial time algorithm for offline dynamic power management. In: Symposium on Discrete Algorithms (SODA), pp. 364–367. ACM-SIAM (2006)

    Google Scholar 

  27. Baptiste, P., Chrobak, M., Dürr, C.: Polynomial-time algorithms for minimum energy scheduling. ACM Trans. Algorithms 8(3), 26 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  28. Bingham, B.D., Greenstreet, M.R.: Energy optimal scheduling on multiprocessors with migration. In: International Symposium on Parallel and Distributed Processing with Applications (ISPA), pp. 153–161. IEEE (2008)

    Google Scholar 

  29. Bunde, D.P.: Power-aware scheduling for makespan and flow. J. Sched. 12(5), 489–500 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  30. Chen, J.J., Hsu, H.R., Chuang, K.H., Yang, C.L., Pang, A.C., Kuo, T.W.: Multiprocessor energy efficient scheduling with task migration considerations. In: Euromicro Conference on Real-Time Systems (ECRTS), pp. 101–108. IEEE (2004)

    Google Scholar 

  31. Chrobak, M., Feige, U., Taghi Hajiaghayi, M., Khanna, S., Li, F., Naor, S.: A Greedy approximation algorithm for minimum-gap scheduling. In: Spirakis, P.G., Serna, M. (eds.) CIAC 2013. LNCS, vol. 7878, pp. 97–109. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  32. Chrobak, M., Golin, M., Lam, T.-W., Nogneng, D.: Scheduling with gaps: new models and algorithms. In: Paschos, V.T., Widmayer, P. (eds.) CIAC 2015. LNCS, vol. 9079, pp. 114–126. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

  33. Chrétienne, P.: On single-machine scheduling without intermediate delays. Discret. Appl. Math. 156(13), 2543–2550 (2008). In: 5th Conference, Honour of Peter Hammer’s and Jakob Krarup’s 70th Birthday, Graphs and Optimization, Fifth International Conference on Graphs and Optimization (GOV 2006)

    Article  MathSciNet  MATH  Google Scholar 

  34. Cohen-Addad, V., Li, Z., Mathieu, C., Milis, I.: Energy-efficient algorithms for non-preemptive speed-scaling. In: Bampis, E., Svensson, O. (eds.) WAOA 2014. LNCS, vol. 8952, pp. 107–118. Springer, Heidelberg (2015)

    Google Scholar 

  35. Demaine, E.D., Ghodsi, M., Hajiaghayi, M.T., Sayedi-Roshkhar, A.S., Zadimoghaddam, M.: Scheduling to minimize gaps and power consumption. In: Symposium on Parallelism in Algorithms and Architectures (SPAA), pp. 46–54. ACM (2007)

    Google Scholar 

  36. Gaujal, B., Navet, N.: Dynamic voltage scaling under EDF revisited. Real-Time Syst. 37(1), 77–97 (2007)

    Article  MATH  Google Scholar 

  37. Gaujal, B., Navet, N., Walsh, C.: Shortest-path algorithms for real-time scheduling of FIFO tasks with minimal energy use. ACM Trans. Embed. Comput. Syst. 4(4), 907–933 (2005)

    Article  Google Scholar 

  38. Gerards, M.E.T., Hurink, J.L., Hölzenspies, P.K.F.: A survey of offline algorithms for energy minimization under deadline constraints. J. Sched. 19(1), 3–19 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  39. Greiner, G., Nonner, T., Souza, A.: The bell is ringing in speed scaled multiprocessor scheduling. In: Symposium on Parallelism in Algorithms and Architectures (SPAA), pp. 11–18. ACM (2009)

    Google Scholar 

  40. Gururaj, Jalan, and Stein. Unpublished work, see survey of m. chrobak

    Google Scholar 

  41. Huang, C.-C., Ott, S.: New results for non-preemptive speed scaling. In: Csuhaj-Varjú, E., Dietzfelbinger, M., Ésik, Z. (eds.) MFCS 2014, Part II. LNCS, vol. 8635, pp. 360–371. Springer, Heidelberg (2014)

    Google Scholar 

  42. Im, S., Shadloo, M.: Brief announcement: a QPTAS for non-preemptive speed-scaling. In: Proceedings of ACM SPAA (2016)

    Google Scholar 

  43. Irani, S., Gupta, R.K., Shukla, S.K.: Competitive analysis of dynamic power management strategies for systems with multiple power savings states. In: Conference on Design, Automation and Test in Europe (DATE), pp. 117–123. IEEE (2002)

    Google Scholar 

  44. Irani, S., Pruhs, K.: Algorithmic problems in power management. ACM SIGACT News 36(2), 63–76 (2005)

    Article  Google Scholar 

  45. Kumar, G., Shannigrahi, S.: On the NP-hardness of speed scaling with sleep state. Theor. Comput. Sci. 600, 1–10 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  46. Li, M., Liu, B.J., Yao, F.F.: Min-energy voltage allocation for tree-structured tasks. J. Comb. Optim. 11(3), 305–319 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  47. Li, M., Yao, A.C., Yao, F.F.: Discrete and continuous min-energy schedules for variable voltage processors. Proc. Nat. Acad. Sci. U.S.A. 103(11), 3983–3987 (2006)

    Article  Google Scholar 

  48. Megow, N., Verschae, J.: Dual techniques for scheduling on a machine with varying speed. In: Fomin, F.V., Freivalds, R., Kwiatkowska, M., Peleg, D. (eds.) ICALP 2013, Part I. LNCS, vol. 7965, pp. 745–756. Springer, Heidelberg (2013)

    Google Scholar 

  49. Pruhs, K., Uthaisombut, P., Woeginger, G.J.: Getting the best response for your erg. ACM Trans. Algorithms 4(3), 38 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  50. Pruhs, K., van Stee, R., Uthaisombut, P.: Speed scaling of tasks with precedence constraints. Theor. Comput. Syst. 43(1), 67–80 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  51. Shabtay, D., Kaspi, M.: Parallel machine scheduling with a convex resource consumption function. Eur. J. Oper. Res. 173(1), 92–107 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  52. Shioura, A., Shakhlevich, N., Strusevich, V.: Energy optimization in speed scaling models via submodular optimization. In: 12th Workshop on Models and Algorithms for Planning and Scheduling Problems (MAPSP) (2015)

    Google Scholar 

  53. Vásquez, O.C.: Energy in computing systems with speed scaling: optimization and mechanisms design (2012). arXiv:1212.6375

  54. Yao, F.F., Demers, A.J., Shenker, S.: A scheduling model for reduced cpu energy. In: Symposium on Foundations of Computer Science (FOCS), pp. 374–382. IEEE (1995)

    Google Scholar 

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Acknowledgments

This work has been partially supported by the COFECUB project Choosing (n. 828/15).

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Authors and Affiliations

  1. Sorbonne Universités, UPMC Univ Paris 06, UMR 7606, LIP6, Paris, France

    Evripidis Bampis

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  1. Evripidis Bampis
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Correspondence to Evripidis Bampis .

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Editors and Affiliations

  1. Sobolev Institute of Mathematics , Novosibirsk, Russia

    Yury Kochetov

  2. Krasovsky Institute of Mathematics and Mechanics, Ekaterinburg, Russia

    Michael Khachay

  3. Sobolev Institute of Mathematics , Novosibirsk, Russia

    Vladimir Beresnev

  4. Far Eastern Federal University , Vladivostik, Russia

    Evgeni Nurminski

  5. University of Florida , Gainesville, Florida, USA

    Panos Pardalos

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Bampis, E. (2016). Algorithmic Issues in Energy-Efficient Computation. In: Kochetov, Y., Khachay, M., Beresnev, V., Nurminski, E., Pardalos, P. (eds) Discrete Optimization and Operations Research. DOOR 2016. Lecture Notes in Computer Science(), vol 9869. Springer, Cham. https://doi.org/10.1007/978-3-319-44914-2_1

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  • DOI: https://doi.org/10.1007/978-3-319-44914-2_1

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