Skip to main content

Advertisement

Log in

DVS scheduling in a line or a star network of processors

  • Published:
Journal of Combinatorial Optimization Aims and scope Submit manuscript

Abstract

Dynamic voltage scaling (DVS) is a technique which allows the processors to change speed when executing jobs. Most of the previous works study either single processor or multiple parallel processors. In this paper, we consider a network of DVS enabled processors. Every job needs to go along a certain path in the network and has a certain workload finished on any processor it goes through before it moves on to the next processor. Our objective is to minimize the total energy consumption while finishing every job before its deadline. Due to the intrinsic complexity of this problem, we only focus on line networks with two nodes and a simple one-level tree network (a star). We show that in some of these simple cases, the optimal schedule can be computed efficiently and interleaving is not needed to achieve optimality. However, in both types of networks, how to find the optimal sequence of execution remains a big challenge for jobs with general workloads.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Albers S (2011) Algorithms for dynamic speed scaling. In: STACS 2011, pp 1–11

  • Andrews M, Fernandez A, Zhang L, Zhao W (2010) Routing for energy minimization in the speed scaling model. In: Proceedings of 29th IEEE international conference on computer communications, pp 2435–2443

  • Bansal N, Chan HL, Lam TW, Lee L-K (2008) Scheduling for speed bounded processors. In: Proceedings of the 35th international symposium on automata, languages and programming, pp 409–420

  • Bansal N, Kimbrel T, Pruhs K (2004) Dynamic speed scaling to manage energy and temperature. In: Proceedings of the 45th annual symposium on foundations of computer science, pp 520–529

  • Chan HL, Chan WT, Lam TW, Lee LK, Mak KS, Wong PWH (2007) Energy efficient online deadline scheduling. In: Proceedings of the 18th annual ACM-SIAM symposium on discrete algorithms, pp 795–804

  • Chan WT, Lam TW, Mak KS, Wong PWH (2007) Online deadline scheduling with bounded energy efficiency. In: Proceedings of the 4th annual conference on theory and applications of models of computation, pp 416–427

  • Garey M, Johnson DS, Sethi R (1976) The complexity of flowshop and jobshop scheduling. Math Oper Res 1:117–129

    Article  MATH  MathSciNet  Google Scholar 

  • Graham RL, Lawler EL, Lenstra JK, Rinnooy Kan AHG (1979) Optimization and approximation in deterministic sequencing and scheduling theory: a survey. Ann Discret Math 5:287–326

    Article  MATH  MathSciNet  Google Scholar 

  • Hong I, Qu G, Potkonjak M, Srivastavas MB (1998) Synthesis techniques for low-power hard real-time systems on variable voltage processors. In: Proceedings of the IEEE real-time systems, symposium, pp 178–187

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

    Article  Google Scholar 

  • Ishihara T, Yasuura H (1998) Voltage scheduling problem for dynamically variable voltage processors. In: Proceedings of international symposium on low power electronics and design, pp 197–202

  • Johnson SM (1954) Optimal two- and three-stage production schedules with setup times included. Naval Res Logist Q 1:61–68

    Article  Google Scholar 

  • Kwon W, Kim T (2003) Optimal voltage allocation techniques for dynamically variable voltage processors. In: Proceedings of the 40th conference on design automation, pp 125–130

  • Lam TW, Lee LK, To IKK, Wong PWH (2007) Energy efficient deadline scheduling in two processor systems. In: Proceedings of the 18th international symposium on algorithm and computation, pp 476–487

  • Li M, Yao FF (2005) An efficient algorithm for computing optimal discrete voltage schedules. SIAM J Comput 35(3):658–671

    Article  MathSciNet  Google Scholar 

  • Li M, Yao AC, Yao FF (2005) Discrete and continuous min-energy schedules for variable voltage processors. Proc Natl Acad Sci USA 103:3983–3987

    Article  Google Scholar 

  • Pinedo M (2002) Flow shops and flexible fow shops (deterministic), scheduling: theory, algorithms, and systems (chapter 6), 2nd edn. Prentice Hall, Englewood Cliffs

    Google Scholar 

  • Pruhs K, Stein C (2010) How to schedule when you have to buy your energy. In: Proceedings of the 13th international workshop on approximation, randomization, and combinatorial optimization. Algorithms and techniques, pp 352–365

  • Wu W, Li M, Chen E (2009) Min-energy scheduling for aligned jobs in accelerate model. In: Proceedings of the 20th international symposium on algorithms and computation, pp 462–472

  • Yao F, Demers A, Shenker S (1995) A scheduling model for reduced CPU energy. In: Proceedings of the 36th annual IEEE symposium on foundations of computer science, pp 374–382

Download references

Acknowledgments

This work was fully supported by a Grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CityU 124411).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Minming Li.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mu, Z., Li, M. DVS scheduling in a line or a star network of processors. J Comb Optim 29, 16–35 (2015). https://doi.org/10.1007/s10878-013-9668-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10878-013-9668-y

Keywords

Navigation