Skip to main content

Advertisement

SpringerLink
Log in

A genetic algorithm-based method for optimizing the energy consumption and performance of multiprocessor systems

  • Foundations
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

In a multiprocessor system, scheduling is an NP-hard problem, and solving it using conventional techniques demands the support of evolutionary algorithms such as genetic algorithms (GAs). Handling the energy consumption issues, while delivering the desired performance for a system, is also a challenging task. In order to achieve these goals, this paper proposes a GA-based method for optimizing the energy consumption and performance of multiprocessor systems using a weighted-sum approach. A performance optimization algorithm with two different selection operators, namely the proportional roulette wheel selection (PRWS) and the rank-based roulette wheel selection (RRWS), is proposed, and the impact of adding elitism in the GA is investigated. Simulation results show that for a specific task graph, using the considered selection operators with elitism yields, respectively, 16.80, 17.11 and 17.82% reduction in energy consumption with a deviation in finish time of 2.08, 2.01 and 1.76 ms when an equal weight factor of 0.5 is considered. This confirms that the selection operator RRWS is superior to PRWS. It is also seen that using elitism enhances the optimization procedure. For a given specific workload, the average percentage reduction in energy consumption with varying weight vector is in the range 12.57–19.51%, with a deviation in finish time of the schedule varying between 1.01 and 2.77 ms.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Aupy G, Benoit A (2012) Approximation algorithms for energy, reliability and make-span optimization. CoRR-Computing Research Repository. arXiv:1210.4673

  • Bambha N, Bhattacharyya SS (2000) A joint power/performance optimization algorithm for multiprocessor systems using a period graph construct. In: Proceedings 13th international symposium on system synthesis, Madrid, pp 91–97

  • Bonyadi MR, Moghaddam ME (2009) Bipartite genetic algorithm for multi-processor task scheduling. Int J Parallel Program 37(5):462–487

    Article  MATH  Google Scholar 

  • Bougeret M, Dutot P-F, Trystram D, Jansen K, Robenek C (2015) Improved approximation algorithms for scheduling parallel jobs on identical clusters. Theor Comput Sci 600(4):70–85

    Article  MathSciNet  MATH  Google Scholar 

  • Chen G, Huang K, Knoll A (2014) Energy optimization for real-time multiprocessor system-on-chip with optimal DVFS and DPM combination. ACM Trans Embed Comput Syst 13(3s):1–21

    Article  Google Scholar 

  • Chinnery D, Keutzer K (2007) Overview of the factors affecting the power consumption. In: Closing the power gap between ASIC & custom tools and techniques for low power design. Springer, US, pp 11–53

  • Erbas C, Cerav-Erbas S, Pimentel AD (2006) Multiobjective optimization and evolutionary algorithms for the application mapping problem in multiprocessor system-on-chip design. IEEE Trans Evol Comput 10(3):358–374

    Article  Google Scholar 

  • Friese RD (2016) Efficient genetic algorithm encoding for large-scale multi-objective resource allocation. In: IEEE international parallel and distributed processing symposium workshops (IPDPSW), Chicago, IL, pp 1360–1369

  • Garshasbi MS, Effat parvar M (2013) Task scheduling of parallel heterogeneous multi-processor systems using genetic algorithm. Int J Comput Appl 61(9):23–27

    Google Scholar 

  • Hasan MZ, Bird M (2011) Energy reductions for embedded processors in reconfigurable hardware. In: 2011 IEEE international conference on electro/information technology, Mankato, MN, 2011, pp 1–8

  • Hashemian N, Diallo C, Vizvári B (2014) Make-span minimization for parallel machines scheduling with multiple availability constraints. Ann Oper Res 213(1):173–186

    Article  MathSciNet  MATH  Google Scholar 

  • He C, Lin H (2017) Notes on a hierarchical scheduling problem on identical machines. Inf Process Lett 120:6–10

    Article  MathSciNet  MATH  Google Scholar 

  • Hou ESH, Ansari N, Ren Hong (1994) A genetic algorithm for multiprocessor scheduling. IEEE Trans Parallel Distrib Syst 5(2):113–120

    Article  Google Scholar 

  • Kashan AH, Keshmiry M, Dahooie JH, Abbasi-Pooya A (2016) A simple yet effective grouping evolutionary strategy (GES) algorithm for scheduling parallel machines. J Neural Comput Appl 1–14. doi:10.1007/s00521-016-2789-3

  • Kessaci Y, Mezmaz M, Melab N, Talbi E-G, Tuyttens D (2011) Parallel evolutionary algorithms for energy aware scheduling. In: Bouvry P, Gonzalez-Velez H, Kołodziej J (eds) Intelligent decisions systems in large-scale distributed environments, studies in computational intelligence series, Chap 4, vol 362. Springer, Berlin, pp 75–100

    Google Scholar 

  • Konar A (2005) Computational intelligence: principles, techniques and applications. Springer, New York

    Book  MATH  Google Scholar 

  • Kowalczyk D, Leus R (2016) An exact algorithm for parallel machine scheduling with conflicts. J Sched 20(4):355–372

  • Li K(2016a) Energy-efficient task scheduling on multiple heterogeneous computers: algorithms, analysis, and performance evaluation. IEEE Trans Sustain Comput 1(1):7–19

  • Li K (2016b) Energy and time constrained task scheduling on multiprocessor computers with discrete speed levels. J Parallel Distrib Comput 95:15–28

  • Li K (2017) Scheduling parallel tasks with energy and time constraints on multiple many core processors in a cloud computing environment. Future Gener Comput Syst. doi:10.1016/j.future.2017.01.010

  • Miao L, Qi Y, Hou D, Wu Cl, Dai YH (2009) Energy saving task scheduling for heterogeneous CMP system based on multi-objective fuzzy genetic algorithm. In: IEEE international conference on systems, man and cybernetics, San Antonio, TX, pp 3923–3927

  • Norazizi Sham Mohd Sayuti M, Indrusiak LS, Garcia-Ortiz A (2013) An optimization algorithm for minimizing energy dissipation in NoC-based hard real-time embedded systems. In: Proceedings of the 21st international conference on real-time networks and systems, 2013. ACM, New York, NY, USA, pp 3–12

  • Oklapi E, Deubzer M, Schmidhuber S, Lalo E, Mottok J (2014) Optimization of real-time multicore systems reached by a genetic algorithm approach for runnable sequencing. In: International conference on applied electronics, Pilsen, pp 233–238

  • Page AJ, Naughton TJ (2005) Dynamic task scheduling using genetic algorithms for heterogeneous distributed computing. In: 19th IEEE international parallel and distributed processing symposium, p 189

  • Pillai P, Shin KG (2001) Real-time dynamic voltage scaling for low-power embedded operating systems. In: Symposium on operating systems principles, pp 89–102

  • Razali NM, Geraghty JJ (2011) Genetic algorithm performance with different selection strategies in solving travelling salesman problem. In: Proceedings of world congress of engineering, vol 2

  • Ren H, Hou ESH, Ansari N (1994) A genetic algorithm for multiprocessor scheduling. IEEE Trans Parallel Distrib Syst 5:113–120. doi:10.1109/71.265940

    Article  Google Scholar 

  • Sanati B, Cheng AMK (2016) LBBA: an efficient online benefit-aware multiprocessor scheduling for QoS via online choice of approximation algorithms. Future Genern Comput Syst 59:125–135

    Article  Google Scholar 

  • Shakya S, Prajapati U (2015) Task scheduling in grid computing using genetic algorithm. In: 2015 International conference on green computing and internet of things (ICGCIoT), Noida, pp 1245–1248

  • Singh R (2016) An optimized task duplication based scheduling in parallel system. Iint J Intell Syst Appl 8:26–37

    Google Scholar 

  • Singh K, Pillai AS (2014a) Energy optimization of embedded processors using elitist genetic algorithm. In: International conference on communication and computing (ICC 2014), Alpha College of Engineering, Bangalore, India, June 12–14, pp 237–245

  • Singh K, Pillai AS (2014b) Schedule length optimization by elite-genetic algorithm using rank based selection for multiprocessor systems. In: International conference on embedded systems (ICES 2014), Amrita School of Engineering, Coimbatore, India, July pp 86–91

  • Standard Task Graph Set, http://www.kasahara.elec.waseda.ac.jp/schedule/index.html. Last visited May 8, 2017

  • Sun H, Stolf P, Pierson J-M (2017) Spatio-temporal thermal-aware scheduling for homogeneous high-performance computing datacenters. Future Gener Comput Syst 71:157–170

    Article  Google Scholar 

  • The Genetic and Evolutionary Algorithms Toolbox, http://www.pg.gda.pl/~mkwies/dyd/geadocu/algselct.html. Last visited May 8, 2017

  • Theys MD, Braun TD, Siegal HJ, Maciejewski AA, Kwok Y-K (2001) Mapping tasks onto distributed heterogeneous computing systems using a genetic algorithm approach, chapter 6. Wiley, New York, pp 135–178

    Google Scholar 

  • Yeap GK (1998) Practical low power digital VLSI design. Kluwer, Norwell

    Book  Google Scholar 

  • Zhang W, Xie H, Cao B, Cheng AMK (2014) Energy-aware real-time task scheduling for heterogeneous multiprocessors with particle swarm optimization algorithm. Math Probl Eng 2014(2014):287475-1–287475-9. doi:10.1155/2014/287475

  • Zomaya AY, Teh Y-H (2001) Observations on using genetic algorithms for dynamic load-balancing. IEEE Trans Parallel Distrib Syst 12(9):899–911

    Article  Google Scholar 

Download references

Author information

Author notes

  1. Anju S. Pillai, Kaumudi Singh, and Vijayalakshmi Saravanan have contributed equally to this work.

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore, India

    Anju S. Pillai

  2. Department of Electronic System Engineering, Indian Institute of Science, Bangalore, India

    Kaumudi Singh

  3. Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada

    Vijayalakshmi Saravanan

  4. Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada

    Alagan Anpalagan

  5. Department of Computer Science, Ryerson University, Toronto, Canada

    Isaac Woungang

  6. Department of Information and Communication Engineering Faculty of Information Engineering, Fukuoka Institute of Technology (FIT), Fukuoka, Japan

    Leonard Barolli

Authors
  1. Anju S. Pillai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kaumudi Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vijayalakshmi Saravanan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alagan Anpalagan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Isaac Woungang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Leonard Barolli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Isaac Woungang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by A. Di Nola.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pillai, A.S., Singh, K., Saravanan, V. et al. A genetic algorithm-based method for optimizing the energy consumption and performance of multiprocessor systems. Soft Comput 22, 3271–3285 (2018). https://doi.org/10.1007/s00500-017-2789-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-017-2789-y

Keywords

Search

Navigation