Computing

, Volume 95, Issue 1, pp 67–88 | Cite as

PASTA: a power-aware solution to scheduling of precedence-constrained tasks on heterogeneous computing resources

Article

Abstract

Power efficiency is one of the main challenges in large-scale distributed systems such as datacenters, Grids, and Clouds. One can study the scheduling of applications in such large-scale distributed systems by representing applications as a set of precedence-constrained tasks and modeling them by a Directed Acyclic Graph. In this paper we address the problem of scheduling a set of tasks with precedence constraints on a heterogeneous set of Computing Resources (CRs) with the dual objective of minimizing the overall makespan and reducing the aggregate power consumption of CRs. Most of the related works in this area use Dynamic Voltage and Frequency Scaling (DVFS) approach to achieve these objectives. However, DVFS requires special hardware support that may not be available on all processors in large-scale distributed systems. In contrast, we propose a novel two-phase solution called PASTA that does not require any special hardware support. In its first phase, it uses a novel algorithm to select a subset of available CRs for running an application that can balance between lower overall power consumption of CRs and shorter makespan of application task schedules. In its second phase, it uses a low-complexity power-aware algorithm that creates a schedule for running application tasks on the selected CRs. We show that the overall time complexity of PASTA is \(O(p.v^{2})\) where \(p\) is the number of CRs and \(v\) is the number of tasks. By using simulative experiments on real-world task graphs, we show that the makespan of schedules produced by PASTA are approximately 20 % longer than the ones produced by the well-known HEFT algorithm. However, the schedules produced by PASTA consume nearly 60 % less energy than those produced by HEFT. Empirical experiments on a physical test-bed confirm the power efficiency of PASTA in comparison with HEFT too.

Keywords

DAG scheduling Energy-awareness High performance computing Heterogeneous computing resources 

Mathematics Subject Classification (2010)

68M14 68M20 

References

  1. 1.
    Rivoire S, Shah MA, Ranganathan P, Kozyrakis C (2007) JouleSort: a balanced energy-efficiency benchmark. Paper presented at the ACM SIGMOD International Conference on Management of Data, Beijing, ChinaGoogle Scholar
  2. 2.
    Wang L, Laszewski Gv, Dayal J, Wang F (2010) Towards energy aware scheduling for precedence constrained parallel tasks in a cluster with DVFS. Paper presented at the 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, Melbourne, AustraliaGoogle Scholar
  3. 3.
    Smith JE, Nair R (2005) Virtual machines: versatile platforms for systems and processes. Morgan Kaufmann, San FransiscoMATHGoogle Scholar
  4. 4.
    Orgerie A, Lefèvre L, Gelas J (2008) Save watts in your grid: green strategies for energy aware framework in large scale distributed systems. Paper presented at the international conference on parallel and distributed systems, Melbourne, Victoria, AustraliaGoogle Scholar
  5. 5.
    Topcuouglu H, Hariri S, Wu M (2002) Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans Parallel Distrib Syst 13(3):260–274CrossRefGoogle Scholar
  6. 6.
    Tao Y, Gerasoulis A (1994) DSC: scheduling parallel tasks on an unbounded number of processors. IEEE Trans Parallel Distrib Syst 5(9):951–967CrossRefGoogle Scholar
  7. 7.
    Ilavarasan E, Thambidurai P (2007) Low complexity performance effective task scheduling algorithm for heterogeneous computing environments. J Comput Sci 3(2):94–103CrossRefGoogle Scholar
  8. 8.
    Daoud M, Kharma N (2008) A high performance algorithm for static task scheduling in heterogeneous distributed computing systems. J Parallel Distrib Comput 68(4):399–409MATHCrossRefGoogle Scholar
  9. 9.
    Hagras T, Janecek J (2005) A high performance low complexity algorithm for compile-time task scheduling in heterogeneous systems. Parallel Comput 31(7):653–670CrossRefGoogle Scholar
  10. 10.
    Liu G, Poh K, Xie M (2005) Iterative list scheduling for heterogeneous computing. J Parallel Distrib Comput 65(5):654–665MATHCrossRefGoogle Scholar
  11. 11.
    Tang X, Li K, Liao G, Li R (2010) List scheduling with duplication for heterogeneous computing systems. J Parallel Distrib Comput 70(4):323–329MATHCrossRefGoogle Scholar
  12. 12.
    Bertrand C (2001) Triplet: a clustering scheduling algorithm for heterogeneous systems. Paper presented at the 2nd European Conference on Scalable Systems, Lion, FranceGoogle Scholar
  13. 13.
    Garey M, Johnson L (1976) Some simplified NP-complete graph problems. Theor Comput Sci 1(3):237–267MathSciNetMATHCrossRefGoogle Scholar
  14. 14.
    Omara FA, Arafa MM (2010) Genetic algorithms for task scheduling problem. J Parallel Distrib Comput 70(1):13–22MATHCrossRefGoogle Scholar
  15. 15.
    Kong X, Sun J, Xu W (2008) Permutation-based particle swarm algorithm for tasks scheduling in heterogeneous systems with communication delays. Int J Comput Intell Res 4(1):61–70Google Scholar
  16. 16.
    Deng R, Jiang C, Yin F (2009) Ant colony pptimization for precedence-constrained heterogeneous multiprocessor assignment problem. Paper presented at the 1st ACM/SIGEVO summit on genetic and evolutionary computation, Shanghai, ChinaGoogle Scholar
  17. 17.
    Zhuravlev S, Saez JC, Blagodurov S, Fedorova A, Prieto M (2012) Survey of energy-cognizant scheduling techniques. IEEE Trans Parallel Distrib Syst (preprint)Google Scholar
  18. 18.
    Baskiyar S, Abdel-Kader R (2010) Energy aware DAG scheduling on heterogeneous systems. Cluster Comput 13(4):373–383CrossRefGoogle Scholar
  19. 19.
    Zhang Y, Hu X, Chen D (2002) Task scheduling and voltage selection for energy minimization. Paper presented at the design automation conference, New OrleansGoogle Scholar
  20. 20.
    Pruhs K, Van Stee R, Uthaisombut P (2008) Speed scaling of tasks with precedence constraints. Theory Comput Syst 43(1):67–80MathSciNetMATHCrossRefGoogle Scholar
  21. 21.
    Mishra R, Rastogi N, Zhu D, Mossé D, Melhem R (2003) Energy aware scheduling for distributed real-time systems. Paper presented at the international parallel and distributed processing symposium, Nice, FranceGoogle Scholar
  22. 22.
    Lee Y, Zomaya A (2012) Energy efficient utilization of resources in cloud computing systems. Journal Supercomput 60(2):268–280MathSciNetCrossRefGoogle Scholar
  23. 23.
    Zhu Q, Zhu J, Agrawal G (2010) Power-aware consolidation of scientific workflows in virtualized environments. Paper presented at the ACM/IEEE international conference for high performance computing, networking, storage and analysis, Los Alamitos, CA, USAGoogle Scholar
  24. 24.
    Cioara T, Anghel I, Salomie I, Copil G, Moldovan D, Kipp A (2011) Energy aware dynamic resource consolidation algorithm for virtualized service centers based on reinforcement learning. Paper presented at the international symposium on parallel and distributed computing, Los Alamitos, CA, USAGoogle Scholar
  25. 25.
    Goiri I, Beauchea R, Le K, Nguyen TD, Haque ME, Guitart J, Torres J, Bianchini R (2011) GreenSlot: scheduling energy consumption in green datacenters. Paper presented at the SC conference, Los Alamitos, CA, USAGoogle Scholar
  26. 26.
    Goiri I, Le K, Nguyen TD, Guitart J, Torres J, Bianchini R (2012) GreenHadoop: leveraging green energy in data-processing frameworks. Paper presented at the 7th ACM European conference on computer systems, New York, NY, USAGoogle Scholar
  27. 27.
    Sinnen O (2007) Task scheduling for parallel systems. Wiley-Interscience, New YorkCrossRefGoogle Scholar
  28. 28.
    Kwok Y, Ahmad I (1999) Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Comput Surv 31(4):406–471CrossRefGoogle Scholar
  29. 29.
    Zhao H, Sakellariou R (2004) An experimental investigation into the rank function of the heterogeneous earliest finish time scheduling algorithm. Euro-Par Parallel Process 189–194Google Scholar
  30. 30.
    Bharathi S, Chervenak A, Deelman E, Mehta G, Su MH, Vahi K (2008) Characterization of scientific workflows. Paper presented at the 3rd workshop on workflows in support of large scale science, Austin, TX, USAGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.School of Computer EngineeringIran University of Science and TechnologyNarmakIran

Personalised recommendations