Skip to main content

Advertisement

Log in

Energy-Aware Workflow Scheduling in Grid Under QoS Constraints

  • Research Article - Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

In this study, we have considered the problem of scheduling precedence-constraint parallel applications (workflows) in heterogeneous grid-computing environment. Recently many heuristics have been devoted to grid scheduling typically restricted to optimizing the execution time (makespan) only without paying much concentration on energy consumption. Reducing energy consumption can bring various advantages like reducing operating costs, environmental perspective and increase in system reliability. This paper aims to develop energy-aware task scheduling algorithm in grid based on the dynamic voltage and frequency scaling (DVFS) technique. The user negotiates with the service provider on their quality of service (QoS) requirements along with green computing specifications to reach the service level agreement. With the use of DVFS, the algorithm minimizes the energy consumption of task execution while satisfying the QoS constraints (deadline). The proposed static scheduling algorithm works in three phases: deadline distribution, tasks ordering and then assigning the best services to tasks along with selecting the appropriate voltage levels while meeting its sub-deadline. The simulation results using randomly generated task graphs and task graphs corresponding to real-world problems exhibit that the proposed algorithm achieves energy efficiency and reduces energy consumption up to 68 % with the increase in 30 % of the execution time.

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.

Similar content being viewed by others

References

  1. Buyya R., Venugopal S.: A gentle introduction to grid computing and technologie. Comput. Soc. India Commun. 29, 9–19 (2005)

    Google Scholar 

  2. Yu J., Buyya R.: A taxonomy of workflow management systems for grid computing. J. Grid Comput. 3(3–4), 171–200 (2005)

    Article  Google Scholar 

  3. Dongarra J.J., Walker D.W.: The quest for petascale computing. Comput. Sci. Eng. 3(3), 32–39 (2001)

    Article  Google Scholar 

  4. Gara A., Blumrich M.A., Chen D., Chiu G.T., Coteus P., Giampapa M.E., Vranas P.: Overview of the Blue Gene/L system architecture. IBM J. Res. Dev. 49(2.3), 195–212 (2005)

    Article  Google Scholar 

  5. William, Forrest.: How to cut data centre carbon emissions? Website, December (2008)

  6. Pettey, C., (2007). Gartner estimates ICT industry accounts for 2 percent of global CO2 emissions. Dostupno na: https://www.gartner.com/newsroom/id/503867, 14, (2013)

  7. Wang, L.; Lu, Y.: Efficient power management of heterogeneous soft real-time clusters. In: Proceedings of the 2008 real-time systems symposium, Barcelona, Spain (2008)

  8. Kim, K.; Buyya, R.; Kim, J.: Power aware scheduling of bag-of-tasks applications with deadline constraints on dvs-enabled clusters. In: Proceedings of the seventh IEEE international symposium on cluster computing and the grid, vol. 7, pp. 541–548. (2007)

  9. Benini L., Bogliolo A., Micheli G.D.: A survey of design techniques for system level dynamic power management. IEEE Trans. VLSI Syst. 8(3), 299–316 (2000)

    Article  Google Scholar 

  10. Pouwelse, J.; Langendoen, K.; Sips, H.: Energy priority scheduling for variable voltage processors. In: International symposium on low power electronics and design, pp. 28–33 (2001)

  11. Magklis G., Semeraro G., Albonesi D.H., Dropsho S.G., Dwarkadas S., Scott M.L.: Dynamic frequency and voltage scaling for a multiple-clock-domain microprocessor. IEEE Micro 23(6), 62–68 (2003)

    Article  Google Scholar 

  12. Zhu D., Melhem R., Childers B.: Scheduling with dynamic voltage/speed adjustment using slack reclamation in multiprocessor real-time systems. IEEE Trans. Parallel Distrib. Syst. 14, 686–700 (2003)

    Article  Google Scholar 

  13. Orgerie, A.C.; Lefèvre, L.; Gelas, J.P.: Save watts in your Grid: green strategies for energy-aware framework in large scale distributed systems. In: Proceedings of the 2008 14th IEEE international conference on parallel and distributed systems, ICPADS ’08, pp. 171–178. IEEE Computer Society, Washington, DC (2008)

  14. Khan S., Ahmad I.: A cooperative game theoretical technique for joint optimization of energy consumption and response time in computational Grids. IEEE Trans. Parallel Distrib. Syst. 20, 346–360 (2009)

    Article  Google Scholar 

  15. Kliazovich D., Bouvry P., Khan S.U.: DENS: data centre energy-efficient network-aware scheduling. Cluster comput. 16(1), 65–75 (2013)

    Article  Google Scholar 

  16. Ge, R.; Feng, X.; Feng, W.C.; Cameron, K. W.: Cpu miser: A performance-directed, run-time system for power-aware clusters. In: Parallel processing, 2007. ICPP 2007. international conference on, pp. 18–18. IEEE (2007)

  17. Ruan, X.; Qin, X.; Zong, Z.; Bellam, K.; Nijim, M.: An energy-efficient scheduling algorithm using dynamic voltage scaling for parallel applications on clusters, In: Proceedings of 16th international conference on computer communications and networks, ICCCN, pp. 735–740 (2007)

  18. Pinel F., Dorronsoro B., Pecero J.E., Bouvry P., Khan S.U.: A two-phase heuristic for the energy-efficient scheduling of independent tasks on computational grids. Cluster comput. 16(3), 421–433 (2013)

    Article  Google Scholar 

  19. Park, G. L.; Shirazi, B.; Marquis, J.; Choo, H.: Decisive path scheduling: a new list scheduling method. In: Parallel processing, 1997. Proceedings of the 1997 international conference on, pp. 472–480. IEEE (1997)

  20. Ahmad, I.; Kwok, Y. K.; Wu, M.Y.: Analysis, evaluation, and comparison of algorithms for scheduling task graphs on parallel processors. In: Parallel architectures, algorithms, and networks, 1996. Proceedings of second international symposium on, pp. 207–213. IEEE (1996)

  21. Michael R.G., David S.J.: Computers and intractability: a guide to the theory of NP-completeness. Freeman, New York (1979)

    MATH  Google Scholar 

  22. Topcuoglu H., Hariri S., Wu M.Y.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans Parallel Distrib. Syst. 13(3), 260–274 (2002)

    Article  Google Scholar 

  23. Blythe, J.; Jain, S.; Deelaman, E.; Gil, Y.; Vahi, K.; Mandal, A.; Kennedy, K.: Task scheduling strategies for workflow-based applications in grids. In: Proceedings of the fifth IEEE international symposium on cluster computing and the grid (CCGrid’05), vol. 2, pp. 759–767 (2005)

  24. Braun T.D., Siegal H.J., Beck N.: A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J. Parallel Distrib. Comput. 61, 810–837 (2001)

    Article  Google Scholar 

  25. Selvi S., Manimegalai D.: Task scheduling using two-phase variable neighborhood search algorithm on heterogeneous computing and grid environments. Arabian J. Sci. Eng. 40, 817–844 (2015)

    Article  Google Scholar 

  26. Fujimoto, N.; Hagihara, K.: Near-optimal dynamic task scheduling of precedence constrained coarse-grained tasks onto a computational grid. In: Proceedings of ISPDC 2003, Ljubljana, Slovenia, October (2003)

  27. Dogan A., Ozguner F.: Bi-objective scheduling algorithms for execution time reliability trade-off in heterogeneous computing systems. Comput. J 48(3), 300–314 (2005)

    Article  Google Scholar 

  28. Abudhagir U.S., Shanmugavel S.: A novel dynamic reliability optimized resource scheduling algorithm for grid computing system. Arabian J Sci. Eng. 39(10), 7087–7096 (2014)

    Article  Google Scholar 

  29. Sakellariou, R.; Zhao, H.; Tsiakkouri, E.; Dikaiakos, M.D.: Scheduling workflows with budget constraints. In: Integrated research in GRID computing, pp. 189–202. Springer, New York (2007)

  30. Wieczorek M., Prodan R., Fahringer T.: Scheduling of scientific workflows in the ASKALON grid environment. ACM SIGMOD Record 34(3), 56–62 (2005)

    Article  Google Scholar 

  31. Wieczorek, M.; Podlipning, S.; Prodan, R.; Fahringer, T.: Bi-criteria Scheduling of Scientific Workflows for the Grid. In: 8th IEEE international symposium on cluster computing and the grid, (CCGRID’08) pp. 9–16 (2008)

  32. Ye, G.; Rao, R.; Li, M.: A multi objective resources scheduling approach based on genetic algorithms in grid environment. In: Fifth international conference on grid and cooperative computing workshops, pp. 504–509 (2006)

  33. Garg R., Singh A.K.: Reference point based multi-objective optimization to workflow grid scheduling. Int. J. Appl. Evol. Comput. (IJAEC). 3(1), 80–99 (2012)

    Article  Google Scholar 

  34. Da-Costa, G.; Gelas, J.-P.; Georgiou, Y.; Lefevre, L.; Orgerie, A.-C.; Pierson, J.-M.; Richard, O.; Sharma, K.: The green-net framework: energy efficiency in large scale distributed systems. In: Proceedings of the 2009 HPPAC, high performance power aware computing workshop in conjunction with IPDPS 2009, Roma, pp.1–8 (2009)

  35. Clark, C.; Fraser, K.; Hand, S.; Hansen, J. G.; Jul, E.; Limpach, C.; Prat, I.; Warfield, A.: Live migration of virtual machines. In: Proceedings of the 2nd conference on symposium on networked systems design & Implementation, vol. 2, pp. 273–286 (2005)

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

    Article  MATH  MathSciNet  Google Scholar 

  37. Garg S.K., Yeo C.S., Anandasivam A., Buyya R.: Environment-conscious scheduling of HPC applications on distributed cloud-oriented data centres. J. Parallel Distrib. Comput. 71(6), 732–749 (2011)

    Article  MATH  Google Scholar 

  38. Wang L., Khan S.U., Chen D., Kołodziej J., Ranjan R., Xu C.Z., Zomaya A.: Energy-aware parallel task scheduling in a cluster. Future Gener. Comput. Syst. 29(7), 1661–1670 (2013)

    Article  Google Scholar 

  39. Mezmaz M., Melab N., Kessaci Y., Lee Y.C., Talbi E.G., Zomaya A.Y., Tuyttens D.: A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems. J. Parallel Distrib. Comput. 71(11), 1497–1508 (2011)

    Article  Google Scholar 

  40. Subrata R., Zomaya A.Y., Landfeldt B.: Cooperative power-aware scheduling in grid computing environments. J. Parallel Distrib. Comput. 70(2), 84–91 (2010)

    Article  MATH  Google Scholar 

  41. Kolodziej J., Khan S.U., Wang L., Zomaya A.: Energy efficient genetic-based schedulers in computational grids. Concurr. Comput. 27(4), 809–829 (2012)

    Article  Google Scholar 

  42. Zong, Z.; Manzanares, A.; Stinar, B.; Qin, X.: Energy-aware duplication strategies for scheduling precedence-constrained parallel tasks on clusters. In: Cluster computing, 2006 IEEE international conference on, pp. 1–8. IEEE (2006)

  43. Lim, M.Y.; Freeh, V.W.; Lowenthal, D.K.: Adaptive, transparent frequency and voltage scaling of communication phases in MPI programs. In: SC 2006 conference, proceedings of the ACM/IEEE, pp. 1–14. IEEE (2006)

  44. Mei J., Li K., Hu J., Yin S., Sha E.H.M.: Energy-aware preemptive scheduling algorithm for sporadic tasks on DVS platform. Microprocess. Microsyst. 37(1), 99–112 (2013)

    Article  Google Scholar 

  45. Zamani, R.; Afsahi, A.; Qian, Y.; Hamacher, C.: A feasibility analysis of power-awareness and energy minimization in modern interconnects for high-performance computing. In: Cluster computing, 2007 IEEE international conference on (pp. 118–128). IEEE (2007)

  46. Sharifi M., Shahrivari S., Salimi H.: PASTA: a power-aware solution to scheduling of precedence-constrained tasks on heterogeneous computing resources. Computing 95(1), 67–88 (2013)

    Article  MATH  Google Scholar 

  47. Zong Z., Manzanares A., Ruan X., Qin X.: EAD and PEBD: two energy-aware duplication scheduling algorithms for parallel tasks on homogeneous clusters. IEEE Trans. Comput. 60(3), 360–374 (2011)

    Article  MathSciNet  Google Scholar 

  48. Lee, Y.C.; Zomaya, A.Y.: Minimizing energy consumption for precedence-constrained application using dynamic voltage scaling, in CCGRID, pp.92–99 (2009)

  49. Baskiyar S., Abdel-Kader R.: Energy aware DAG scheduling on heterogeneous systems. Cluster Comput. 13(4), 373–383 (2010)

    Article  Google Scholar 

  50. Garg R., Singh A.K.: Multi-objective workflow grid scheduling using \({\varepsilon}\) -fuzzy dominance sort based discrete particle swarm optimization. J. Supercomput. 68(2), 709–732 (2014)

    Article  Google Scholar 

  51. Garg R., Singh A.K.: Fault tolerant task scheduling on computational grid using checkpointing under transient faults. Arabian J. Sci. Eng. 39(12), 8775–8791 (2014) doi:10.1007/s13369-014-1455-2

  52. Ma Y., Gong B., Sugihara R., Gupta R.: Energy-efficient deadline scheduling for heterogeneous systems. J. Parallel Distrib. Comput. 72(12), 1725–1740 (2012)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ritu Garg.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Garg, R., Singh, A.K. Energy-Aware Workflow Scheduling in Grid Under QoS Constraints. Arab J Sci Eng 41, 495–511 (2016). https://doi.org/10.1007/s13369-015-1705-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-015-1705-y

Keywords

Navigation