Journal of Grid Computing

, Volume 13, Issue 2, pp 233–253 | Cite as

Energy-Aware Scheduling for Precedence-Constrained Parallel Virtual Machines in Virtualized Data Centers

  • Vahid Ebrahimirad
  • Maziar Goudarzi
  • Aboozar Rajabi


Large scale Internet services are expected to only increase in complexity and popularity. Their energy consumption is also a major concern in data centers. Smart scheduling of their sub-services on data center Physical Machines (PM) can effectively improve their energy as well as performance. Since today servers are not energy-proportional yet, a major and traditionally neglected source of inefficiency in them is the utilization level of PMs. We present two scheduling algorithms for precedence-constrained parallel Virtual Machines (VM) in a virtualized data center where each VM represents a sub-service of the Internet-scale service. Our algorithms use virtualization technology to increase utilization of the PMs, and hence reduce total number of active PMs, to improve energy with minimal effect on makespan. Both proposed algorithms have a polynomial time complexity which make them suitable options for scheduling of large services. Simulation results using real-world services demonstrate that the algorithms are capable of increasing utilization level of PMs on average by 52 % and improving energy consumption by 18 % while the makespan of services is degraded less than 2 %.


Energy-aware scheduling List-based scheduling Precedence-constrained parallel virtual machines Virtualized data centers Parallel and distributed computing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Brown, R.: Report to congress on server and data center energy efficiency: Public law 109-431 (2008)Google Scholar
  2. 2.
    McKinsey Report. Available:
  3. 3.
    Pascual, J.A., Lorido-Botrán, T., Miguel-Alonso, J., Lozano, J.A.: Towards a Greener Cloud Infrastructure Management using Optimized Placement Policies. J. Grid Computing, 1–15 (2014)Google Scholar
  4. 4.
    Rodero, I., Viswanathan, H., Lee, E., Gamell, M., Pompili, D., Parashar, M.: Energy-Efficient Thermal-Aware Autonomic Management of Virtualized HPC Cloud Infrastructure. J. Grid Computing 10, 447–473 (2012)CrossRefGoogle Scholar
  5. 5.
    Deng, Q., Meisner, D., Ramos, L., Wenisch, T.F., Bianchini, R.: Memscale: active low-power modes for main memory. ACM SIGPLAN Notices 46, 225–238 (2011)CrossRefGoogle Scholar
  6. 6.
    Burd, T.D., Brodersen, R.W.: Energy efficient CMOS microprocessor design. In: Proceedings of the Twenty-Eighth Hawaii International Conference on System Sciences, pp. 288–297 (1995)Google Scholar
  7. 7.
    Kaxiras, S., Hu, Z., Martonosi, M.: Cache decay: exploiting generational behavior to reduce cache leakage power. In: Proceedings of 28th Annual International Symposium on Computer Architecture, pp. 240–251 (2001)Google Scholar
  8. 8.
    Kaxiras, S., Martonosi, M.: Computer architecture techniques for power-efficiency. Synthesis Lectures on Computer Architecture 3, 1–207 (2008)CrossRefGoogle Scholar
  9. 9.
    Venkatachalam, V., Franz, M.: Power reduction techniques for microprocessor systems. ACM Comput. Surv. (CSUR) 37, 195–237 (2005)CrossRefGoogle Scholar
  10. 10.
    Chen, G., He, W., Liu, J., Nath, S., Rigas, L., Xiao, L., et al.: Energy-Aware Server Provisioning and Load Dispatching for Connection-Intensive Internet Services, in NSDI , pp. 337–350 (2008)Google Scholar
  11. 11.
    Lee, Y.C., Zomaya, A.Y.: Energy efficient utilization of resources in cloud computing systems. J. Supercomput. 60, 268–280 (2012)CrossRefGoogle Scholar
  12. 12.
    Zhu, Q., Zhu, J., Agrawal, G.: Power-aware consolidation of scientific workflows in virtualized environments. In: Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1–12 (2010)Google Scholar
  13. 13.
    Beloglazov, A.: Energy-Efficient Management of Virtual Machines in Data Centers for Cloud Computing, Ph.D., The University of Melbourne (2013)Google Scholar
  14. 14.
    Topcuoglu, H., Hariri, S., Wu, M.-Y.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Transactions on Parallel and Distributed Systems 13, 260–274 (2002)CrossRefGoogle Scholar
  15. 15.
    Tang, X., Li, K., Liao, G., Li, R.: List scheduling with duplication for heterogeneous computing systems. J. Parallel Distrib. Comput. 70, 323–329 (2010)CrossRefzbMATHGoogle Scholar
  16. 16.
    Bittencourt, L., Madeira, E.M.: Towards the scheduling of multiple workflows on computational grids. J. Grid Computing 8, 419–441 (2010)CrossRefGoogle Scholar
  17. 17.
    Arabnejad, H., Barbosa, J.: A budget constrained scheduling algorithm for workflow applications. J. Grid Computing, 1–15 (2014)Google Scholar
  18. 18.
    Li, K.: Energy efficient scheduling of parallel tasks on multiprocessor computers. J. Supercomput. 60, 223–247 (2012)CrossRefGoogle Scholar
  19. 19.
    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, 360–374 (2011)CrossRefMathSciNetGoogle Scholar
  20. 20.
    Lee, Y.C., Zomaya, A.Y.: Energy conscious scheduling for distributed computing systems under different operating conditions. IEEE Transactions on Parallel and Distributed Systems 22, 1374–1381 (2011)CrossRefGoogle Scholar
  21. 21.
    Sharifi, M., Shahrivari, S., Salimi, H.: PASTA: a power-aware solution to scheduling of precedence-constrained tasks on heterogeneous computing resources. Computing 95, 67–88 (2013)CrossRefzbMATHGoogle Scholar
  22. 22.
    Mezmaz, M., Melab, N., Kessaci, Y., Lee, Y.C., Talbi, E.-G., Zomaya, A.Y., et al.: A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems. J. Parallel Distrib. Comput. 71, 1497–1508 (2011)CrossRefGoogle Scholar
  23. 23.
    Li, K.: Scheduling precedence constrained tasks with reduced processor energy on multiprocessor computers. IEEE Trans. Comput. 61, 1668–1681 (2012)CrossRefMathSciNetGoogle Scholar
  24. 24.
    Liu, W., Li, H., Du, W., Shi, F.: Energy-aware task clustering scheduling algorithm for heterogeneous clusters. In: Proceedings of the 2011 IEEE/ACM International Conference on Green Computing and Communications, pp. 34–37 (2011)Google Scholar
  25. 25.
    Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Futur. Gener. Comput. Syst. (2012)Google Scholar
  26. 26.
    Ilavarasan, E., Thambidurai, P.: Low complexity performance effective task scheduling algorithm for heterogeneous computing environments. J. Comput. Sci. 3, 94–103 (2007)CrossRefGoogle Scholar
  27. 27.
    Daoud, M.I., Kharma, N.: A high performance algorithm for static task scheduling in heterogeneous distributed computing systems. J. Parallel Distrib. Comput. 68, 399–409 (2008)CrossRefzbMATHGoogle Scholar
  28. 28.
    Hagras, T., Janeèek, J.: A high performance, low complexity algorithm for compile-time task scheduling in heterogeneous systems. Parallel Comput. 31, 653–670 (2005)CrossRefGoogle Scholar
  29. 29.
    Liu, G., Poh, K.-L., Xie, M.: Iterative list scheduling for heterogeneous computing. J. Parallel Distrib. Comput. 65, 654–665 (2005)CrossRefzbMATHGoogle Scholar
  30. 30.
    Yang, T., Gerasoulis, A.: DSC: Scheduling parallel tasks on an unbounded number of processors. IEEE Transactions on Parallel and Distributed Systems 5, 951–967 (1994)CrossRefGoogle Scholar
  31. 31.
    Cirou, B., Jeannot, E.: Triplet: a clustering scheduling algorithm for heterogeneous systems. In: International Conference on Parallel Processing Workshops, pp. 231–236 (2001)Google Scholar
  32. 32.
    Bozdag, D., Catalyurek, U., Ozguner, F.: A task duplication based bottom-up scheduling algorithm for heterogeneous environments. In: 20th International Parallel and Distributed Processing Symposium, pp. 231–236 (2006)Google Scholar
  33. 33.
    Nesmachnow, S., Dorronsoro, B., Pecero, J., Bouvry, P.: Energy-aware scheduling on multicore heterogeneous grid computing systems. J. Grid Computing 11, 653–680 (2013)CrossRefGoogle Scholar
  34. 34.
    Garey, M.R., Johnson, D.S., Stockmeyer, L.: Some simplified NP-complete problems. In: Proceedings of the sixth annual ACM symposium on Theory of computing, pp. 47–63 (1974)Google Scholar
  35. 35.
    Gary, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-completeness. WH Freeman and Company, New York (1979)Google Scholar
  36. 36.
    Kwok, Y.-K., Ahmad, I.: Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Comput. Surv. (CSUR) 31, 406–471 (1999)CrossRefGoogle Scholar
  37. 37.
    Panwar, P., Lal, A., Singh, J.: A Genetic Algorithm Based Technique for Efficient Scheduling of Tasks on Multiprocessor System. In: Proceedings of the International Conference on Soft Computing for Problem Solving, pp. 911–919 (2012)Google Scholar
  38. 38.
    Khajemohammadi, H., Fanian, A., Gulliver, T.A.: Efficient workflow scheduling for grid computing using a leveled multi-objective genetic algorithm. J. Grid Computing, 1–27 (2014)Google Scholar
  39. 39.
    Shroff, P., Watson, D.W., Flann, N.S., Freund, R.F.: Genetic simulated annealing for scheduling data-dependent tasks in heterogeneous environments. In: 5th Heterogeneous Computing Workshop, pp. 98–117 (1996)Google Scholar
  40. 40.
    Kong, X., Chen, X., Zhang, W., Liu, G., Ji, H.: A Dynamic Simulated Annealing Algorithm with Self-adaptive Technique for Grid Scheduling, pp. 129–133 (2009)Google Scholar
  41. 41.
    Wu, M.-Y., Shu, W., Gu, J.: Efficient local search far DAG scheduling. IEEE Transactions on Parallel and Distributed Systems 12, 617–627 (2001)CrossRefGoogle Scholar
  42. 42.
    Wu, M.-Y., Shu, W., Gu, J.: Local search for DAG scheduling and task assignment. In: Proceedings of the 1997 International Conference on Parallel Processing, pp. 174–180 (1997)Google Scholar
  43. 43.
    El-Rewini, H., Lewis, T.G.: Scheduling parallel program tasks onto arbitrary target machines. J. Parallel Distrib. Comput. 9, 138–153 (1990)CrossRefGoogle Scholar
  44. 44.
    Sih, G.C., Lee, E.A.: A compile-time scheduling heuristic for interconnection-constrained heterogeneous processor architectures. IEEE Transactions on Parallel and Distributed Systems 4, 175–187 (1993)CrossRefGoogle Scholar
  45. 45.
    Iverson, M.A., Özgüner, F., Follen, G.J.: Parallelizing existing applications in a distributed heterogeneous environment. In: 4th Heterogeneous Computing Workshop (1995)Google Scholar
  46. 46.
    Baskiyar, S., SaiRanga, P.C.: Scheduling directed a-cyclic task graphs on heterogeneous network of workstations to minimize schedule length. In: International Conference on Parallel Processing Workshops, pp. 97–103 (2003)Google Scholar
  47. 47.
    Chan, W.-Y., Li, C.-K.: Heterogeneous Dominant Sequence Cluster (HDSC): a low complexity heterogeneous scheduling algorithm. In: IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, pp. 956–959 (1997)Google Scholar
  48. 48.
    Shi, Z., Dongarra, J.J.: Scheduling workflow applications on processors with different capabilities. Futur. Gener. Comput. Syst. 22, 665–675 (2006)CrossRefGoogle Scholar
  49. 49.
    Hsu, C.-H., Slagter, K.D., Chen, S.-C., Chung, Y.-C.: Optimizing energy consumption with task consolidation in clouds. Inf. Sci. 258, 452–462 (2014)CrossRefGoogle Scholar
  50. 50.
    Cosnard, M., Marrakchi, M., Robert, Y., Trystram, D.: Parallel Gaussian elimination on an MIMD computer. Parallel Comput. 6, 275–296 (1988)CrossRefzbMATHMathSciNetGoogle Scholar
  51. 51.
    Sinnen, O.: Task scheduling for parallel systems, vol. 60. Wiley (2007)Google Scholar
  52. 52.
    Fan, X., Weber, W.-D., Barroso, L.A.: Power provisioning for a warehouse-sized computer. ACM SIGARCH Computer Architecture News 35, 13–23 (2007)CrossRefGoogle Scholar
  53. 53.
    Neiger, G., Santoni, A., Leung, F., Rodgers, D., Uhlig, R.: Intel virtualization technology: Hardware support for efficient processor virtualization. Intel Technology Journal 10, 167–177 (2006)CrossRefGoogle Scholar
  54. 54.
    Kusic, D., Kephart, J.O., Hanson, J.E., Kandasamy, N., Jiang, G.: Power and performance management of virtualized computing environments via lookahead control. Clust. Comput. 12, 1–15 (2009)CrossRefGoogle Scholar
  55. 55.
    Minas, L., Ellison, B.: Energy efficiency for information technology: How to reduce power consumption in servers and data centers. Intel Press (2009)Google Scholar
  56. 56.
  57. 57.
    Maechling, P., Deelman, E., Zhao, L., Graves, R., Mehta, G., Gupta, N., et al.: SCEC CyberShake Workflows—Automating Probabilistic Seismic Hazard Analysis Calculations. In: Workflows for e-Science, pp. 143–163. Springer (2007)Google Scholar
  58. 58.
    Laird, P.W.: Institutional Profile: The USC Epigenome Center (2009)Google Scholar
  59. 59.
    Montage: An astronomical image engine. Available:
  60. 60.
    Abramovici, A., Althouse, W.E., Drever, R.W., Gürsel, Y., Kawamura, S., Raab, F.J., et al.: LIGO: The laser interferometer gravitational-wave observatory. Science 256, 325–333 (1992)CrossRefGoogle Scholar
  61. 61.
    Livny, J., Teonadi, H., Livny, M., Waldor, M.K.: High-throughput, kingdom-wide prediction and annotation of bacterial non-coding RNAs, PloS one, vol. 3 (2008)Google Scholar
  62. 62.
    Deelman, E., Mehta, G., Singh, G., Su, M.-H., Vahi, K.: Pegasus: Mapping large-scale workflows to distributed resources. In: Workflows for e-Science, pp. 376–394. Springer (2007)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Vahid Ebrahimirad
    • 1
  • Maziar Goudarzi
    • 1
  • Aboozar Rajabi
    • 2
  1. 1.Department of Computer EngineeringSharif University of TechnologyTehranIran
  2. 2.School of Electrical and Computer EngineeringUniversity of TehranTehranIran

Personalised recommendations