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Reliability and energy efficient workflow scheduling in cloud environment

  • Ritu Garg
  • Mamta Mittal
  • Le Hoang SonEmail author
Article
  • 65 Downloads

Abstract

Cloud data centers consume huge amounts of electrical energy which results in an increased operational cost, decreased system reliability and carbon dioxide footprints. Thus, it is highly important to develop scheduling strategy to reduce energy consumption. Dynamic voltage and frequency scaling (DVFS) has been recognized as an efficient technique for reducing energy consumption. However, there is negative impact of DVFS on the reliability of system as it increases the transient faults during the application execution. Hence, it is essential to address the issue of reliability for mission critical applications. Recent studies on workflow scheduling in distributed environment have not considered reliability while minimizing the energy consumption. In this paper, we propose a new scheduling algorithm called the reliability and energy efficient workflow scheduling algorithm which jointly optimizes lifetime reliability of application and energy consumption and guarantees the user specified QoS constraint. The proposed algorithm works in four phases: priority calculation, clustering of tasks, distribution of target time and assigning the cluster to processing element with appropriate voltage/frequency levels. The simulation results obtained by using randomly generated task graphs and Gaussian Elimination task graphs shows that the proposed approach is effective in joint optimization of lifetime reliability of system and energy consumption compared to existing algorithms.

Keywords

Workflow scheduling Cloud environments Reliability Energy consumption 

Notes

Acknowledgements

The author (Le Hoang Son) would like to send sincere thanks to Prof. Pham Ky Anh, Prof. Nguyen Huu Dien and all staff members of the Center for High Performance Computing, VNU University of Science for their supports throughout 13 years of establishment (2005–2018).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This research does not involve any human or animal participation. All authors have checked and agreed the submission.

References

  1. 1.
    Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Fut. Gener. Comput. Syst. 25(6), 599–616 (2009)CrossRefGoogle Scholar
  2. 2.
    Theis, T.N., Wong, H.S.P.: The end of Moore’s law: a new beginning for information technology. Comput. Sci. Eng. 19(2), 41–50 (2017)CrossRefGoogle Scholar
  3. 3.
    Thirumalaiselvan, C., Venkatachalam, V.: A strategic performance of virtual task scheduling in multi cloud environment. Clust. Comput. (2017).  https://doi.org/10.1063/1.4981634 Google Scholar
  4. 4.
    Kumar, A.S., Venkatesan, M.: Task scheduling in a cloud computing environment using HGPSO algorithm. Clust. Comput. (2018).  https://doi.org/10.1007/s10586-018-2515-2 Google Scholar
  5. 5.
    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: 2008 14th IEEE International Conference on Parallel and Distributed Systems (pp. 171–178). IEEE (2008)Google Scholar
  6. 6.
    Thanka, M.R., Maheswari, P.U., Edwin, E.B.: An improved efficient: artificial Bee Colony algorithm for security and QoS aware scheduling in cloud computing environment. Clust. Comput. (2017).  https://doi.org/10.1007/s10586-017-1223-7 Google Scholar
  7. 7.
    Garg, R., Singh, A.: Energy-aware workflow scheduling in grid under QoS constraints. Arab. J. Sci. Eng. 41(2), 495–511 (2015)CrossRefGoogle Scholar
  8. 8.
    Ullman, J.D.: NP-complete scheduling problems. J. Comput. Syst. Sci. 10, 384–393 (1975)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    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)CrossRefGoogle Scholar
  10. 10.
    Maheswaran, M., Ali, S., Siegal, H.J., Hensgen, D., Freund, R.F.: Dynamic matching and scheduling of a class of independent tasks onto heterogeneous computing systems. In: Heterogeneous Computing Workshop, 1999 (HCW’99), Proceedings, pp. 30–44. IEEE (1999)Google Scholar
  11. 11.
    Wang, L., Lu, Y.: Efficient power management of heterogeneous soft real-time clusters. In: Real-Time Systems Symposium, 2008, pp. 323–332. IEEE (2008)Google Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    Dongarra, J.J., Jeannot, E., Saule, E., Shi, Z.: Bi-objective scheduling algorithms for optimizing makespan and reliability on heterogeneous systems. In: Proceedings of the Nineteenth Annual ACM Symposium on Parallel Algorithms and Architectures, pp. 280–288. ACM (2007)Google Scholar
  14. 14.
    Dogan, A., Ozguner, F.: Matching and scheduling algorithms for minimizing execution time and failure probability of applications in heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 308–323 (2002)CrossRefGoogle Scholar
  15. 15.
    Tang, X., Li, K., Qiu, M., Sha, E.H.M.: A hierarchical reliability-driven scheduling algorithm in grid systems. J. Parallel Distrib. Comput. 72(4), 525–535 (2012)CrossRefGoogle Scholar
  16. 16.
    Zhang, Y., Chakrabarty, K.: Energy-aware adaptive checkpointing in embedded real-time systems. In: Proceedings of the Design, Automation & Test in Europe Conference, pp. 918–923 (2003)Google Scholar
  17. 17.
    Zhu, D., Melhem, R., Mosse, D.: The effects of energy management on reliability in real-time embedded systems. In: IEEE/ACM International Conference on Computer Aided Design (ICCAD’04), pp. 35–40 (2004)Google Scholar
  18. 18.
    Lee, Y.C., Zomaya, A.Y.: Energy conscious scheduling for distributed computing systems under different operating conditions. IEEE Trans. Parallel Distrib. Syst. 22(8), 1374–1381 (2011)CrossRefGoogle Scholar
  19. 19.
    Pruhs, K., Van Stee, R., Uthaisombut, P.: Speed scaling of tasks with precedence constraints. Theory Comput. Syst. 43(1), 67–80 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Wang, L., Khan, S.U., Chen, D., KołOdziej, J., Ranjan, R., Xu, C.Z., Zomaya, A.: Energy-aware parallel task scheduling in a cluster. Fut. Gener. Comput. Syst. 29(7), 1661–1670 (2013)CrossRefGoogle Scholar
  21. 21.
    Faragardi, H.R., et al.: An analytical model to evaluate reliability of cloud computing systems in the presence of QoS requirements. In: 2013 IEEE/ACIS 12th International Conference on Computer and Information Science (ICIS). IEEE (2013)Google Scholar
  22. 22.
    Qin, X., Jiang, H.: A dynamic and reliability-driven scheduling algorithm for parallel real-time jobs executing on heterogeneous clusters. J. Parallel Distrib. Comput. 65(8), 885–900 (2005)CrossRefzbMATHGoogle Scholar
  23. 23.
    Boeres, C., Sardiña, I., Drummond, L.: An efficient weighted bi-objective scheduling algorithm for heterogeneous systems. Parallel Comput. 37(8), 349–364 (2011)CrossRefGoogle Scholar
  24. 24.
    Girault, Alain, Saule, Erik, Trystram, Denis: Reliability versus performance for critical applications. J. Parallel Distrib. Comput. 69(3), 326–336 (2009)CrossRefGoogle Scholar
  25. 25.
    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)CrossRefGoogle Scholar
  26. 26.
    Qi, X., Zhu, D., Aydin, H.: Global scheduling based reliability-aware power management for multiprocessor real-time systems. Real Time Syst. 47(2), 109–142 (2011)CrossRefzbMATHGoogle Scholar
  27. 27.
    Zhang, L., et al.: Maximizing reliability with energy conservation for parallel task scheduling in a heterogeneous cluster. Inf. Sci. 319, 113 (2015)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2011)CrossRefGoogle Scholar
  29. 29.
    Garg, R., Singh, A.K.: Multi-objective workflow grid scheduling using ε-fuzzy dominance sort based discrete particle swarm optimization. J. Supercomput. 68(2), 709–732 (2014)CrossRefGoogle Scholar
  30. 30.
    Guérout, T., Monteil, T., Da Costa, G., Calheiros, R.N., Buyya, R., Alexandru, M.: Energy-aware simulation with DVFS. Simul. Model. Pract. Theory 39, 76–91 (2013)CrossRefGoogle Scholar
  31. 31.
    Cosnard, M., Marrakchi, M., Robert, Y., Trystram, D.: Parallel Gaussian elimination on an MIMD computer. Parallel Comput. 6(3), 275–296 (1988)MathSciNetCrossRefzbMATHGoogle Scholar
  32. 32.
    Son, L.H., Jha, S., Kumar, R., Chatterjee, J.M., Khari, M.: Collaborative handshaking approaches between internet of computing and internet of things towards a smart world: a review from 2009–2017. Telecommun. Syst. (2018).  https://doi.org/10.1007/s11235-018-0481-x Google Scholar
  33. 33.
    Kapoor, R., Gupta, R., Kumar, R., Son, L.H., Jha, S.: New scheme for underwater acoustically wireless transmission using direct sequence code division multiple access in MIMO systems. Wirel. Netw. (2018).  https://doi.org/10.1007/s11276-018-1750-z Google Scholar
  34. 34.
    Singh, K., Singh, K., Son, H., Aziz, A.: Congestion control in wireless sensor networks by hybrid multi-objective optimization algorithm. Comput. Netw. 138, 90–107 (2018)CrossRefGoogle Scholar
  35. 35.
    Tam, N.T., Hai, D.T., Son, L.H., Vinh, L.T.: Improving lifetime and network connections of 3D wireless sensor networks based on fuzzy clustering and particle swarm optimization. Wirel. Netw. 24(5), 1477–1490 (2018)CrossRefGoogle Scholar
  36. 36.
    Hai, D.T., Son, L.H., Le Vinh, T.: Novel fuzzy clustering scheme for 3D wireless sensor networks. Appl. Soft Comput. 54, 141–149 (2017)CrossRefGoogle Scholar
  37. 37.
    Tam, N.T., Thanh, H.D., Son, L.H., Le, V.T.: Optimization for the sensor placement problem in 3D environments. In: 2015 IEEE 12th International Conference on Networking, Sensing and Control (ICNSC), pp. 327–333. IEEE (2015)Google Scholar
  38. 38.
    Son, L.H., Thong, P.H.: Soft computing methods for WiMax network planning on 3D geographical information systems. J. Comput. Syst. Sci. 83(1), 159–179 (2017)MathSciNetCrossRefzbMATHGoogle Scholar
  39. 39.
    Saravanan, K., Anusuya, E., Kumar, R., Son, L.H.: Real time water quality monitoring using internet of things in SCADA. Environ. Monit. Assess. 190, 556 (2018)CrossRefGoogle Scholar
  40. 40.
    Kumar, R., Son, L.H., Jha, S., Mittal, M., Goyal, L.M.: Spatial data analysis using association rule mining in distributed environments: a privacy prospect. Spat. Inf. Res. 26, 629–638 (2018)CrossRefGoogle Scholar
  41. 41.
    Kapoor, R., Gupta, R., Son, L.H., Jha, S., Kumar, R.: Boosting performance of power quality event identification with KL divergence measure and standard deviation. Measurement 126, 134–142 (2018)CrossRefGoogle Scholar
  42. 42.
    Kapoor, R., Gupta, R., Son, L.H., Jha, S., Kumar, R.: Detection of power quality event using histogram of oriented gradients and support vector machine. Measurement 120, 52–75 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computer Engineering DepartmentNational Institute of TechnologyKurukshetraIndia
  2. 2.Department of Computer Science & EngineeringG.B. Pant Engineering CollegeNew DelhiIndia
  3. 3.Institute of Research and DevelopmentDuy Tan UniversityDa NangVietnam
  4. 4.VNU Information Technology InstituteVietnam National UniversityHanoiVietnam

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