Reliability and energy efficient workflow scheduling in cloud environment
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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 consumptionNotes
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.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.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.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.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.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.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.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.Ullman, J.D.: NP-complete scheduling problems. J. Comput. Syst. Sci. 10, 384–393 (1975)MathSciNetCrossRefzbMATHGoogle Scholar
- 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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.Girault, Alain, Saule, Erik, Trystram, Denis: Reliability versus performance for critical applications. J. Parallel Distrib. Comput. 69(3), 326–336 (2009)CrossRefGoogle Scholar
- 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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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