Abstract
Reducing energy consumption has become an important task in cloud datacenters. Many existing scheduling approaches in cloud datacenters try to consolidate virtual machines (VMs) to the minimum number of physical machines (PMs) and hence minimize the energy consumption. VM live migration technique is used to dynamically consolidate VMs to as few PMs as possible; however, it introduces high migration overhead. Furthermore, the cost factor is usually not taken into account by existing approaches, which will lead to high payment cost for cloud users. In this paper, we aim to achieve energy reduction for cloud providers and payment saving for cloud users, and at the same time, without introducing VM migration overhead and without compromising deadline guarantees for user tasks. Motivated by the fact that some of the tasks have relatively loose deadlines, we can further reduce energy consumption by proactively postponing the tasks without waking up new PMs. In this paper, we propose a heuristic task scheduling algorithm called Energy and Deadline Aware with Non-Migration Scheduling (EDA-NMS) algorithm. EDA-NMS exploits the looseness of task deadlines and tries to postpone the execution of the tasks that have loose deadlines in order to avoid waking up new PMs. When determining the VM instant types, EDA-NMS selects the instant types that are just sufficient to guarantee task deadline to reduce user payment cost. The results of extensive experiments show that our algorithm performs better than other existing algorithms on achieving energy efficiency without introducing VM migration overhead and without compromising deadline guarantees.
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References
Beloglazov, A., Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurrency Comput. Pract. Experience 24(13), 1397–1420 (2012)
Berral, J.L., Gavalda, R., Torres, J.: Adaptive scheduling on power-aware managed data-centers using machine learning. In: Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing. pp. 66–73. IEEE Computer Society (2011)
Van den Bossche, R., Vanmechelen, K., Broeckhove, J.: Cost-optimal scheduling in hybrid IaaS clouds for deadline constrained workloads. In: Proceedings of CLOUD, pp. 228–235. IEEE (2010)
Burns, A., Davis, R.: Mixed criticality systems-a review. Department of Computer Science, University of York, Technical report (2013)
Calheiros, R.N., Buyya, R.: Energy-efficient scheduling of urgent bag-of-tasks applications in clouds through DVFS. In: Proceedings of CloudCom, pp. 342–349. IEEE (2014)
Chen, H., Zhu, X., Guo, H., Zhu, J., Qin, X., Wu, J.: Towards energy-efficient scheduling for real-time tasks under uncertain cloud computing environment. J. Syst. Softw. 99, 20–35 (2015)
Facebook. https://www.facebook.com/
Gao, Y., Wang, Y., Gupta, S.K., Pedram, M.: An energy and deadline aware resource provisioning, scheduling and optimization framework for cloud systems. In: Proceedings of the Ninth IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis, p. 31. IEEE Press (2013)
Hadoop MapReduce. https://hadoop.apache.org/docs/r1.2.1/fair_scheduler.html
He, C., Zhu, X., Guo, H., Qiu, D., Jiang, J.: Rolling-horizon scheduling for energy constrained distributed real-time embedded systems. J. Syst. Softw. 85(4), 780–794 (2012)
Hosseinimotlagh, S., Khunjush, F.: Migration-less energy-aware task scheduling policies in cloud environments. In: Proceedings of WAINA, pp. 391–397. IEEE (2014)
Hosseinimotlagh, S., Khunjush, F., Samadzadeh, R.: Seats: smart energy-aware task scheduling in real-time cloud computing. J. Supercomput. 71(1), 45–66 (2015)
Mall, R.: Real-Time Systems: Theory and Practice. Pearson Education, India (2009)
Mao, M., Li, J., Humphrey, M.: Cloud auto-scaling with deadline and budget constraints. In: Proceedings of GRID, pp. 41–48. IEEE (2010)
Pop, F., Dobre, C., Cristea, V., Bessis, N., Xhafa, F., Barolli, L.: Deadline scheduling for aperiodic tasks in inter-cloud environments: a new approach to resource management. J. Supercomput. 71(5), 1754–1765 (2015)
Qiu, M., Sha, E.H.M.: Cost minimization while satisfying hard/soft timing constraints for heterogeneous embedded systems. ACM Trans. Des. Autom. Electron. Syst. (TODAES) 14(2), 25 (2009)
Sengupta, A., Pal, T.K.: Fuzzy preference ordering of intervals. In: Sengupta, A., Pal, T.K. (eds.) Fuzzy Preference Ordering of Interval Numbers in Decision Problems. STUDFUZZ, vol. 238, pp. 59–89. Springer, Heidelberg (2009)
Tang, Z., Qi, L., Cheng, Z., Li, K., Khan, S.U., Li, K.: An energy-efficient task scheduling algorithm in DVFS-enabled cloud environment. J. Grid Comput. 1–20 (2015)
Veni, T., Bhanu, S.: A survey on dynamic energy management at virtualization level in cloud data centers. Comput. Sci. Inf. Technol. 3, 107–117 (2013)
Wang, W.J., Chang, Y.S., Lo, W.T., Lee, Y.K.: Adaptive scheduling for parallel tasks with QoS satisfaction for hybrid cloud environments. J. Supercomput. 66(2), 783–811 (2013)
Yahoo. https://www.yahoo.com/
Zhu, X., Yang, L.T., Chen, H., Wang, J., Yin, S., Liu, X.: Real-time tasks oriented energy-aware scheduling in virtualized clouds. TOCC 2(2), 168–180 (2014)
Acknowledgments
The work on this paper has been supported by Scientific and Technological Research Program for Guangxi Educational Commission grants \(\sharp \)2013YB113, Guangxi Universities key Laboratory Fund of Embedded Technology and Intelligent Information Processing.
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Zhang, Y., Chen, L., Shen, H., Cheng, X. (2016). An Energy-Efficient Task Scheduling Heuristic Algorithm Without Virtual Machine Migration in Real-Time Cloud Environments. In: Chen, J., Piuri, V., Su, C., Yung, M. (eds) Network and System Security. NSS 2016. Lecture Notes in Computer Science(), vol 9955. Springer, Cham. https://doi.org/10.1007/978-3-319-46298-1_6
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