Abstract:In the research on cloud workflow systems, the time efficiency optimization of the task execution is the emphasis. The energy consumption optimization of the task execution is often ignored. However, time-optimal task scheduling plans have different energy consumption. Therefore, how to solve energy-optimal task scheduling plans with time constraint are discussed in this paper. Firstly, the energy model of task execution is improved. Then, the fitness computation method of the task plan is designed to evaluate energy consumption. Finally, an adaptive inertia weight computation method is applied to adjust particle velocity accurately and a particle swarm optimization (PSO) algorithm is presented to solve the energy consumption optimization problem of task scheduling in cloud workflow systems. Experimental results show that the proposed algorithm has a stable convergence speed with low energy consumption.
[1] JUVE G, CHERVENAK A, DEELMAN E, et al. Characterizing and Profiling Scientific Workflows. Future Generation Computer Systems, 2013, 29(3): 682-692. [2] MASDARI M, VALIKARDAN S, SHAHI Z, et al. Towards Workflow Scheduling in Cloud Computing: A Comprehensive Analysis. Journal of Network and Computer Applications, 2016, 66(5): 64-82. [3] DING Y W, QIN X L, LIU L, et al. Energy Efficient Scheduling of Virtual Machines in Cloud with Deadline Constraint. Future Generation Computer Systems, 2015, 50(9): 62-74. [4] VON LASZEWSKI G, WANG L Z, YOUNGE A J, et al. Power-Aware Scheduling of Virtual Machines in DVFS-Enabled Clusters // Proc of the IEEE International Conference on Cluster Computing and Workshops. New York, USA: IEEE, 2009: 1-10. [5] KANG D K, ALHAZEMI F, KIM S H, et al. Dynamic Virtual Machine Consolidation for Energy Efficient Cloud Data Centers // Proc of the 6th International Conference on Cloud Computing. Berlin, Germany: Springer, 2015: 70-80. [6] LEE Y C, ZOMAYA A Y. Energy Efficient Utilization of Resources in Cloud Computing Systems. The Journal of Supercomputing, 2012, 60(2): 268-280. [7] SHEN Y, BAO Z F, QIN X L, et al. Adaptive Task Scheduling Strategy in Cloud: When Energy Consumption Meets Performance Guarantee. World Wide Web, 2016(2): 1-19. [8] 叶可江,吴朝晖,姜晓红,等.虚拟化云计算平台的能耗管理.计算机学报, 2012, 35(6): 1262-1285. (YE K J, WU Z H, JIANG X H, et al. Power Management of Virtualized Cloud Computing Platform. Chinese Journal of Computers, 2012, 35(6): 1262-1285.) [9] BELOGLAZOV A, ABAWAJY J, BUYYA R. Energy-Aware Resource Allocation Heuristics for Efficient Management of Data Centers for Cloud Computing. Future Generation Computer Systems, 2012, 28(5): 755-768. [10] FAN X B, WEBER W D, BARROSO L A. Power Provisioning for a Warehouse-Sized Computer // Proc of the 34th Annual International Symposium on Computer Architecture. New York,USA: ACM, 2007: 13-23. [11] REHMAN Z, HUSSAIN O K, HUSSAIN F K, et al. User-Side QoS Forecasting and Management of Cloud Services. World Wide Web, 2015, 18(6): 1677-1716. [12] PIETRI I, MALAWSKI M, JUVE G, et al. Energy-Constrained Provisioning for Scientific Workflow Ensembles // Proc of the 3rd International Conference on Cloud and Green Computing. Washington, USA: IEEE, 2013: 34-41. [13] LI H J, ZHU G F, CUI C Y, et al. Energy-Efficient Migration and Consolidation Algorithm of Virtual Machines in Data Centers for Cloud Computing. Computing, 2016, 98(3): 303-317. [14] GRAUBNER P, SCHMIDT M, FREISLEBEN B. Energy-Efficient Virtual Machine Consolidation. IT Professional, 2013, 15(2): 28- 34. [15] USMANI Z, SINGH S. A Survey of Virtual Machine Placement Techniques in a Cloud Data Center. Procedia Computer Science, 2016, 78: 491-498. [16] LIANG X L, LI W F, ZHANG Y, et al. An Adaptive Particle Swarm Optimization Method Based on Clustering. Soft Computing, 2015, 19(2): 431-448. [17] NICKABADI A, EBADZADEH M M, SAFABAKHSH R. A Novel Particle Swarm Optimization Algorithm with Adaptive Inertia Weight. Applied Soft Computing, 2011, 11(4): 3658-3670. [18] WU F H, WU Q B, TAN Y S. Workflow Scheduling in Cloud: A Survey. The Journal of Supercomputing, 2015, 71(9): 3373-3418. [19] NAIK K J, KUMAR K V, SATYANARAYANA N. Scheduling Tasks on Most Suitable Fault Tolerant Resource for Execution in Computational Grid. International Journal of Grid and Distributed Computing, 2012, 5(3): 121-132. [20] NETJINDA N, SIRINAOVAKUL B, ACHALAKUL T. Cost Optimal Scheduling in IaaS for Dependent Workload with Particle Swarm Optimization. The Journal of Supercomputing, 2014, 68(3): 1579-1603. [21] GONG M Q, WU Y, CAI Q, et al. Discrete Particle Swarm Optimization for High-Order Graph Matching. Information Sciences, 2016, 328: 158-171. [22] WU Z J, LIU X, NI Z W, et al. A Market-Oriented Hierarchical Scheduling Strategy in Cloud Workflow Systems. The Journal of Supercomputing, 2013, 63(1): 256-293.