CLPS-GA: A case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling |
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| Affiliation: | 1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, PR China;2. Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA;3. Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA 70803, USA;1. Management College, Inner Mongolia University of Technology, Hohhot 010051, China;2. School of Economics and Management, Inner Mongolia University, Hohhot 010021, China;3. College of Science, Inner Mongolia University of Technology, Hohhot 010051, China;1. Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan;2. Department of Electrical Engineering, National Dong Hwa University, Hualien, Taiwan;3. Institute of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan;1. School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, Anhui, China;2. Anhui Key Laboratory of Software Engineering in Computing and Communication, University of Science and Technology of China, Hefei 230027, Anhui, China;1. Technical University of Cluj-Napoca, Romania;2. Babes-Bolyai University of Cluj-Napoca, Romania;3. Université Nice Sophia-Antipolis, France |
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| Abstract: | Since the appearance of cloud computing, computing capacity has been charged as a service through the network. The optimal scheduling of computing resources (OSCR) over the network is a core part for a cloud service center. With the coming of virtualization, the OSCR problem has become more complex than ever. Previous work, either on model building or scheduling algorithms, can no longer offer us a satisfactory resolution. In this paper, a more comprehensive and accurate model for OSCR is formulated. In this model, the cloud computing environment is considered to be highly heterogeneous with processors of uncertain loading information. Along with makespan, the energy consumption is considered as one of the optimization objectives from both economic and ecological perspectives. To provide more attentive services, the model seeks to find Pareto solutions for this bi-objective optimization problem. On the basis of classic multi-objective genetic algorithm, a case library and Pareto solution based hybrid Genetic Algorithm (CLPS-GA) is proposed to solve the model. The major components of CLPS-GA include a multi-parent crossover operator (MPCO), a two-stage algorithm structure, and a case library. Experimental results have verified the effectiveness of CLPS-GA in terms of convergence, stability, and solution diversity. |
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| Keywords: | Cloud computing Cloud service Energy Genetic algorithm Pareto solutions Case library |
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