A PSO-Based Algorithm for Load Balancing in Virtual Machines of Cloud Computing Environment

详细信息    查看全文

• 作者：Zhanghui Liu (1) yuhcaolong@126.com
  Xiaoli Wang (1) fzugwz@163.com
• 关键词：Cloud Computing &#8211 ; VMs &#8211 ; Load Balancing &#8211 ; Task Scheduling &#8211 ; PSO
• 刊名：Lecture Notes in Computer Science
• 出版年：2012
• 出版时间：2012
• 年：2012
• 卷：7331
• 期：1
• 页码：142-147
• 全文大小：204.1 KB
• 参考文献：1. Virtualization and Cloud Computing Group.: Virtualization and Cloud Computing, pp.110–114. Publishing House of Electronics Industry, Beijing (2009) (in Chinese)
  2. Hu, J., Gu, J., Sun, G., et al.: A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment. In: 3rd International Symposium on Parallel Architectures, Algorithms and Programming, Dalian, Liaoning, China, pp. 89–96 (2010)
  3. Fang, Y., Wang, F., Ge, J.: A Task Scheduling Algorithm Based on Load Balancing in Cloud Computing. In: Wang, F.L., Gong, Z., Luo, X., Lei, J. (eds.) WISM 2010. LNCS, vol. 6318, pp. 271–277. Springer, Heidelberg (2010)
  4. Paton, N.W., de Aragao, M.A.T., Lee, K., Fernandes, A.A.A.: Optimizing Utility in Cloud Computing through Automatic Workload Execution. IEEE Data Eng. Bull. 32, 51–58 (2009)
  5. Li, L.: An Optimistic Differentiated Service Job Scheduling System for Cloud Computing Service Users and Providers. In: Third International Conference on Multimedia and Ubiquitous Engineering, Qingdao, China, pp. 295–299 (2009)
  6. Wei, G., Athanasios, V.V., Yao, Z., et al.: A game-theoretic method of fair resource allocation for Cloud Computing Services. The Journal of SuperComputing 2, 252–269 (2009)
  7. Martin, R., David, L., Taleb-Bendiab, A.: A Comparative Study into Distributed Load Balancing Algorithms for Cloud Computing. In: 2010 IEEE 24th International Conference on Advanced Information Netwoking and Applications Workshops, Perth, Australia, pp. 551–556 (2010)
  8. Zhang, B., Gao, J., Ai, J.: Cloud Loading Balance Algorithm. In: 2nd International Conference on Information and Engineering, ICISE 2010, Hangzhou, China, pp. 5001–5004 (2010) (in Chinese)
  9. Laura, G., David, I., Varun, M., et al.: Harnessing Virtual Machine Resource Control for Job Management. In: The 1st Workshop on System-level Virtualization for High Performance Computing, Lisbon, Portugal (2007)
  10. Kwok, Y.-K., Ahmad, I.: Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Computing Surveys 4, 406–471 (2009)
  11. Ji, Y.-M., Wang, R.-C.: Study on PSO algorithm in solving grid task scheduling. Journal on Communications 10, 60–66 (2007) (in Chinese)
  12. Pandey, S., Wu, L., Guru, S., et al.: A Particle Swarm Optimization (PSO)-based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments. In: 24th IEEE International Conference on Advanced Information Networking and Applications, Perth, Australia, pp. 400–407 (2010)
  13. James, K., Russell, E.: Particle Swarm Optimization. In: Proceedings of Neural Networks 1995, Perth, Australia, pp. 1942–1948 (1995)
  14. Zhou, H.-R., Zheng, P.-E.: Optimization for parrel multi-machine scheduling based on hierarchial genetic algorithm. Computer Applications, 2273–2275 (2007) (in Chinese)
  15. Zhou, C., Gao, H.-B., Gao, L., et al: Particle Swarm Optimization (PSO) Algorithm. Application Research of Computers, pp. 7–11 (2003) (in Chinese)
• 作者单位：1. College of Mathematics and Computer Sciences, Fuzhou University, Fuzhou, China
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

It is possible for IT service providers to provide computing resources in an pay-per-use way in Cloud Computing environments. At the same time, terminal users can also get satisfying services conveniently. But if we take only execution time into consideration when scheduling the cloud resources, it may occur serious load imbalance problem between Virtual Machines (VMs) in Cloud Computing environments. In addition to solve this problem, a new task scheduling model is proposed in this paper. In the model, we optimize the task execution time in view of both the task running time and the system resource utilization. Based on the model, a Particle Swarm Optimization (PSO) – based algorithm is proposed. In our algorithm, we improved the standard PSO, and introduce a simple mutation mechanism and a self-adapting inertia weight method by classifying the fitness values. In the end of this paper, the global search performance and convergence rate of our adaptive algorithm are validated by the results of the comparative experiments.