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An Enhanced Approach for Performance Improvement using Hybrid Optimization Algorithm with K-means++ in a Virtualized Environment
Virtualization plays a vital role in cloud computing. It provides better manageability, availability, optimistic provisioning, scalability and resource utilization in current cloud computing environments. However the performance issue is a major concern in virtualization. The performance of the application running inside the virtual machine gets affected by the interference of the co-virtual machines. This approach provides a novel task scheduling mechanism that allocates the suitable resources to virtual machines which are running in parallel. An interference prediction scheme is proposed to utilize characteristics that are collected when an application running on virtual machines to maintain less system overhead. Nelder-mead method is employed in prediction to create relationship model from the observed response and control variables. A hybrid algorithm: Ant Colony Optimization and Cuckoo search algorithm with K-means++ is adopted for task scheduling process. This approach shows effective improvements in terms of throughput and execution time.
Ant Colony Optimization, Cuckoo Search, K-means++ Algorithm, Performance Interference, Throughput, Virtualization.
- Tsai Jinn-Tsong, Jia-Cen Fang, Jyh-Horng Chou. Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm, Computers and Operations Research. 2013; 40:3045−55.
- Zhang Y, Franke H, Moreira JE, Sivasubramaniam A. A Comparative Analysis of Space-and Time-Sharing Techniques for Parallel Job Scheduling in Large Scale Parallel Systems, IEEE Transactions on Parallel and Distributed Systems. 2002.
- Angelou, Evangelos, Konstantinos Kaffes, Athanasia Asiki, Georgios Goumas, Nectarios Koziris. Improving Virtual Host Efficiency through Resource and Interference Aware Scheduling, arXiv preprint arXiv:1601.07400. 2016.
- Ali AF, Tawhid MA. A Hybrid Cuckoo Search Algorithm with Nelder Mead Method for Solving Global Optimization Problems, SpringerPlus. 2016; 5:473. DOI: 10.1186/s40064016-2064-1.
- Bahmani, Bahman, Benjamin Moseley, Andrea Vattani, Ravi Kumar, Sergei Vassilvitskii, Scalable k-means++, Proceedings of the VLDB Endowment. 2012; 5(7):622−33.
- Rui Han, Junwei Wang, Siguang Huang, Chenrong Shao.Interference-Aware Component Scheduling for Reducing Tail Latency in Cloud Interactive Services, In: 2015 IEEE 35th International Conference on Distributed Computing Systems (ICDCS); 2015. p. 744−45.
- Wei Zhang, Sundaresan Rajasekaran, Timothy Wood, Mingfa Zhu. Mimp: Deadline and Interference Aware Scheduling of Hadoop Virtual Machines, In: 2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid); 2014. p. 394−403.
- Chin-Fu Kuo, Chi-Sheng Shih, Tei-Wei Kuo. Resource Allocation Framework for Distributed Real-Time End-ToEnd Tasks. In: 12th International Conference on Parallel and Distributed Systems; ICPADS 2006.
- Yu-Chon Kao, Ya-Shu Chen. Data-Locality-Aware MapReduce Real-Time Scheduling Framework, Journal of Systems and Software Volume. 2016; 112; 65−77.
- Hadi Salimi, Mohsen Sharifi. Batch Scheduling of Consolidated Virtual Machines based on their Workload Interference Model, Future Generation Computer Systems. 2013; 29(8):2057−66.
- Nelder JA, Mead R. A Simplex Method for Function Minimization, Comput. J. 1965; 7:308–13.
- Luersen M, Le Riche R, Guyon F. A Constrained, Globalized, and Bounded Nelder–Mead Method for Engineering Optimization, Structural and Multidisciplinary Optimization. 2004; 27:43. Doi: 10.1007/s00158-003-03209.
- Jeffrey C. Lagarias, James A. Reeds, Margaret H. Wright, Paul E. Wright. Convergence Properties of the NelderMead Simplex Algorithm in Low Dimensions, SIAM J. Optim. 1998; 9(1); 112–147.
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