Multiobjective Virtual Machine Placement in Cloud Environment

In this paper, the virtual machine placement problem is formulated as a multi-objective optimization problem. The objectives are maximizing profit, maximizing load balancing and minimizing recourse wastage. Results of Genetic algorithms, Non-dominated Sorting Genetic Algorithm and Non-dominated Sorting Genetic Algorithm-II are compared with common solution representation, penalty and benefit values. All the three algorithms reported good solutions whereas GA and NSGA are subjected to premature convergence and duplicate solutions. NSGA-II gives good and diversified range of solutions.

[1]  Kwang Mong Sim,et al.  Agent-Based Adaptive Resource Allocation on the Cloud Computing Environment , 2011, 2011 40th International Conference on Parallel Processing Workshops.

[2]  Limin Xiao,et al.  A statistical based resource allocation scheme in cloud , 2011, 2011 International Conference on Cloud and Service Computing.

[3]  Dan Lin,et al.  A competitive genetic algorithm for resource-constrained project scheduling problem , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[4]  H. Motameni,et al.  Task scheduling with Load balancing for computational grid using NSGA II with fuzzy mutation , 2012, 2012 2nd IEEE International Conference on Parallel, Distributed and Grid Computing.

[5]  Michael E. Wall,et al.  Galib: a c++ library of genetic algorithm components , 1996 .

[6]  Quanyan Zhu,et al.  Dynamic Resource Allocation for Spot Markets in Cloud Computing Environments , 2011, 2011 Fourth IEEE International Conference on Utility and Cloud Computing.

[7]  Djamal Zeghlache,et al.  Minimum Cost Maximum Flow Algorithm for Dynamic Resource Allocation in Clouds , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[8]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[9]  Mark Harman,et al.  Pareto efficient multi-objective test case selection , 2007, ISSTA '07.

[10]  Zhen Xiao,et al.  Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment , 2013, IEEE Transactions on Parallel and Distributed Systems.

[11]  Min Liu,et al.  Multi-objective optimization model of virtual resources scheduling under cloud computing and it's solution , 2011, 2011 International Conference on Cloud and Service Computing.

[12]  Dmytro Dyachuk,et al.  Maximizing Cloud Providers' Revenues via Energy Aware Allocation Policies , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[13]  Li Xu,et al.  Multi-objective Optimization Based Virtual Resource Allocation Strategy for Cloud Computing , 2012, 2012 IEEE/ACIS 11th International Conference on Computer and Information Science.

[14]  Ryan Jansen,et al.  Energy efficient virtual machine allocation in the cloud , 2011, 2011 International Green Computing Conference and Workshops.

[15]  Rajkumar Buyya,et al.  CloudSim: A Novel Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services , 2009, ArXiv.

[16]  Kalyanmoy Deb,et al.  MULTI-OBJECTIVE FUNCTION OPTIMIZATION USING NON-DOMINATED SORTING GENETIC ALGORITHMS , 1994 .

[17]  Eckart Zitzler,et al.  Evolutionary algorithms for multiobjective optimization: methods and applications , 1999 .

[18]  Jing Xu,et al.  Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments , 2010, 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing.

[19]  Andrew J. Page,et al.  Dynamic task scheduling using genetic algorithms for heterogeneous distributed computing , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

[20]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[21]  B Sivaselvan,et al.  Time table scheduling using Genetic Algorithms employing guided mutation , 2010, 2010 IEEE International Conference on Computational Intelligence and Computing Research.

[22]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[23]  A. B. Dieker,et al.  Cloud Computing Operations Research , 2013 .

[24]  Gunho Lee,et al.  Resource Allocation and Scheduling in Heterogeneous Cloud Environments , 2012 .

[25]  Hua-Ping Chen,et al.  Two-agent scheduling on a single batch processing machine with non-identical job sizes , 2011, 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC).

[26]  Jing Xu,et al.  A multi-objective approach to virtual machine management in datacenters , 2011, ICAC '11.

[27]  Dhaval Bonde.  Techniques for Virtual Machine Placement in Clouds , 2010 .

[28]  Adrian Brezulianu,et al.  A genetic algorithm approach for a constrained employee scheduling problem as applied to employees at mall type shops , 2009, ICHIT '09.

[29]  Kwang Mong Sim,et al.  Location-Aware Dynamic Resource Allocation Model for Cloud Computing Environment , 2012 .

[30]  Marin Golub,et al.  Solving timetable scheduling problem using genetic algorithms , 2003, Proceedings of the 25th International Conference on Information Technology Interfaces, 2003. ITI 2003..

[31]  S. D. Madhu Kumar,et al.  Power Efficient Resource Allocation for Clouds Using Ant Colony Framework , 2011, ArXiv.