Network Aware Virtual Machine Migration by PSO Optimization
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 5
Abstract
A good VM migration algorithm can greatly improve network performance and scalability. Only a few studies presently focus on the network-aware VM migration (NetVMM) problem. The NetVMM problem is a type of a multiple-knapsack problem. Thus, finding an optimal solution in polynomial time is not practical. Our goal is to find an approximation solution to this NPcomplete problem which is energy efficient. In this era of technology, some energy efficient techniques are needed. Hence we were motivated to carry out this project and work for energy efficient environment. Our objective to find is an approximate optimal solution which is energy efficient through repeated iterations to make it a good solution for the VM migration problem. We will make a Matlab program for showing the output of the PSO optimization algorithms used. In computing and research, a genetic algorithm (GA) may be a meta-heuristic galvanized by the method of natural action that belongs to the larger category of organic process algorithms. Genetic algorithms are unremarkably accustomed generate high-quality solutions to improvement and search issues by hoping on bio-inspired operators like mutation, crossover and choice. In a genetic algorithm, a population of candidate solutions to an optimization problem evolves toward better solutions. Each candidate solution has a set of properties. Particle swarm optimization (PSO) solves a optimization scenario by having a number of candidate solutions, here virtual machines, and migrating these virtual machines around in the search-space according to some derived formulae over the virtual machines’ position and velocity. We will be implementing the PSO optimization algorithm in Matlab environment to solve the problem of cost optimization of the data centers, so that optimal number of tasks are divided in each virtual machine, hence balancing the cost and the network.
Authors and Affiliations
Sarthak Tandon, Chandan Kesarwani, Paridhi Srivastava, Abhishek Suryan, Swathi J N
Keywords
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