Virtual machine allocation to the task using an optimization method in cloud computing environment

• Published in: International journal of information technology (Singapore. Online) Vol. 12; no. 2; pp. 485 - 493
• Main Authors: Rawat, Pradeep Singh, Dimri, Priti, Saroha, Gyanendra Pal
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.06.2020
• Subjects: Artificial Intelligence; Computer Imaging; Computer Science; Image Processing and Computer Vision; Machine Learning; Original Research; Pattern Recognition and Graphics; Software Engineering; Vision; Resource; Cloud computing; Big Bang; GA; Big Crunch; Optimization
• ISSN: 2511-2104; 2511-2112
• DOI: 10.1007/s41870-018-0242-9

In the present era of computing and technology user requirements are changing frequently. Cloud computing model provides storage, computing and network resources as a service. Maintenance cost and operational cost is high in case of traditional computing model. End users pay for hardware, software, and platform. In cloud computing model everything is provided as a service. It includes 5 characteristics, four deployment models, and three service models. End-user satisfaction is the first priority of cloud service providers who are responsible to manage the cloud resources. An efficient resource allocation or resource provisioning is one of the prominent issues in a cloud computing environment. Specifically, this challenging issue can be resolved using an optimization based technique. User requests are mapped on virtual machines using task allocation schemes. The virtual machine provides storage, computing and network resources to the user requests coming from the cluster of computing nodes. In this research article specifically, we focused on the performance of proposed scheme which is based on BB-BC optimization method. Search space includes all possible schedules which uses randomly generated population, time and cost are optimization parameters. This approach is based on Big Bang Theory. It outperforms existing genetic-based task allocation algorithm with optimization criteria time and cost. Existing GA-EXE, GA-COST and proposed scheme is simulated using a similar service model. BB-BC based optimization technique provides the global best solution with increasing population size. Time and cost are used for performance evaluation of independent tasks. The motivation of research is divided into 5 sections. Sections  1 , 2 , 3 , 4 and 5 describe Introduction, Literature review, Methods and input parameters, Simulation results and discussions and Conclusions, respectively.