Energy Efficient Load Balanced Optimal Resource Allocation Scheme for Cloud Environment
K. Aruna Kumari1, J. K. R. Sastry2, K. Rajasekhara Rao3

1K. Aruna Kumari, Research Scholar, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation University, SRKR Engineering College, Bhimavaram (Andhra Pradesh), India.
2J K R Sastry, Department of Electronics and Computer Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram (Andhra Pradesh), India.
3K. Rajasekhara Rao, Department of Computer Science and Engineering, Usha Rama College of Engineering & Technology, Telaprolu, K (Andhra Pradesh), India.
Manuscript received on 11 May 2019 | Revised Manuscript received on 05 June 2019 | Manuscript Published on 15 June 2019 | PP: 146-153 | Volume-8 Issue-1S3 June 2019 | Retrieval Number: A10270681S319/2019©BEIESP
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Abstract: Different kinds of resources are required for tasks that are configured into virtual machines to be executed by physical machines. Resources are to be allocated to the physical machines for the tasks that are configured into the VM are to be scheduled for execution. Resources allocation and task scheduling are interrelated subjects. The Tasks must be executed in real time such that SLA conditions are met. The total load on the physical machines must be managed in such a way that the tasks are executed in real time as per the SLA requirements. The resources required for the tasks to be executed must be manged effectively. Many traditional methods exist in the literature that deals with optimal resources allocations to the VMs such that SLA conditions are satisfied to the extent of resources available. While that being the case, SLA conditions that are related to response time requirement have not been given with due consideration which needs to be considered along with the making available the resources that satisfy the SLA conditions related to resources. In this paper a method that takes into account both the issues of optimum resources allocation and Task scheduling to meet the response time requirements have been presented. The method (EELBRAM) is based on machine learning and load balancing undertaken through optimum energy management. The fitness is achieved through combined satisfaction of SLA conditions (weighted sum of SLA conditions). A method based on support vector machine has been used for allocation of resources that optimises future resource requirements. The experimentation of the method is presented through use of CloudSim simulator.
Keywords: Load balancing, Energy Management, SLA Evaluation, Optimal Resource Allocation, Resource Allocation.
Scope of the Article: Cloud Computing