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Graduate Projects - Details

Computer Science Program

Project ID: 522
Author: Divya Reddy Ramidi
Project Title: Virtual Machine Mirgration and Task Mapping Architecture for Cloud
Semester: 3 2017
Committe Chair: Dr. Ajay Katangur
Committee Member 1: Dr. Dulal Kar
Committee Member 2: -
Project Description: Growth of information technology led to the increasing need of computing and storage. Cloud services is one such technology with high demand and hence requires more computing resources and storage. Therefore, the energy consumption by the cloud is also increasing. Cloud data centers consume huge amount of energy and there by emitting carbon dioxide to the environment. Active efforts are put in to this research to minimize the energy consumption of data centers. This work proposes an approach for energy efficient resource management. Earlier approaches do not focus on the variations of workloads and lack in examining the effect of algorithms on performance. Virtual machine configuration also plays a vital role for reducing energy consumption and resource wastage, but is not given much importance. To address these weaknesses, this work proposes a novel approach to map groups of tasks to customized virtual machine types. Mapping of the tasks is based on task usage patterns like length, file size and bandwidth. Data is clustered in to group of tasks and is mapped to the suitable virtual machine based on the configuration. If the virtual machine is overloaded in this process, the performance is reduced and if the virtual machine is under loaded, resources are wasted and energy consumption increases. Virtual machine migration is done to balance the load by calculating the load using MIPS, RAM and Bandwidth. Complete end-end architecture is proposed in this work with clustering of tasks, allocation of tasks to virtual machines and performed virtual machine migration techniques. The results of this work show that the energy consumption is decreased compared to the earlier approach which uses traditional virtual machine migration techniques.
Project URL:   522.pdf
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