Virtual Machine Migration and Task Mapping Architecture for Energy Optimization in Cloud
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. Cloud data centers consume huge amount of energy and there by emitting carbon dioxide to the environment. 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\textemdash length, file size, bandwidth etc. Data is clustered in to group of tasks and is mapped to the suitable virtual machine based on the configuration. Virtual machine migration is employed 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 virtual machine migration techniques. The results of this work show that the energy consumption is decreased compared to the earlier approaches, which uses traditional virtual machine migration techniques.
Cloud Computing, CloudSim, Load Balancing, Virtual Machine
Ramidi, Divya Reddy, Ajay K. Katangur, and Dulal C. Kar. "Virtual Machine Migration and Task Mapping Architecture for Energy Optimization in Cloud." In 2017 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 1566-1571. IEEE, 2017.
Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017