Abstract
With the development of cloud environment which is serving user requests, storing data etc., energy consumption has become a big issue. Increased energy data consumption of data centers emit a large amount of CO2 and also has made the IT industry to worry about when we think of green computing. As more tasks are running in the datacenter, minimizing the energy consumption becomes a challenge. Technologies like virtualization, migration, and DVFS (Dynamic Voltage and Frequency Scaling) and workload consolidation are the appreciating solutions and hence used in our work to reduce energy consumption and power without affecting the progress rate of jobs. Virtualization is a technology in which physical machines are partitioned into multiple virtual machines (VMs). Techniques like Fuzzy logic and Linear Regression are also used for the host discovery and allocation of VM identified for migration. We have also compared our proposed mechanism with existing systems in various dimensions. To understand this, a prior knowledge of cloud’s energy consumption is required.
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Asha, N., Raghavendra Rao, G. (2019). MDRAA: A Multi-decisive Resource Allocation Approach to Enhance Energy Efficiency in a Cloud Computing Environment. In: Luhach, A., Jat, D., Hawari, K., Gao, XZ., Lingras, P. (eds) Advanced Informatics for Computing Research. ICAICR 2019. Communications in Computer and Information Science, vol 1076. Springer, Singapore. https://doi.org/10.1007/978-981-15-0111-1_26
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