Energy-aware and Carbon-efficient VM Placement Optimization in Cloud Datacenters using Evolutionary Computing Methods

Description

In this research, we use two well-known evolutionary algorithms, the genetic algorithm, and the memetic algorithm, for the dynamic placement of VMs. The proposed method can reduce energy and carbon costs while reducing resource allocation time. In this method, the Power Usage Effectiveness (PUE) is used to evaluate the efficiency of cloud datacenters. Also, three types of energy supply sources have been considered for cloud data centers, among which, renewable energy sources are given higher priority to reduce carbon production and reduce overall costs.

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