The growth of data storage in a virtual environment is becoming more essential and also the demand on the computation for an optimized performance are increasing in par with the enterprises requirements leads to substantial increase in the power consumption of very large infrastructures. This is resolves with the implementation of cloud storage which saves more energy. This proposed research focus on the different methods for enforcing the cloud storage for effective consumption of energy. Currently, advancement of cloud computing methodology leads huge data center size and network resources are expanding rapidly. According to the minimum migration policy and Virtual Resource Dynamic Integration (VRDI) for energy efficiency, Virtual Machines (VM) selection algorithms were launched. High energy consumption of network resources was drawn by mobile internet development sequentially. Primarily VRDI method doesn’t consider the consequence of the network resources on the energy consumption of a data center and so the proposed work influences the criteria like bandwidth, data size etc. On the other hand, VMs load pattern’s prediction in the VM selection algorithm becomes a tedious process. Extreme Learning Machine (ELM) classifier is proposed for prediction of VMs load pattern in order to rectify this issue. An energy-efficient Optimization Virtual Resource Dynamic Allocation (OVRDA) method is proposed to reduce the energy consumption of a data center. Loading the patterns of the Physical Machines (PMs) and the subsequent thresholds of PMs were computed with the help of the statistical data, in this proposed OVRDA. Another proposed work is, PM selection algorithms based on the Enhanced Firefly Algorithm (Enhanced FA) established a set of PMs which should be incorporated. According to the minimum migration policy to select the VMs, which are installed on the integrated PMs, VM selection algorithm will be executed. According to the enhanced FA, the goal of the VM placement will be confirmed. The experiments report that the proposed OVRDA method reduces the energy consumption of data center and assures the Quality of Service (QoS) of the cloud applications which are established on the VMs.
Volume 12 | 01-Special Issue
Pages: 629-640
DOI: 10.5373/JARDCS/V12SP1/20201112