Abstract
Objectives: Cloud offers multiple benefits through its data center-based services. The whole world uses these services that are hosted by physical machines. Millions of virtual machines get an optimized utilization of hardware to utilize these physical machines. The unbalanced distribution of virtual machines to physical machines offers an in-efficient utilization of data center hardware that will also lead to more carbon emission and harm the environment as well. Methods/material: A learning function is needed to offer energy efficiency in the cloud data center through VM optimization through its optimal allocation to physical machines. Therefore, an optimal VM placement and migration algorithm is a challenge that is addressed in this paper to reach an efficient energy optimization and resource utilization level. Finding/Novelty: The proposed algorithm is led by a learning function that takes into account the available number of physical machines, number of virtual machines, incoming requests and decides to run an optimal number of physical machines to obtain energy efficiency level for the cloud data center by migrating the virtual machines (VMs). Keywords: Datacenter; Cloud computing; Energy efficiency; VM placement; VM migration; Green Cloud
Highlights
Computing has become a new consumer and virtualization model for high-cost computing infrastructures and web-based IT solutions
Energy proficiency in our suggestions is to utilize the Boxed algorithm to optimize the format of client requests for and to go for merging of V ms when requester is low in number, dynamic coordination is performed by the de-allocation algorithm, which reassembles the virtual machines (VMs) as much as possible, much as could be expected to release whatever number of servers are in sleep or shutdown model
This research focused on the energy efficiency of data center through energy-efficient resource allocations
Summary
Computing has become a new consumer and virtualization model for high-cost computing infrastructures and web-based IT solutions. Cloud computing is focused on virtualized data centers, and application providers will be available on a subscription basis. The data may include the power usage effectiveness (PUE) and cooling efficiency, network cost, and carbon emissions rate of the cloud data center that provides the service and energy-efficient broker calculates the carbon emissions of all cloud providers that provide the requested cloud service. The energy-efficient cloud framework is designed to track the overall energy consumption required by service users. It relies on two major components of the carbon emissions catalog and the energyefficient cloud, tracking the energy efficiency of each cloud provider and encouraging the cloud provider to be energy-efficient. Users can use the cloud to access these three types of services (SaaS, PaaS, and IaaS), so the service process should be energy efficient
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