Energy-Efficient Virtual Machine Migration Approach For Optimization Of Cloud Data Centres

Vatsal, Saumitra; Agarwal, Shalini

doi:10.1109/incet51464.2021.9456193

Cited by 2 publications

(1 citation statement)

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“…Improved resource usage and energy consumption are addressed and considered as NP-Hard problems. A heuristic algorithm [28] was applied for securing optimal Virtual Machine migration. These techniques also minimize CO2 emissions.…”

Section: Related Workmentioning

confidence: 99%

VM Scheduling for Efficient Dynamically Migrated Virtual Machines (VMS-EDMVM) in Cloud Computing Environment

2022

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With the massive demand and growth of cloud computing, virtualization plays an important role in providing services to end-users efficiently. However, with the increase in services over Cloud Computing, it is becoming more challenging to manage and run multiple Virtual Machines (VMs) in Cloud Computing because of excessive power consumption. It is thus important to overcome these challenges by adopting an efficient technique to manage and monitor the status of VMs in a cloud environment. Reduction of power/energy consumption can be done by managing VMs more effectively in the datacenters of the cloud environment by switching between the active and inactive states of a VM. As a result, energy consumption reduces carbon emissions, leading to green cloud computing. The proposed Efficient Dynamic VM Scheduling approach minimizes Service Level Agreement (SLA) violations and manages VM migration by lowering the energy consumption effectively along with the balanced load. In the proposed work, VM Scheduling for Efficient Dynamically Migrated VM (VMS-EDMVM) approach first detects the over-utilized host using the Modified Weighted Linear Regression (MWLR) algorithm and along with the dynamic utilization model for an underutilized host. Maximum Power Reduction and Reduced Time (MPRRT) approach has been developed for the VM selection followed by a two-phase Best-Fit CPU, BW (BFCB) VM Scheduling mechanism which is simulated in CloudSim based on the adaptive utilization threshold base. The proposed work achieved a Power consumption of 108.45 kWh, and the total SLA violation was 0.1%. The VM migration count was reduced to 2,202 times, revealing better performance as compared to other methods mentioned in this paper.

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Section: Related Workmentioning

confidence: 99%