Virtual machine migration algorithm for energy efficiency optimization in cloud computing

Zhou, Zhou; Yu, Junyang; Li, Fangmin; Yang, Fei

doi:10.1002/cpe.4942

Cited by 28 publications

(17 citation statements)

References 21 publications

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“…An algorithm named krill herd is being implemented in order to optimize consumption of energy and to minimize the violations of SLA occurred in the data centers. [5] proposed an algorithm for effective utilization of energy in order to minimize the number of Physical Systems(PS) and by using the concept of trigger time the utilization of energy has been reduced by setting a double threshold value and the outcome of this technique, minimizes the consumption of energy by 7% and violations of SLA by 13%.…”

Section: Related Studymentioning

confidence: 99%

Effective Load Balancing Mechanism for Migration of Virtual Machine in Dynamic Cloud Environment

D¹

2022

JUSST

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Cloud Computing is a technology which is widely been used by most of the organizations to carry out their business and day by day many users are using cloud to perform their day to day activities, this increases the load on the virtual machines in the data centers to effectively perform load balancing, such that resources are effectively utilized. In this paper we proposed an algorithm for dynamically handling the load balancing in the virtual machines migration at the data centers such that the processing time, response time, data transfer cost is being minimized. The results after simulation clearly shows that the proposed system has better load balancing of resources in dynamic cloud environment.

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Section: Related Studymentioning

confidence: 99%

Effective Load Balancing Mechanism for Migration of Virtual Machine in Dynamic Cloud Environment

D¹

2022

JUSST

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“…Most existing studies determine the server's upper threshold for overloaded hosts according to the host resource utilization. By applying VM consolidation on the overloaded and underloaded hosts, the energy consumption is reduced, and the QoS is improved [19,35,39].…”

Section: Related Workmentioning

confidence: 99%

Energy-Aware and Proactive Host Load Detection in Virtual Machine Consolidation

Fard

Sohrabi

Ghods

2021

ITC

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With the expansion and enhancement of cloud data centers in recent years, increasing the energy consumptionand the costs of the users have become the major concerns in the cloud research area. Service quality parametersshould be guaranteed to meet the demands of the users of the cloud, to support cloud service providers,and to reduce the energy consumption of the data centers. Therefore, the data center's resources must be managedefficiently to improve energy utilization. Using the virtual machine (VM) consolidation technique is animportant approach to enhance energy utilization in cloud computing. Since users generally do not use all thepower of a VM, the VM consolidation technique on the physical server improves the energy consumption andresource efficiency of the physical server, and thus improves the quality of service (QoS). In this article, a serverthreshold prediction method is proposed that focuses on the server overload and server underload detectionto improve server utilization and to reduce the number of VM migrations, which consequently improves theVM's QoS. Since the VM integration problem is very complex, the exponential smoothing technique is utilizedfor predicting server utilization. The results of the experiments show that the proposed method goes beyondexisting methods in terms of power efficiency and the number of VM migrations.

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“…They use virtualization technology to low load. Virtual machines with overloaded physical machines are migrated to other physical machines 28 . A resource allocation heuristic virtual machine placement algorithm is proposed based on energy and SLA perception.…”

Section: Related Workmentioning

confidence: 99%

An energy efficient and resource‐constrained scheduling framework for smart city application

Jing

Miao

Song

et al. 2020

Trans Emerging Tel Tech

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Summary With the development of smart Internet of things devices, intelligent applications are expected to lead further innovation in smart city. However, although cloud computing infrastructure can be used to meet traditional challenges, the scheduling model for new big data intelligent application has still not matured. In this work, we proposed a two‐stage scheduling framework for smart city intelligent application. In the first stage, we propose a virtual machine selection algorithm for edge computing to enhance relative migration benefits. The algorithm defines the invalid virtual machine migration and relative migration benefits from the change in the overall computing resources of the cloud data center after the virtual machine migration. In the second stage, we proposed an energy efficient and resource‐constrained scheduling framework for edge computing. The historical data of the cloud and edge computing workload of the computing node are processed in a sliding window manner, and the median absolute deviation of the historical data is used as the base of the physical reserved resource constraint when the base also changes as the workload changes. The experimental results show that energy‐efficient and resource‐constrained can make the computer resource provide high‐quality services for users in a low‐energy state.

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Virtual machine migration algorithm for energy efficiency optimization in cloud computing

Cited by 28 publications

References 21 publications

Effective Load Balancing Mechanism for Migration of Virtual Machine in Dynamic Cloud Environment

Effective Load Balancing Mechanism for Migration of Virtual Machine in Dynamic Cloud Environment

Energy-Aware and Proactive Host Load Detection in Virtual Machine Consolidation

An energy efficient and resource‐constrained scheduling framework for smart city application

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