An energy-efficient cuckoo search algorithm for virtual machine placement in cloud computing data centers

Salami, Hamza Onoruoiza; Bala, Abubakar; Sait, Sadiq M.; Ismail, Idris

doi:10.1007/s11227-021-03807-3

Cited by 16 publications

(5 citation statements)

References 24 publications

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“…Salami et al (2021) [62] offer a virtual machine placement problem (VMPP) based on the cuckoo search (CS) algorithm. New cost and perturbation metrics have been created to increase the algorithm's performance.…”

Section: Virtual Machine Management Using Metaheuristic Methodsmentioning

confidence: 99%

A systematic review on effective energy utilization management strategies in cloud data centers

2022

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Data centers are becoming considerably more significant and energy-intensive due to the exponential growth of cloud computing. Cloud computing allows people to access computer resources on demand. It provides amenities on the pay-as-you-go basis across the data center locations spread over the world. Consequently, cloud data centers consume a lot of electricity and leave a proportional carbon impact on the environment. There is a need to investigate efficient energy-saving approaches to reduce the massive energy usage in cloud servers. This review paper focuses on identifying the research done in the field of energy consumption (EC) using different techniques of machine learning, heuristics, metaheuristics, and statistical methods. Host CPU utilization prediction, underload/overload detection, virtual machine selection, migration, and placement have been performed to manage the resources and achieve efficient energy utilization. In this review, energy savings achieved by different techniques are compared. Many researchers have tried various methods to reduce energy usage and service level agreement violations (SLAV) in cloud data centers. By using the heuristic approach, researchers have saved 5.4% to 90% of energy with their proposed methods compared with the existing methods. Similarly, the metaheuristic approaches reduce energy consumption from 7.68% to 97%, the machine learning methods from 1.6% to 88.5%, and the statistical methods from 5.4% to 84% when compared to the benchmark approaches for a variety of settings and parameters. So, making energy use more efficient could cut down the air pollution, greenhouse gas (GHG) emissions, and even the amount of water needed to make power. The overall outcome of this review work is to understand different methods used by researchers to save energy in cloud data centers.

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Section: Virtual Machine Management Using Metaheuristic Methodsmentioning

confidence: 99%

A systematic review on effective energy utilization management strategies in cloud data centers

2022

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“…It has been observed that PM consume the maximum amount of power and causes high carbon emissions. Past studies have shown that energy consumption has a linear relationship with the CPU utilization of PM [20]. Moreover, it has been proven that idle PM are primarily responsible for wasting energy and are required to be shut down when not in use.…”

Section: Energy Consumptionmentioning

confidence: 99%

Multi-Objective Reinforcement Learning for Virtual Machines Placement in Cloud Computing

Bhatt,

Singhal

2024

IJACSA

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The rapid demand for cloud services has provoked cloud providers to efficiently resolve the problem of Virtual Machines Placement in the cloud. This paper presents a VM Placement using Reinforcement Learning that aims to provide optimal resource and energy management for cloud data centers. Reinforcement Learning provides better decision-making as it solves the complexity of VM Placement problem caused due to tradeoff among the objectives and hence is useful for mapping requested VM on the minimum number of Physical Machines. An enhanced Tournament-based selection strategy along with Roulette Wheel sampling has been applied to ensure that the optimization goes through balanced exploration and exploitation, thereby giving better solution quality. Two heuristics have been used for the ordering of VM, considering the impact of CPU and memory utilizations over the VM placement. Moreover, the concept of the Pareto approximate set has been considered to ensure that both objectives are prioritized according to the perspective of the users. The proposed technique has been implemented on MATLAB 2020b. Simulation analysis showed that the VMRL performed preferably well and has shown improvement of 17%, 20% and 18% in terms of energy consumption, resource utilization and fragmentation respectively in comparison to other multi-objective algorithms.

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“…Among them, the channel power gains from the user and the network to the base station are   mB ht ,   jB ht [12]. Calculate the maximum communication rate of cloud computing platform communication network to cellular users through Shannon channel capacity calculation formula:…”

Section: Fig 4 Str Tree Zoning Diagrammentioning

confidence: 99%

An Improved Ant Colony Algorithm for Virtual Resource Scheduling in Cloud Computing

Zhong¹,

Ye²

2023

IJACSA

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In order to solve the problems of uneven spatial distribution of data nodes and unclear weight relationship of virtual scheduling features in cloud computing platform, a virtual resource scheduling method based on improved ant colony algorithm is studied and designed to improve the performance of virtual resource scheduling in cloud computing platform by this method. After analyzing the information resource sequence change of the cloud computing platform, according to the STR -Tree partition graph, a simulated annealing-based algorithm is employed to classify the resource types after optimal scheduling into IO types, middle types and CPU types, and the time span and load balance are set as the measurement indexes. The simulation results show that after applying this method, the occupied resources of the main platform are 535 MB, which are much lower than the other two comparison algorithms, and the method has improved the allocation rationality, resource balance, maximum queue length and energy consumption. This result indicates that applying this virtual resource scheduling method can effectively improve the intelligent scheduling of virtual resources in the cloud computing platform.

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An energy-efficient cuckoo search algorithm for virtual machine placement in cloud computing data centers

Cited by 16 publications

References 24 publications

A systematic review on effective energy utilization management strategies in cloud data centers

A systematic review on effective energy utilization management strategies in cloud data centers

Multi-Objective Reinforcement Learning for Virtual Machines Placement in Cloud Computing

An Improved Ant Colony Algorithm for Virtual Resource Scheduling in Cloud Computing

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