Optimal Virtual Machine Placement Based on Grey Wolf Optimization

Al-Moalmi, Ammar; Luo, Juan; Salah, Ahmad; Li, Kenli

doi:10.3390/electronics8030283

Cited by 41 publications

(17 citation statements)

References 44 publications

(76 reference statements)

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“…Equations ( 6) and ( 7) relate to CPU and memory utilization respectively that must not exceed the value of capacity constraints (Pi). e power consumption of active PM without load is approximately 50% to 70% of fully utilized PM [32]. erefore, turning off inactive PMs are essential to decrease the total power consumption of a cloud data center.…”

Section: Objective Functionmentioning

confidence: 99%

Intelligent SLA-Aware VM Allocation and Energy Minimization Approach with EPO Algorithm for Cloud Computing Environment

Samriya

Patel

Khurana

et al. 2021

Mathematical Problems in Engineering

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Cloud computing is the most prominent established framework; it offers access to resources and services based on large-scale distributed processing. An intensive management system is required for the cloud environment, and it should gather information about all phases of task processing and ensuring fair resource provisioning through the levels of Quality of Service (QoS). Virtual machine allocation is a major issue in the cloud environment that contributes to energy consumption and asset utilization in distributed cloud computing. Subsequently, in this paper, a multiobjective Emperor Penguin Optimization (EPO) algorithm is proposed to allocate the virtual machines with power utilization in a heterogeneous cloud environment. The proposed method is analyzed to make it suitable for virtual machines in the data center through Binary Gravity Search Algorithm (BGSA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). To compare with other strategies, EPO is energy-efficient and there are significant differences. The results of the proposed system have been evaluated through the JAVA simulation platform. The exploratory outcome presents that the proposed EPO-based system is very effective in limiting energy consumption, SLA violation (SLAV), and enlarging QoS requirements for giving capable cloud service.

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Section: Objective Functionmentioning

confidence: 99%

Intelligent SLA-Aware VM Allocation and Energy Minimization Approach with EPO Algorithm for Cloud Computing Environment

Samriya

Patel

Khurana

et al. 2021

Mathematical Problems in Engineering

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“…Simulated experiments showed that the algorithm has better scheduling effect. Al-Moalmi et al [ 32 ] proposed the use of the GWA for the optimization of virtual machine layout, which can reduce the number and energy of active hosts. Experiments showed that using this algorithm can effectively reduce energy consumption and use CPU and memory resources better.…”

Section: Related Workmentioning

confidence: 99%

An improved Wolf pack algorithm for optimization problems: Design and evaluation

et al. 2021

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Wolf Pack Algorithm (WPA) is a swarm intelligence algorithm that simulates the food searching process of wolves. It is widely used in various engineering optimization problems due to its global convergence and computational robustness. However, the algorithm has some weaknesses such as low convergence speed and easily falling into local optimum. To tackle the problems, we introduce an improved approach called OGL-WPA in this work, based on the employments of Opposition-based learning and Genetic algorithm with Levy’s flight. Specifically, in OGL-WPA, the population of wolves is initialized by opposition-based learning to maintain the diversity of the initial population during global search. Meanwhile, the leader wolf is selected by genetic algorithm to avoid falling into local optimum and the round-up behavior is optimized by Levy’s flight to coordinate the global exploration and local development capabilities. We present the detailed design of our algorithm and compare it with some other nature-inspired metaheuristic algorithms using various classical test functions. The experimental results show that the proposed algorithm has better global and local search capability, especially in the presence of multi-peak and high-dimensional functions.

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“…In [20] the authors adopt grey wolf optimizer to solve VM optimization into an optimum number of active hosts. As a result of submitting the VMs to the minimum number of PMs, the power consumption had been reduced to the minimum.…”

Section: Related Workmentioning

confidence: 99%

Intra-Balance Virtual Machine Placement for Effective Reduction in Energy Consumption and SLA Violation

2019

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Cloud computing has emerged as one of the most important technological revolutions globally. However, the rapid growth of cloud computing has imposed a massive financial burden and resulted in environmental side effects due to excessive energy consumption. The high power consumption is not only attributed to the size of data centers but also to the ineptitude of resource usage. Most of the extant research has focused on reducing power consumption by an aggressive VM consolidation, which leads to the violation of the service level agreement (SLA). Furthermore, the unbalanced resource consumption exacerbates the unavailable wasted resources that are referred to as unavailable resource fragmentation. In this paper, we propose the use of a balanced resources consumption algorithm called BRC-IBMMT in order to enhance the efficiency of resource consumption while achieving an acceptable balance between conflicting correlation objectives of power consumption as well as SLA violation. The extensive simulation results of different types of workload validate and lend credence to the significance of the proposed method in reducing power consumption and SLA violation of the cloud data center. INDEX TERMS Balanced resource placement, VM consolidation, energy consumption, cloud computing.

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Optimal Virtual Machine Placement Based on Grey Wolf Optimization

Cited by 41 publications

References 44 publications

Intelligent SLA-Aware VM Allocation and Energy Minimization Approach with EPO Algorithm for Cloud Computing Environment

Intelligent SLA-Aware VM Allocation and Energy Minimization Approach with EPO Algorithm for Cloud Computing Environment

An improved Wolf pack algorithm for optimization problems: Design and evaluation

Intra-Balance Virtual Machine Placement for Effective Reduction in Energy Consumption and SLA Violation

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