Simultaneous application assignment and virtual machine placement via ant colony optimization for energy-efficient enterprise data centers

Alharbi, Fares; Tian, Yu-Chu; Tang, Maolin; Ferdaus, Hasanul; Zhang, Wei Zhe; Yu, Zu Guo

doi:10.1007/s10586-020-03186-z

Cited by 10 publications

(3 citation statements)

References 29 publications

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“…Alharbi et al (2021) [53] improved existing research that manages data center resources using two independent layers: applications allotted to VMs and VM placement to hosts; both are bin packing problems. This sequential double-layered bin packing (Consec2LBP) makes easier the issue solving and restricts added solution quality development.…”

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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“…An integrated method that incorporates application assignment and VMP has been proposed in Reference 45 to increase the energy efficiency of business cloud data centers. It turned the challenge of reducing corporate data center energy usage into an Integrated Two‐layer Bin Packing (Int2LBP) issue.…”

Section: Vmp In Cloud Computingmentioning

confidence: 99%

Nature‐inspired virtual machine placement mechanisms: A systematic review

Kong

He²,

Abnoosian

2022

Concurrency and Computation

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Cloud data centers do not completely use their resources, resulting in resource underutilization. Cloud computing companies primarily leverage virtualization technologies to supply cost-effective service provision. In order to optimize cloud performance, virtual machines (VMs) must be placed among physical machines (PMs). When it comes to concentrating on the issues in the cloud computing environment, effective VM placement (VMP) is one of the primary difficulties that might cost suppliers money. VMP may be applied in a variety of ways in cloud computing. In terms of lowering related processing overhead, consolidating the cloud environment to become a highly on-demand method, balancing the load among PMs, power usage, and refining performance, VMP techniques still require improvement in the computing environment. This study aims to provide a comprehensive overview of VMP approaches. This article provides an up-to-date survey of the most related VMP literature to highlight study possibilities in cloud settings utilizing nature-inspired metaheuristic algorithms. The findings suggest that placing VMs in the most efficient place saves power usage substantially. The key problem is to minimize data center energy usage without compromising performance or breaking service level agreements. Finally, we will discuss and look at what further may be accomplished in this line of science.

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“…Through an analysis of five types of real-world work flows, ACO has been shown to outperform Particle Swarm Optimization (PSO) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Recently, ACO has also been investigated with a refined formulation and new heuristics for energy-efficient VM placement [2,3]. As both ACO and GA belong to metaheuristic methods, they share many similar features.…”

Section: N Vmà1pmmentioning

confidence: 99%

Accelerated computation of the genetic algorithm for energy-efficient virtual machine placement in data centers

Ding

Tian²,

Wang³

et al. 2022

Neural Comput & Applic

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Energy efficiency is a critical issue in the management and operation of cloud data centers, which form the backbone of cloud computing. Virtual machine (VM) placement has a significant impact on energy-efficiency improvement for virtualized data centers. Among various methods to solve the VM-placement problem, the genetic algorithm (GA) has been well accepted for the quality of its solution. However, GA is also computationally demanding, particularly in the computation of its fitness function. This limits its application in large-scale systems or specific scenarios where a fast VM-placement solution of good quality is required. Our analysis in this paper reveals that the execution time of the standard GA is mostly consumed in the computation of its fitness function. Therefore, this paper designs a data structure extended from a previous study to reduce the complexity of the fitness computation from quadratic to linear one with respect to the input size of the VM-placement problem. Incorporating with this data structure, an alternative fitness function is proposed to reduce the number of instructions significantly, further improving the execution-time performance of GA. Experimental studies show that our approach achieves 11 times acceleration of GA computation for energy-efficient VM placement in large-scale data centers with about 1500 physical machines in size.

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Simultaneous application assignment and virtual machine placement via ant colony optimization for energy-efficient enterprise data centers

Cited by 10 publications

References 29 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

Nature‐inspired virtual machine placement mechanisms: A systematic review

Accelerated computation of the genetic algorithm for energy-efficient virtual machine placement in data centers

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