Towards energy efficient cloud: an optimized ant colony model for virtual machine placement

Zhang, Liumei; Wang, Yichuan; Zhu, Lei; Ji, Wenjiang

doi:10.1007/bf03391585

Cited by 17 publications

(4 citation statements)

References 22 publications

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“…e algorithm obtained a set of Pareto sets to minimize the total waste of resources and energy consumption. Zhang et al [17] proposed a virtual machine layout optimization model based on an optimized ant colony algorithm based on virtual machine utilization parameters, multiobjective optimization strategies, and feature metrics, which solved redundant power consumption due to waste of idle resources of the physical machine problem.…”

Section: Related Workmentioning

confidence: 99%

Energy-Saving Virtual Machine Placement Method for User Experience in Cloud Environment

Pang

Kuangdi

Wang

et al. 2020

Mathematical Problems in Engineering

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Green computing focuses on the energy consumption to minimize costs and adverse environmental impacts in data centers. Improving the utilization of host computers is one of the main green cloud computing strategies to reduce energy consumption, but the high utilization of the host CPU can affect user experience, reduce the quality of service, and even lead to service-level agreement (SLA) violations. In addition, the ant colony algorithm performs well in finding suitable computing resources in unknown networks. In this paper, an energy-saving virtual machine placement method (UE-ACO) is proposed based on the improved ant colony algorithm to reduce the energy consumption and satisfy users’ experience, which achieves the balance between energy consumption and user experience in data centers. We improve the pheromone and heuristic factors of the traditional ant colony algorithm, which can guarantee that the improved algorithm can jump out of the local optimum and enter the global optimal, avoiding the premature maturity of the algorithm. Experimental results show that compared to the traditional ant colony algorithm, min-min algorithm, and round-robin algorithm, the proposed algorithm UE-ACO can save up to 20%, 24%, and 30% of energy consumption while satisfying user experience.

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Section: Related Workmentioning

confidence: 99%

Energy-Saving Virtual Machine Placement Method for User Experience in Cloud Environment

Pang

Kuangdi

Wang

et al. 2020

Mathematical Problems in Engineering

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“…In this work, the proposal achieves better resource utilisation in comparison with the first fit, best fit and worst fit strategies. A VM allocation model based on collective intelligence was introduced by Zhang et al [58]. This proposal considers the problem of redundant power consumption resulting from idle resource waste of physical machines.…”

Section: Evolutionary Algorithms In Cloud Design and Operationmentioning

confidence: 99%

MT-EA4Cloud: A Methodology For testing and optimising energy-aware cloud systems

Cañizares

Núñez

Lara

et al. 2020

Journal of Systems and Software

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Currently, using conventional techniques for checking and optimising the energy consumption in cloud systems is unpractical, due to the massive computational resources required. An appropriate test suite focusing on the parts of the cloud to be tested must be efficiently synthesised and executed, while the correctness of the test results must be checked. Additionally, alternative cloud configurations that optimise the energetic consumption of the cloud must be generated and analysed accordingly, which is challenging.To solve these issues we present MT-EA4Cloud, a formal approach to check the correctnessfrom an energy-aware point of view -of cloud systems and optimise their energy consumption. To make the checking of energy consumption practical, MT-EA4Cloud combines metamorphic testing, evolutionary algorithms and simulation. Metamorphic testing allows to formally model the underlying cloud infrastructure in the form of metamorphic relations. We use metamorphic testing to alleviate both the reliable test set problem, generating appropriate test suites focused on the features reflected in the metamorphic relations, and the oracle problem, using the metamorphic relations to check the generated results automatically. MT-EA4Cloud uses evolutionary algorithms to efficiently guide the search for optimising the energetic consumption of cloud systems, which can be calculated using different cloud simulators.

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“…In Zhang et al, a multiobjective combinatorial optimization method based on the ACO algorithm was proposed to optimize the total energy consumption and idle resources. Besides, an integration method to verify the desirability of current mapping is introduced.…”

Section: Multiobjective Vmp Mechanismsmentioning

confidence: 99%

Multiobjective virtual machine placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments: A comprehensive review

Vahed

Ghobaei‐Arani

Souri

2019

Int J Communication

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Cloud computing introduced a new paradigm in IT industry by providing ondemand, elastic, ubiquitous computing resources for users. In a virtualized cloud data center, there are a large number of physical machines (PMs) hosting different types of virtual machines (VMs). Unfortunately, the cloud data centers do not fully utilize their computing resources and cause a considerable amount of energy waste that has a great operational cost and dramatic impact on the environment. Server consolidation is one of the techniques that provide efficient use of physical resources by reducing the number of active servers. Since VM placement plays an important role in server consolidation, one of the main challenges in cloud data centers is an efficient mapping of VMs to PMs. Multiobjective VM placement is generating considerable interest among researchers and academia. This paper aims to represent a detailed review of the recent state-of-the-art multiobjective VM placement mechanisms using nature-inspired metaheuristic algorithms in cloud environments. Also, it gives special attention to the parameters and approaches used for placing VMs into PMs. In the end, we will discuss and explore further works that can be done in this area of research.KEYWORDS cloud computing, multiobjective optimization, nature-inspired metaheuristic algorithms, server consolidation, virtual machine placement | INTRODUCTIONCloud computing is an emerging paradigm in IT industry, which is mainly based on two important concepts including abstraction and virtualization. 1 As NIST 2 defined, this model provides on-demand, ubiquitous, rapid, and elastic access to shared resources, which is based on the pay-as-go model. Cloud services can appear into two distinct models: deployment models and service models. Cloud deployment models are based on where the cloud's infrastructure has deployed, and they can be classified into four categories: public clouds, private clouds, hybrid clouds, and community clouds. [3][4][5] On the other hand, the cloud service models are based on the services they provide. Generally, there are three common basic cloud services including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).

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Towards energy efficient cloud: an optimized ant colony model for virtual machine placement

Cited by 17 publications

References 22 publications

Energy-Saving Virtual Machine Placement Method for User Experience in Cloud Environment

Energy-Saving Virtual Machine Placement Method for User Experience in Cloud Environment

MT-EA4Cloud: A Methodology For testing and optimising energy-aware cloud systems

Multiobjective virtual machine placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments: A comprehensive review

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