Multi-objective dynamic management of virtual machines in cloud environments

Mollamotalebi, Mahdi; Hajireza, Shahnaz

doi:10.1186/s13677-017-0086-z

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…In consideration of this, recent research has been directed towards heuristic or metaheuristic algorithms ( Mollamotalebi & Hajireza, 2017 ; Chang et al, 2018 ; Satpathy et al, 2018 ; Masdari et al, 2019 ). The first fit decreasing algorithm ( Keller et al, 2012 ) places the PMs into a successive list and sorts the VMs in descending order according to their resource demand.…”

Section: Related Workmentioning

confidence: 99%

A multi-objective algorithm for virtual machine placement in cloud environments using a hybrid of particle swarm optimization and flower pollination optimization

Mejahed

Elshrkawey

2022

PeerJ Computer Science

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The demand for virtual machine requests has increased recently due to the growing number of users and applications. Therefore, virtual machine placement (VMP) is now critical for the provision of efficient resource management in cloud data centers. The VMP process considers the placement of a set of virtual machines onto a set of physical machines, in accordance with a set of criteria. The optimal solution for multi-objective VMP can be determined by using a fitness function that combines the objectives. This paper proposes a novel model to enhance the performance of the VMP decision-making process. Placement decisions are made based on a fitness function that combines three criteria: placement time, power consumption, and resource wastage. The proposed model aims to satisfy minimum values for the three objectives for placement onto all available physical machines. To optimize the VMP solution, the proposed fitness function was implemented using three optimization algorithms: particle swarm optimization with Lévy flight (PSOLF), flower pollination optimization (FPO), and a proposed hybrid algorithm (HPSOLF-FPO). Each algorithm was tested experimentally. The results of the comparative study between the three algorithms show that the hybrid algorithm has the strongest performance. Moreover, the proposed algorithm was tested against the bin packing best fit strategy. The results show that the proposed algorithm outperforms the best fit strategy in total server utilization.

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Section: Related Workmentioning

confidence: 99%

A multi-objective algorithm for virtual machine placement in cloud environments using a hybrid of particle swarm optimization and flower pollination optimization

Mejahed

Elshrkawey

2022

PeerJ Computer Science

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“…• The combination of the maximization of resource utilization and the minimization of energy consumption is the predominant approach [24,25,26,27,28]. This can be explained by the fact that these two objectives help reducing the DC operational costs [17].…”

Section: The Multi-objective Vmp Problemmentioning

confidence: 99%

Multi-objective optimization for VM placement in homogeneous and heterogeneous cloud service provider data centers

et al. 2021

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We address the Virtual Machine Placement (VMP) problem that arises in Cloud Service Providers data centers. We purpose, a Multi-Objective Integer Linear Programming model which aims at optimizing simultaneously the number of hosted Virtual Machines (VM), the resource wastage and the number of active Physical Machines (PM) in order to minimize power consumption. This new combination of objectives enables to maximize the client satisfaction rate with minimizing the Data Center (DC) operational costs. We modelize this problem with a multi-objective integer linear program and solve it through two different methods. The first method computes a unique solution for a given preference order over the objectives whereas the second computes a set of non-dominated solutions. Both methods are compared through extensive simulation scenarios. We consider two DC architectures: homogeneous DCs (i.e., a DC with PMs having the same amount of resources) and heterogeneous DCs. We study the impact of each DC configuration on the performances of the solutions. We show that the second method leads to solutions with a reduction of up to 30% over the number of used PMs and that the heterogeneous DCs outperforms the homogeneous one across all objectives.

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“…A dynamic VM management was presented by Mollamotalebi and Hajireza, which aimed to optimize energy consumption and SLA violations both simultaneously. If there is a change in data center's state, the biobjective algorithm checks data center's metrics like power efficiency and SLA violation metrics.…”

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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Multi-objective dynamic management of virtual machines in cloud environments

Cited by 9 publications

References 15 publications

A multi-objective algorithm for virtual machine placement in cloud environments using a hybrid of particle swarm optimization and flower pollination optimization

A multi-objective algorithm for virtual machine placement in cloud environments using a hybrid of particle swarm optimization and flower pollination optimization

Multi-objective optimization for VM placement in homogeneous and heterogeneous cloud service provider data centers

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

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