Optimizing virtual machine placement in IaaS data centers: taxonomy, review and open issues

Talebian, Hamid; Gani, Abdullah; Sookhak, Mehdi; Abdelatif, Ahmed Abdelaziz; Yousafzai, Abdullah; Vasilakos, Athanasios V.; Yu, F. Richard

doi:10.1007/s10586-019-02954-w

Cited by 40 publications

(21 citation statements)

References 127 publications

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“…A recent detailed work has been introduced to review the state-of-the-art multiobjective techniques based on meta-heuristic algorithms [52]. Moreover, another recent extensive survey has been devoted to study the VM placement with a wide exploration of single and multi-objective techniques [19].…”

Section: Vm Placementmentioning

confidence: 99%

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PAPSO: A Power-Aware VM Placement Technique Based on Particle Swarm Optimization

et al. 2020

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With the widespread usage of cloud computing to benefit from its services, cloud service providers have invested in constructing large scale data centers. Consequently, a tremendous increase in energy consumption has arisen in conjunction with its results, including a remarkable rise in costs of operating and cooling servers. Besides, increasing energy consumption has a significant impact on the environment due to emissions of carbon dioxide. Dynamic consolidation of Virtual Machines (VMs) into the minimal number of Physical Machines (PMs) is considered as one of the magic solutions to manage power consumption. The virtual machine placement problem is a critical issue for good VM consolidation. This paper proposes a Power-Aware technique depending on Particle Swarm Optimization (PAPSO) to determine the near-optimal placement for the migrated VMs. A discrete version of Particle Swarm Optimization (PSO) is adopted based on a decimal encoding to map the migrated VMs to the best appropriate PMs. Furthermore, an effective minimization fitness function is employed to reduce power consumption without violating the Service Level Agreement (SLA). Specifically, PAPSO consolidates the migrated VMs into the minimum number of PMs with a major constraint to decrease the number of overloaded hosts as much as possible. Therefore, the number of VM migrations can be reduced drastically by taking into consideration the main sources for VM migrations; overloaded hosts and underloaded ones. PAPSO is implemented in CloudSim and the experimental results on random workloads with different sizes of VMs and PMs show that PAPSO does not violate SLA and outperforms the Power-Aware Best Fit Decreasing algorithm (PABFD). It can reduce about 8.01%, 39.65%, 66.33%, and 11.87% on average in terms of consumed energy, number of VM migrations, number of host shutdowns and the combined metric Energy SLA Violation (ESV), respectively.

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Section: Vm Placementmentioning

confidence: 99%

“…It adopts PSO to benefit from its ability to search for the optimal solution locally FIGURE 1. An example for dynamic VM consolidation into a minimum number of servers [19].…”

Section: Introductionmentioning

confidence: 99%

PAPSO: A Power-Aware VM Placement Technique Based on Particle Swarm Optimization

et al. 2020

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“…It paid close consideration to the settings and methods utilized to place VMs into PMs. Additionally, Talebian et al 28 provided an in‐depth analysis of the VMP challenge and an outline of several techniques to solve the issue. The study's goal was to highlight existing VMP strategies' limitations and difficulties.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…The study's goal was to highlight existing VMP strategies' limitations and difficulties. Furthermore, Talebian et al 28 developed a VMP taxonomy based on methodology, operation mode, number of objectives, resource demand‐type, problem objectives, and cloud count. The most up‐to‐date VMP approaches were divided into multi‐objective and single‐objective categories, with several notable works discussed in each category.…”

Section: Related Work and Motivationmentioning

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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“…The ABC is based on honey bees' intelligent foraging behavior as they seek food sources; Dervis first presented this method for addressing real-world problems in 2005 [28]. ABC refers to a subsection of the swarm-intelligence based algorithms that address different optimization problems by imitating the honeybee swarms' collective intelligence [28]. A bee gathers food from a specific flower, or food source and a colony of bees develops where such bees cooperates to find better food sources [10].…”

Section: Proposed Modelmentioning

confidence: 99%

Hybrid Load Balancing Approach based on the Integration of QoS and Power Consumption in Cloud Computing

2021

IJATCSE

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Over the past few years, there has been keen research interest in load balancing and task scheduling in the cloud as the extensive amount of data that is stored in the server leads to significantly increased load. This can be resolved by using a hybrid algorithm in which the honeybee behavior algorithm’s advantages are integrated with fuzzy logic to conduct task scheduling and as well as balancing in the cloud. The design of this hybrid algorithm aims to enhance prior approaches. It is developed as per ABC and merges the important QoS factors along with power consumption so that the power that virtual machines (VMs) consume on the host can be precisely assessed, thereby ensuring efficient load balancing algorithm. The present study aims to evaluate the VMs’ power consumption by taking into account crucial QoS factors for selecting which host and virtual machine will be best suited for receiving the task. CloudSim was used to simulate the ILBA_HB algorithm. In terms of makespan, average response time, and degree of imbalance, the performance of the ILBA HB algorithm is compared to that of the LBA HB and HBB-LB algorithms. According to the results, the proposed algorithm outperformed LBA_HB and HBB-LB.

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Optimizing virtual machine placement in IaaS data centers: taxonomy, review and open issues

Cited by 40 publications

References 127 publications

PAPSO: A Power-Aware VM Placement Technique Based on Particle Swarm Optimization

PAPSO: A Power-Aware VM Placement Technique Based on Particle Swarm Optimization

Nature‐inspired virtual machine placement mechanisms: A systematic review

Hybrid Load Balancing Approach based on the Integration of QoS and Power Consumption in Cloud Computing

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