Virtual machine placement strategies in cloud computing

Bharathi, P. Divya; Prakash, P.; Kiran, Mariam

doi:10.1109/ipact.2017.8244949

Cited by 14 publications

(4 citation statements)

References 7 publications

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“…In cloud computing, resources are only a click away which saves time. Cloud services are constrained by parameters such as Quality of Service (QoS), user deadlines, user budget, execution time, energy consumption Service Level Agreements (SLAs) and many more [ 1 , 2 ]. Different techniques have been opted to balance these parameters.…”

Section: Introductionmentioning

confidence: 99%

Energy efficient virtual machines placement in cloud datacenters using genetic algorithm and adaptive thresholds

Alourani,

Khalid,

Tahir

et al. 2024

PLoS ONE

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Cloud computing platform provides on-demand IT services to users and advanced the technology. The purpose of virtualization is to improve the utilization of resources and reduce power consumption. Energy consumption is a major issue faced by data centers management. Virtual machine placement is an effective technique used for this purpose. Different algorithms have been proposed for virtual machine placement in cloud environments. These algorithms have considered different parameters. It is obvious that improving one parameter affects other parameters. There is still a need to reduce energy consumption in cloud data centers. Data centers need solutions that reduce energy consumption without affecting other parameters. There is a need to device solutions to effectively utilize cloud resources and reduce energy consumption. In this article, we present an algorithm for Virtual Machines (VMs) placement in cloud computing. The algorithm uses adaptive thresholding to identify over utilized and underutilized hosts to reduce energy consumption and Service Level Agreement (SLA) violations. The algorithm is validated with simulations and comparative results are presented.

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Section: Introductionmentioning

confidence: 99%

Energy efficient virtual machines placement in cloud datacenters using genetic algorithm and adaptive thresholds

Alourani,

Khalid,

Tahir

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

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“…This model ensures that resources are readily accessible, saving valuable time. However, cloud services operate within specific constraints, for instance, quality of service (QoS), budget considerations, deadlines, energy consumption, execution period, and SLAs [1,2]. To balance these parameters effectively, various techniques are employed, one of which is virtual machine (VM) placement.…”

Section: Introductionmentioning

confidence: 99%

A Modified Genetic-Based solution for Power-Aware Placement of Virtual Machines

Panwar,

Rauthan,

Barthwal

2024

Preprint

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Cloud computing has developed as a ubiquitous technology for delivering services like storage, computing, etc. via the Internet. With the rising demand by customers for cloud computation and associated services, cloud service providers are developing various approaches that enhance the performance, reliability, and availability of cloud systems. Cloud computing uses virtualization to optimise resource usage and minimise power utilisation in data centers (DC). Efficient virtual machine (VM) placement strategies are crucial, especially when using advanced genetic techniques. This research paper introduces the use of a genetic meta-heuristic approach, named PowerGA, to optimise the integration of virtual machines (VM) onto the least number of physical machines (PMs) in cloud DCs. PowerGA optimises VM deployment in cloud DCs to minimise energy utilisation and Service Level Agreement (SLA) breaches, considering factors such as VM migration, host shutdown, overload count, and active physical machines. Extensive simulations using real workload data showed significant improvements over traditional strategies like PABFD, with PowerGA achieving a 25% reduction in energy consumption (EC), 43% fewer VM migrations, a 58% improvement in SLA violations, and a 72% reduction in host shutdowns over ten days of data from PlanetLab. These results highlight PowerGA's effectiveness in energy management and SLA enhancement, demonstrating the benefits of a meta-heuristic genetic algorithm in optimising VM placement for cloud computing efficiency.

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“…It uses software to make a computer work like a system of multiple computers (VMs). VMs can replace a physical computer server and other necessary physical resources as they have their own independent virtual resources [27].…”

Section: Introductionmentioning

confidence: 99%

Enhancing of Artificial Bee Colony Algorithm for Virtual Machine Scheduling and Load Balancing Problem in Cloud Computing

Kruekaew¹,

Kimpan²

2020

IJCIS

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This paper proposes the combination of Swarm Intelligence algorithm of artificial bee colony with heuristic scheduling algorithm, called Heuristic Task Scheduling with Artificial Bee Colony (HABC). This algorithm is applied to improve virtual machines scheduling solution for cloud computing within homogeneous and heterogeneous environments. It was introduced to minimize makespan and balance the loads. The scheduling performance of the cloud computing system with HABC was compared to that supplemented with other swarm intelligence algorithms: Ant Colony Optimization (ACO) with standard heuristic algorithm, Particle Swarm Optimization (PSO) with standard heuristic algorithm and improved PSO (IPSO) with standard heuristic algorithm. In our experiments, CloudSim was used to simulate systems that used different supplementing algorithms for the purpose of comparing their makespan and load balancing capability. The experimental results can be concluded that virtual machine scheduling management with artificial bee colony algorithm and largest job first (HABC_LJF) outperformed those with ACO, PSO, and IPSO.

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Virtual machine placement strategies in cloud computing

Cited by 14 publications

References 7 publications

Energy efficient virtual machines placement in cloud datacenters using genetic algorithm and adaptive thresholds

Energy efficient virtual machines placement in cloud datacenters using genetic algorithm and adaptive thresholds

A Modified Genetic-Based solution for Power-Aware Placement of Virtual Machines

Enhancing of Artificial Bee Colony Algorithm for Virtual Machine Scheduling and Load Balancing Problem in Cloud Computing

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