Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter

Madhumala, R. B.; Tiwari, Harshvardhan; C, Devaraj Verma

doi:10.2478/cait-2021-0005

Cited by 14 publications

(4 citation statements)

References 18 publications

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“…Heuristic algorithms are frequently used to find feasible solutions for the NP-hard VM placement problem. Many of these algorithms are based on bio-inspired, populationbased meta-heuristics [31,32]. For instance, particle swarm optimization uses four key components-initial position, velocity, weight, and fitness function-to determine its effectiveness.…”

Section: Selection Of Destination Server For Vm Migrationmentioning

confidence: 99%

Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing

Kushchazli,

Safargalieva,

Kochetkova

et al. 2024

Mathematics

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The advancement of cloud computing technologies has positioned virtual machine (VM) migration as a critical area of research, essential for optimizing resource management, bolstering fault tolerance, and ensuring uninterrupted service delivery. This paper offers an exhaustive analysis of VM migration processes within cloud infrastructures, examining various migration types, server load assessment methods, VM selection strategies, ideal migration timing, and target server determination criteria. We introduce a queuing theory-based model to scrutinize VM migration dynamics between servers in a cloud environment. By reinterpreting resource-centric migration mechanisms into a task-processing paradigm, we accommodate the stochastic nature of resource demands, characterized by random task arrivals and variable processing times. The model is specifically tailored to scenarios with two servers and three VMs. Through numerical examples, we elucidate several performance metrics: task blocking probability, average tasks processed by VMs, and average tasks managed by servers. Additionally, we examine the influence of task arrival rates and average task duration on these performance measures.

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Section: Selection Of Destination Server For Vm Migrationmentioning

confidence: 99%

Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing

Kushchazli,

Safargalieva,

Kochetkova

et al. 2024

Mathematics

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show abstract

“…However, the execution time of the algorithm was slightly longer due to the use of a genetic algorithm for finding the optimal solution. Madhumala et al 40 proposed algorithm uses the best Virtual Machine packing in active physical machines to reduce energy consumption. The algorithm used a dynamic fitness function that dynamically changed to accommodate noise as well as dynamically requested resources.…”

Section: Related Workmentioning

confidence: 99%

An energy‐efficient method of resource allocation based on request prediction in multiple cloud data centers

Chen

Wen

Wang

2023

Concurrency and Computation

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To meet the ever-increasing requirements of the applications, cloud service providers have further built and managed multiple cloud data centers in multiple regions across geographies with many physical machines (PMs). However, most of the existing resource allocation algorithms are developed for a single cloud data center, which normally cannot efficiently handle the load burst occasions where a single cloud data center may not be enough to satisfy the demands burst of applications. Therefore, it is necessary to consider how to efficiently manage multiple cloud data centers while meeting application requirements and reducing energy consumption. This paper first systematically analyzes multiple cloud data centers and energy consumption models.Then, an energy-efficient method of Resource Allocation based on Request Prediction in multiple cloud data centers (RARP) is proposed. The RARP method constructs a resource allocation framework based on request prediction in multiple cloud data centers, which anticipates the application request volume in advance. At the same time, the RARP method allocates VMs and PMs based on the principle of minimum remaining resources available to achieve minimum usage of PMs, thus minimizing energy consumption to complete application requests. Extensive experiments are conducted on the proposed RARP method through the simulation platform CloudSim. Finally, the experimental test results show that the accuracy of request detection and the energy consumption of cloud data centers are significantly better than those of the comparison algorithms.

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“…LA, cloud task scheduling, and utilizing several cloud platforms are things to be taken into consideration. Madhumala et al [101] Particle swarm optimization and a modified first fit decreasing algorithm were used to provide a method for determining the optimal configuration The suggested approach used less energy while efficiently allocating resources.…”

Section: Samriya Et Al[99]mentioning

confidence: 99%

A Comprehensive Review of Cloud Computing Virtual Machine Consolidation

Singh,

Walia

2023

IEEE Access

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In the last decade, users can access their applications, data, and services via the cloud from any location with an internet connection. The scale of heterogeneous cloud environments is continuously growing due to the development of computing-intensive smart devices. A data center is the central processing unit of any cloud environment, and it is made up of hardware-oriented machines known as Physical Machines (PMs) or server and software-oriented machines known as Virtual Machines (VMs). The deployment of a huge number of physical servers as a result of the exponential development in demand for cloud services has resulted in high energy consumption and ineffective resource usage. Efficient resource utilization and minimizing power consumption by any data center have become crucial challenges. Virtual machine consolidation(VMC) is a method of optimizing computing resources by consolidating multiple VMs onto a reduced number of PMs. By consolidating VMs and running fewer physical servers, VM consolidation can lead to reducing power consumption and efficient resource utilization. This review paper presents a comprehensive analysis of cloud computing virtual machine consolidation, exploring various strategies, benefits, challenges, and future trends in this domain. By examining a wide range of literature from the year 2015 to 2023, this review attempts to provide insight into the current state of VM consolidation and its possible effects on the performance and sustainability of cloud computing. The main flaw in the articles is that the various authors focused on different assessment metrics while the emphasis should have been on improving energy efficiency and quality of service for cloud systems. Future research can be aimed at developing a multiobjective system that emphasizes minimizing cloud energy usage without sacrificing service quality, preventing service level agreements with cloud users from being compromised.

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Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter

Cited by 14 publications

References 18 publications

Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing

Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing

An energy‐efficient method of resource allocation based on request prediction in multiple cloud data centers

A Comprehensive Review of Cloud Computing Virtual Machine Consolidation

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