Hybrid meta‐heuristic algorithm for optimal virtual machine placement and migration in cloud computing DOI
Niroshini Infantia Henry,

C. Anbuananth,

S. Kalarani

и другие.

Concurrency and Computation Practice and Experience, Год журнала: 2022, Номер 34(28)

Опубликована: Сен. 29, 2022

Summary Data sharing in cloud computing happens with multiple participants to freely distribute the group data, which focuses on advancing effectiveness of work cooperative backgrounds and has attained widespread benefits. The main intent this article is accomplish a virtual machines (VMs) placement migration model using hybrid meta‐heuristic concept. A new algorithm named DJ‐HA developed for optimal VM reduce count active servers, minimization makespan, energy consumption faster convergence rate background. Then, done based multi‐objective function concerning makespan same DJ‐HA. From result analysis, correspondingly secured at 4.3%, 3.5%, 31%, 33% more advanced than PSO, GWO, DHOA, JA, 100th iteration Experiment 1. Accordingly, cost suggested 88.8%, 89.4%, 33.3%, 50% increased JA 4. Hence, it proved that enriched other conventional algorithms.

Язык: Английский

Intelligent SLA-Aware VM Allocation and Energy Minimization Approach with EPO Algorithm for Cloud Computing Environment DOI Open Access
Jitendra Kumar Samriya, Subhash Chandra Patel, Manju Khurana

и другие.

Mathematical Problems in Engineering, Год журнала: 2021, Номер 2021, С. 1 - 13

Опубликована: Май 29, 2021

Cloud computing is the most prominent established framework; it offers access to resources and services based on large-scale distributed processing. An intensive management system required for cloud environment, should gather information about all phases of task processing ensuring fair resource provisioning through levels Quality Service (QoS). Virtual machine allocation a major issue in environment that contributes energy consumption asset utilization computing. Subsequently, this paper, multiobjective Emperor Penguin Optimization (EPO) algorithm proposed allocate virtual machines with power heterogeneous environment. The method analyzed make suitable data center Binary Gravity Search Algorithm (BGSA), Ant Colony (ACO), Particle Swarm (PSO). To compare other strategies, EPO energy-efficient there are significant differences. results have been evaluated JAVA simulation platform. exploratory outcome presents EPO-based very effective limiting consumption, SLA violation (SLAV), enlarging QoS requirements giving capable service.

Язык: Английский

Процитировано

26
An energy‐aware virtual machines consolidation method for cloud computing: Simulation and verification DOI
Rahmat Zolfaghari, Amir Sahafi, Amir Masoud Rahmani

и другие.

Software Practice and Experience, Год журнала: 2021, Номер 52(1), С. 194 - 235

Опубликована: Июнь 28, 2021

Abstract Cloud systems have become an essential part of our daily lives owing to various Internet‐based services. Consequently, their energy utilization has also a necessary concern in cloud computing increasingly. Live migration, including several virtual machines (VMs) packed on minimal physical (PMs) as consolidation (VMC) technique, is approach optimize power consumption. In this article, we proposed energy‐aware method for the VMC problem, which called (EVMC), consumption regarding quality service guarantee, comprises: (1) support vector machine classification based rate all resource PMs that used PM detection terms amount' load; (2) modified minimization migration VM selection; (3) particle swarm optimization implemented placement. Also, evaluation functional requirements presented by formal and non‐functional simulation. Finally, contrast standard greedy algorithms such best fit decreasing, EVMC decreases active VMs, respectively, 30%, 50% average. it more efficient 30% average, resources balance degree 15% average cloud.

Язык: Английский

Процитировано

25
An energy-aware virtual machine placement method in cloud data centers based on improved Harris Hawks optimization algorithm DOI

Zahra Karimi Mehrabadi,

Mehdi Fartash, Javad Akbari Torkestani

и другие.

Computing, Год журнала: 2025, Номер 107(6)

Опубликована: Май 23, 2025

Язык: Английский

Процитировано

0
An improved Wolf pack algorithm for optimization problems: Design and evaluation DOI Creative Commons
Xuan Chen, Feng Cheng, Cong Liu

и другие.

PLoS ONE, Год журнала: 2021, Номер 16(8), С. e0254239 - e0254239

Опубликована: Авг. 26, 2021

Wolf Pack Algorithm (WPA) is a swarm intelligence algorithm that simulates the food searching process of wolves. It widely used in various engineering optimization problems due to its global convergence and computational robustness. However, has some weaknesses such as low speed easily falling into local optimum. To tackle problems, we introduce an improved approach called OGL-WPA this work, based on employments Opposition-based learning Genetic with Levy's flight. Specifically, OGL-WPA, population wolves initialized by opposition-based maintain diversity initial during search. Meanwhile, leader wolf selected genetic avoid optimum round-up behavior optimized flight coordinate exploration development capabilities. We present detailed design our compare it other nature-inspired metaheuristic algorithms using classical test functions. The experimental results show proposed better search capability, especially presence multi-peak high-dimensional

Язык: Английский

Процитировано

22
SLA-Aware and Energy-Efficient Virtual Machine Placement and Consolidation in Heterogeneous DVFS Enabled Cloud Datacenter DOI Creative Commons

Badieh Nikzad,

Behnam Barzegar, Homayun Motameni

и другие.

IEEE Access, Год журнала: 2022, Номер 10, С. 81787 - 81804

Опубликована: Янв. 1, 2022

Increasing demand for computational resource as services over the internet has led to expansion of datacenter infrastructures. Thus, authorities are striving adopt optimal power usage schemes minimize costs, emissions and Service Level Agreement (SLA) violations in their task scheduling heterogeneous computation centers. One most effective strategies reduce energy consumption is maximize utilization physical machines shut down idle ones. This can be realized through two main algorithms, namely virtual machine placement consolidation. The VM method a dynamic process put these devices on machines. consolidation technique, however, tries improve efficiency grouping live migration dispersed lower number active machine. In this paper, novel approach proposed improving efficiency. employs heuristics meta-heuristic algorithms with eight performance criteria implemented small medium scale data centers using simulated cloud module. results indicates that showed up 10.3%, 5.3%, 12.5% more significant rather best previous respectively, terms consumption, SLA violation VMs migration.

Язык: Английский

Процитировано

16
A Comprehensive Review of Cloud Computing Virtual Machine Consolidation DOI Creative Commons
Jaspreet Singh, Navpreet Kaur Walia

IEEE Access, Год журнала: 2023, Номер 11, С. 106190 - 106209

Опубликована: Янв. 1, 2023

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

Язык: Английский

Процитировано

9
A Hybrid Discrete Grey Wolf Optimization Algorithm Imbalance-ness Aware for Solving Two-dimensional Bin-packing Problems DOI
Saeed Kosari, Mirsaeid Hosseini Shirvani, Navid Khaledian

и другие.

Journal of Grid Computing, Год журнала: 2024, Номер 22(2)

Опубликована: Май 10, 2024

Язык: Английский

Процитировано

3
Developing green computing awareness based on optimization techniques for environmental sustainability DOI

A. Arivoli,

B. Kalaavathi,

Chen Joy Iong-Zong

и другие.

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 237 - 258

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
An Intelligent Approach for Cloud Infrastructure With Improved Multi‐Objective Graywolf Optimization and Resource Allocation for Dynamic Virtual Machine Placement DOI

S. Shankar,

M. Anbarasan

Transactions on Emerging Telecommunications Technologies, Год журнала: 2025, Номер 36(6)

Опубликована: Май 20, 2025

ABSTRACT Cloud infrastructure plays a pivotal role in modern computing, yet its optimization and resource allocation often lead to significant delays power inefficiencies. This research presents an Intelligent Approach for Infrastructure utilizing Improved multi‐objective gray Wolf Optimization Dynamic Virtual Machine Placement (ICIMRAD). By mimicking the hierarchical structure hunting strategies of Gray wolves, Multi‐objective (IMGWO) algorithm, combined with Genetic Algorithms, effectively enhances accuracy virtual machine placement allocation. The Fuzzy Group Algorithm (FGGA) also addresses complex scheduling challenges, facilitating efficient decision‐making across multiple objectives. dynamic system model operates within Xen environment monitor consumption without affecting guest operating systems. Through extensive simulations, proposed ICIMRAD approach significantly improves metrics such as consumption, achieving reductions 0.58 kWh 50 VMs, overall performance compared traditional methods (e.g., SHOANN, CRASVM, MOOERA). underlying philosophy emphasizes powerful synergy between evolutionary fuzzy logic drive sustainable cloud management.

Язык: Английский

Процитировано

0
Intra-Balance Virtual Machine Placement for Effective Reduction in Energy Consumption and SLA Violation DOI Creative Commons
Ammar Al-Moalmi, Juan Luo, Zhuo Tang

и другие.

IEEE Access, Год журнала: 2019, Номер 7, С. 72387 - 72402

Опубликована: Янв. 1, 2019

Cloud computing has emerged as one of the most important technological revolutions globally. However, rapid growth cloud imposed a massive financial burden and resulted in environmental side effects due to excessive energy consumption. The high power consumption is not only attributed size data centers but also ineptitude resource usage. Most extant research focused on reducing by an aggressive VM consolidation, which leads violation service level agreement (SLA). Furthermore, unbalanced exacerbates unavailable wasted resources that are referred fragmentation. In this paper, we propose use balanced algorithm called BRC-IBMMT order enhance efficiency while achieving acceptable balance between conflicting correlation objectives well SLA violation. extensive simulation results different types workload validate lend credence significance proposed method center.

Язык: Английский

Процитировано

23