A hierarchical decentralized architecture to enable adaptive scalable virtual machine migration DOI Creative Commons
Abdul Rahman Hummaida, Norman W. Paton, Rizos Sakellariou

et al.

Concurrency and Computation Practice and Experience, Journal Year: 2022, Volume and Issue: 35(2)

Published: Nov. 18, 2022

Abstract Cloud computing is an established paradigm for end users to access resources. infrastructure providers seek maximize accepted requests, meet Service Level Agreements (SLAs), and reduce operational costs by dynamically allocating Virtual Machines (VMs) physical nodes. Many solutions have been presented manage cloud infrastructure, however, these tend be centralized suffer in their ability maintain Quality of (QOS) support data centers with thousands Decentralized approaches, no central management, can large centers. However, the obtain optimal resource allocation across center. To address this, we propose a hybrid hierarchical decentralized architecture that achieves lower SLA violations lowers network traffic. We used simulation evaluate our proposal practice variety existing VM placement policies.

Language: Английский

VM consolidation steps in cloud computing: A perspective review DOI

Seyyed Meysam Rozehkhani,

Farnaz Mahan, Witold Pedrycz

et al.

Simulation Modelling Practice and Theory, Journal Year: 2024, Volume and Issue: 138, P. 103034 - 103034

Published: Nov. 9, 2024

Language: Английский

Citations

2
Energy-performance aware virtual machines migration in cloud network by using prediction and fuzzy approaches DOI
Rahmat Zolfaghari

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 131, P. 107825 - 107825

Published: Jan. 4, 2024

Language: Английский

Citations

1
An Efficient Virtual Machine Consolidation Algorithm for Cloud Computing DOI Creative Commons
Ling Yuan, Zhenjiang Wang, Ping Sun

et al.

Entropy, Journal Year: 2023, Volume and Issue: 25(2), P. 351 - 351

Published: Feb. 14, 2023

With the rapid development of integration in blockchain and IoT, virtual machine consolidation (VMC) has become a heated topic because it can effectively improve energy efficiency service quality cloud computing blockchain. The current VMC algorithm is not effective enough does regard load (VM) as an analyzed time series. Therefore, we proposed based on forecast to efficiency. First, migration VM selection strategy increment prediction called LIP. Combined with increment, this accuracy selecting from overloaded physical machines (PMs). Then, point sequence SIR. We merged VMs complementary series into same PM, improving stability PM load, thereby reducing level agreement violation (SLAV) number migrations due resource competition PM. Finally, better LIP experimental results show that our

Language: Английский

Citations

3
Confidence interval‐based overload avoidance algorithm for virtual machine placement DOI
Javad Ahmadi, Abolfazl Toroghi Haghighat, Amir Masoud Rahmani

et al.

Software Practice and Experience, Journal Year: 2022, Volume and Issue: 52(10), P. 2288 - 2311

Published: July 29, 2022

Abstract Virtualization plays an essential role in decreasing energy consumption and optimizing resource utilization by enabling the creation of virtual machines (VM) their consolidation through live migration. Excessive migrations a lack required VMs are two critical factors QoS degradation. The current approaches impose intensive time complexity cannot be used large data centers with hundreds hosts. This article proposes framework for dynamic divided into QoS‐aware algorithm overload avoidance power‐aware VM placement. To compute safe zone criterion any VM, relations were suggested applying interval estimate confidence level. By employing this criterion, offered could guarantee quality service (QoS), particularly specific VMs, while avoiding overhead. placement is developed based on maximum active It provides capability to control number hosts center manager. simulation results real workloads revealed that proposed decline amount level agreement violations 78% 74%, up 13% comparison best benchmark algorithms. Hence, application upgrades declines costs.

Language: Английский

Citations

5
PM2VE: Power Metering Model for Virtualization Environments in Cloud Data Centers DOI
Ziyu Shen, Xusheng Zhang, Zheng Liu

et al.

IEEE Transactions on Cloud Computing, Journal Year: 2023, Volume and Issue: 11(3), P. 3126 - 3138

Published: March 28, 2023

Virtualization technologies provide solutions for cloud computing. Virtual resource scheduling is a crucial task in data centers, and the power consumption of virtual resources critical foundation virtualization scheduling. Containers are smallest unit migration. Although many practical models estimating machines (VMs) have been proposed, few estimation containers put forth. In this paper, we propose fast-training piecewise regression model based on decision tree VM metering estimate configured by treating container as group processes VM. We select appropriate features from collected metrics VMs/containers to help our fit nonlinear relationship between well. Besides, optimize leaf nodes tree, realizing effective environments. evaluate proposed 13 tasks PARSEC compare it with several commonly used centers. The experimental results prove effectiveness model, estimated line expectations.

Language: Английский

Citations

2
An empirical investigation of task scheduling and VM consolidation schemes in cloud environment DOI
Sweta Singh,

Rakesh Kumar,

Dayashankar Singh

et al.

Computer Science Review, Journal Year: 2023, Volume and Issue: 50, P. 100583 - 100583

Published: Sept. 1, 2023

Language: Английский

Citations

2
Combination of Convolutional Neural Network and Gated Recurrent Unit for Energy Aware Resource Allocation DOI Creative Commons

Zeinab Khodaverdian,

Hossein Sadr, S. A. Edalatpanah

et al.

arXiv (Cornell University), Journal Year: 2021, Volume and Issue: unknown

Published: Jan. 1, 2021

Cloud computing service models have experienced rapid growth and inefficient resource usage is known as one of the greatest causes high energy consumption in cloud data centers. Resource allocation centers aiming to reduce has been conducted using live migration Virtual Machines (VMs) their consolidation into small number Physical (PMs). However, selection appropriate VM for an important challenge. To solve this issue, VMs can be classified according pattern user requests sensitive or insensitive classes latency, thereafter suitable selected migration. In paper, combination Convolution Neural Network (CNN) Gated Recurrent Unit (GRU) utilized classification Microsoft Azure dataset. Due fact majority dataset are labeled more group not only reduces but also decreases violation Service Level Agreements (SLA). Based on empirical results, proposed model obtained accuracy 95.18which clearly demonstrates superiority our compared other existing models.

Language: Английский

Citations

4
Energy-Aware and Proactive Host Load Detection in Virtual Machine Consolidation DOI Creative Commons
Seyed Yahya Zahedi Fard, Mohammad Karim Sohrabi, Vahid Ghods

et al.

Information Technology And Control, Journal Year: 2021, Volume and Issue: 50(2), P. 332 - 341

Published: June 17, 2021

With the expansion and enhancement of cloud data centers in recent years, increasing energy consumptionand costs users have become major concerns research area. Service quality parametersshould be guaranteed to meet demands cloud, support service providers,and reduce consumption centers. Therefore, center's resources must managedefficiently improve utilization. Using virtual machine (VM) consolidation technique is animportant approach enhance utilization computing. Since generally do not use all thepower a VM, VM on physical server improves andresource efficiency server, thus (QoS). In this article, serverthreshold prediction method proposed that focuses overload underload detectionto number migrations, which consequently theVM's QoS. integration problem very complex, exponential smoothing utilizedfor predicting The results experiments show goes beyondexisting methods terms power migrations.

Language: Английский

Citations

4
Server Consolidation Algorithms for Cloud Computing DOI

Hind Mikram,

Said El Kafhali, Youssef Saadi

et al.

International Journal of Cloud Applications and Computing, Journal Year: 2022, Volume and Issue: 12(1), P. 1 - 24

Published: Oct. 6, 2022

In recent years, companies and researchers have hosted rented computer resources over ‎the ‎‎internet due to cloud computing, which led an increase in the energy consumed by ‎data centers. This ‎‎consumption is considered one of world's highest, ‎which pushed many ‎researchers propose ‎several techniques such as server ‎consolidation (SC) solve the‎‏ ‏trade‏-‏off‏ ‏‏‎between saving ‎quality service ‎‎(QoS). SC requires maintaining level ‎agreements (SLA) violations ‎minimizing number active physical machines (PMs). ‎Furthermore, achieve this balance ‎‎avoid ‎increasing hardware costs, challenge targets ‎placing new virtual ‎‎(VMs) ‎suitable PMs. work explored existing algorithms ‎that include ‎CloudSim a simulator ‎environment PlanetLab dataset. The authors compared well-known optimization methods ‎and extracted weaknesses main three deployed ‎‎approaches involved consolidation ‎process: bin-packing model, metaheuristics, machine ‎learning-based solutions.‎

Language: Английский

Citations

3
Optimized resource allocation in cloud computing for enhanced performance with modified particle swarm optimization DOI Creative Commons
Sreenivasulu Gogula, P. Sridhar, S. Arvind

et al.

MATEC Web of Conferences, Journal Year: 2024, Volume and Issue: 392, P. 01140 - 01140

Published: Jan. 1, 2024

Cloud Computing (CC) offers abundant resources and diverse services for running a wide range of consumer applications, although it faces specific issues that need attention. customers aim to choose the most suitable resource fulfills requirements consumers at fair cost within an acceptable timeframe; however, times, they wind up paying more shorter duration. Many advanced algorithms focus on optimizing single variable individually. Hence, Optimized Resource Allocation in (ORA-CC) Model is required achieve equilibrium between opposing aims Computing. The ORA-CC study create task processing structure with decision-making ability best real-time handling complicated uses Virtual Computers (VC). It will utilize Modified Particle Swarm Optimization (MoPSO) method meet deadline set by user. fitness value calculated combining base enhanced estimation based algorithm robust arrangement. technique's effectiveness evaluated contrasting few current multi-objective restrictions applied machine scheduling strategies utilizing Cloudsim simulation. comparison demonstrates suggested strategy efficient allocation than other techniques.

Language: Английский

Citations

0