Enhancing Cloud Services: Streamlining Performance Metrics and Cloudlet Scheduling DOI
D Gritto,

T S Vasughi

Published: Dec. 11, 2023

Enhancing cloud service is the primary goal of stakeholders. It a comprehensive that can be achieved through optimizing performance metrics, effective resource utilization, and prioritizing user satisfaction. These also called Quality Service (QoS) are mentioned in Level Agreement (SLA), contractual document. Optimizing experience requires continuous monitoring systems technologies such as virtualization, scheduling, migration, consolidation, load balancing, etc. Scheduling cloudlets, virtual machines, balancing crucial for achieving SLA enumerated QoS other key demands. In order to monitor evaluate effectiveness any knowledge on scheduling become imperative. This paper orchestrated identify essential metrics explore how algorithms enhance performance. Additionally, it seeks conduct comparative evaluation FCFS, SJF, Min-Min, Max-Min, RASA, Suffrage, TASA cloudlet using CloudSim. focuses including average waiting time, makespan, machine utilization ratio, balancing.

Language: Английский

Utilizing power consumption and SLA violations using dynamic VM consolidation in cloud data centers DOI Creative Commons
Umer Arshad, Muhammad Aleem, Gautam Srivastava

et al.

Renewable and Sustainable Energy Reviews, Journal Year: 2022, Volume and Issue: 167, P. 112782 - 112782

Published: July 27, 2022

Cloud Computing services can be accessed anytime, anywhere via the Internet. The overwhelming growth of cloud data centers over past decade has increased their costs as energy demands have risen. As a result, higher carbon dioxide emissions and other greenhouse gasses are putting strain on our ecosystem. main objective this study is to reduce power consumption in computing with no or negligible trade-offs quality service. This paper presents new algorithm called efficiency heuristic using virtual machine consolidation minimize high cloud. By setting two thresholds, hosts classified into three classes. designed model reallocates machines from one physical host another consumption. results proposed been obtained terms migrations, performance degradation caused by migration, service level agreement violations, execution time, showing significant improvement state-of-the-art techniques.

Language: Английский

Citations

81
Live virtual machine migration: A survey, research challenges, and future directions DOI
Muhammad Imran, Muhammad Ibrahim, Muhammad Salah ud din

et al.

Computers & Electrical Engineering, Journal Year: 2022, Volume and Issue: 103, P. 108297 - 108297

Published: Aug. 12, 2022

Language: Английский

Citations

29
Adaptive Computational Solutions to Energy Efficiency in Cloud Computing Environment Using VM Consolidation DOI Open Access
Bhagyalakshmi Magotra, Deepti Malhotra,

Amit Kr. Dogra

et al.

Archives of Computational Methods in Engineering, Journal Year: 2022, Volume and Issue: 30(3), P. 1789 - 1818

Published: Nov. 27, 2022

Language: Английский

Citations

25
Application of virtual machine consolidation in cloud computing systems DOI
Rahmat Zolfaghari, Amir Sahafi, Amir Masoud Rahmani

et al.

Sustainable Computing Informatics and Systems, Journal Year: 2021, Volume and Issue: 30, P. 100524 - 100524

Published: Feb. 11, 2021

Language: Английский

Citations

28
An Energy-Aware Approach to Virtual Machine Consolidation Using Classification and the Dragonfly Algorithm in Cloud Data Centers DOI Open Access

Nastaran Evaznia,

Reza Ebrahimi, Davoud Bahrepour

et al.

Journal of Information Systems and Telecommunication (JIST), Journal Year: 2025, Volume and Issue: 12(48), P. 280 - 290

Published: March 5, 2025

Language: Английский

Citations

0
SEEVMC: A secure, energy‐efficient virtual machine consolation approach for QoS in cloud data centers DOI Creative Commons
Muhammad Usman, Juhua Pu, Attique Ur Rehman

et al.

ETRI Journal, Journal Year: 2025, Volume and Issue: unknown

Published: March 18, 2025

Abstract Cloud computing faces challenges in energy consumption and quality of service (QoS). Virtual machine (VM) consolidation, involving relocation between hosts, helps reduce power usage enhance QoS. OpenStack Neat, a leading VM consolidation framework, uses the modified best‐fit decreasing (MBFD) strategy but QoS issues. To address these, we present secure efficient (SEEVMC) method, introducing unique host selection criterion based on incurred loss during placement. We evaluated SEEVMC with real‐time workload data from PlanetLab Materna over ten days using CloudSim. For PlanetLab, reduced by 78.33%, 57.74%, 19.57%, 6.30% system‐level agreement (SLA) violations 92.49%, 92.78%, 45.16%, 15.67%, compared MBFD, power‐aware best fit decreasing, medium power‐efficient bit decreasing. Materna, 14.12%, 59.5%, 3.92%, 3.80% fewer SLA 74.85%, 86.95%, 11.40%, 46.60%. also migrations time per active host, improving cloud efficiency.

Language: Английский

Citations

0
An energy‐aware virtual machines consolidation method for cloud computing: Simulation and verification DOI
Rahmat Zolfaghari, Amir Sahafi, Amir Masoud Rahmani

et al.

Software Practice and Experience, Journal Year: 2021, Volume and Issue: 52(1), P. 194 - 235

Published: June 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.

Language: Английский

Citations

25
An Energy-Aware Combinatorial Virtual Machine Allocation and Placement Model for Green Cloud Computing DOI Creative Commons
Mustafa Gamsiz, Ali Haydar Özer

IEEE Access, Journal Year: 2021, Volume and Issue: 9, P. 18625 - 18648

Published: Jan. 1, 2021

Resource allocation is an important problem for cloud environments. This paper introduces energy-aware combinatorial auction-based model the resource in clouds. The proposed allows users of a to submit their virtual requests as bids using provided bidding language which complementarities and substitutabilities among those resources be declared. finds most profitable mutually satisfiable set winning bids, corresponding while considering placement available physical by executing optimization problem. During optimization, also takes account non-linear energy requirements based on utilization levels find with lowest cost, thus, providing solution associated formally defined formulated integer programming. Since intractable, four heuristic methods are proposed. To evaluate performance methods, several experiments conducted comprehensive test suite. results demonstrate benefits model, high-quality solutions methods.

Language: Английский

Citations

21
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

et al.

IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 81787 - 81804

Published: Jan. 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.

Language: Английский

Citations

16
Cloud computing virtual machine consolidation based on stock trading forecast techniques DOI
Sergi Vila, Fernando Guirado, Josep L. Lérida

et al.

Future Generation Computer Systems, Journal Year: 2023, Volume and Issue: 145, P. 321 - 336

Published: March 15, 2023

Language: Английский

Citations

8