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

и другие.

Concurrency and Computation Practice and Experience, Год журнала: 2022, Номер 35(2)

Опубликована: Ноя. 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.

Язык: Английский

Detection of quality of service degradation on multi-tenant containerized services DOI
Pedro Horchulhack, Eduardo K. Viegas, Altair O. Santin

и другие.

Journal of Network and Computer Applications, Год журнала: 2024, Номер 224, С. 103839 - 103839

Опубликована: Фев. 1, 2024

Язык: Английский

Процитировано

17

Advancing green computing: Practices, strategies, and impact in modern software development for environmental sustainability DOI Creative Commons

Akoh Atadoga,

Uchenna Joseph Umoga,

Oluwaseun Augustine Lottu

и другие.

World Journal of Advanced Engineering Technology and Sciences, Год журнала: 2024, Номер 11(1), С. 220 - 230

Опубликована: Фев. 17, 2024

Advancing Green Computing: Practices, Strategies, and Impact in Modern Software Development for Environmental Sustainability explores the evolving landscape of green computing within realm software development, emphasizing imperative environmentally sustainable practices. In response to escalating environmental concerns, industry is undergoing a paradigm shift towards reducing its carbon footprint mitigating ecological impacts. This particularly crucial given pervasive influence on technological ecosystems. The review delves into multifaceted dimensions computing, elucidating various practices strategies that are instrumental fostering sustainability. From optimizing code efficiency embracing energy-efficient architectures, underscores diverse approaches available developers minimizing resource consumption emissions. Furthermore, it examines broader ramifications these practices, their potential reshape industry's contribute global efforts conservation. Moreover, highlights symbiotic relationship between modern development methodologies. It elucidates how principles such as agile DevOps can be synergistically integrated with enhance sustainability throughout lifecycle. By adopting an interdisciplinary approach integrates considerations design, deployment processes, organizations catalyze transformative changes greener ecosystem. also investigates tangible impact metrics. Through case studies empirical analyses, showcases efficacy energy consumption, emissions, electronic waste generation. Additionally, discusses economic societal benefits accrued from ranging cost savings enhanced corporate social responsibility. provides comprehensive overview context development. myriad opportunities challenges associated integrating

Язык: Английский

Процитировано

13

SPP: stochastic process-based placement for VM consolidation in cloud environments DOI Creative Commons
Somayeh Rahmani, Vahid Khajehvand, Mohsen Torabian

и другие.

Computing, Год журнала: 2025, Номер 107(1)

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

2

Scalable Virtual Machine Migration using Reinforcement Learning DOI
Abdul Rahman Hummaida, Norman W. Paton, Rizos Sakellariou

и другие.

Journal of Grid Computing, Год журнала: 2022, Номер 20(2)

Опубликована: Апрель 28, 2022

Язык: Английский

Процитировано

24

Machine Learning for Service Migration: A Survey DOI
Nassima Toumi, Miloud Bagaa,

Adlen Ksentini

и другие.

IEEE Communications Surveys & Tutorials, Год журнала: 2023, Номер 25(3), С. 1991 - 2020

Опубликована: Янв. 1, 2023

Future communication networks are envisioned to satisfy increasingly granular and dynamic requirements accommodate the application user demands. Indeed, novel immersive mission-critical services necessitate increased computing network resources, reduced latency, guaranteed reliability. Thus, efficient adaptive resource management schemes required provide maintain sufficient levels of Quality Experience (QoE) during service life-cycle. Service migration is considered a key enabler orchestration. moving on demand an mechanism for mobility support, load balancing in case fluctuations demands, hardware failure mitigation. However, requires planning, as multiple parameters must be optimized reduce disruption minimum. Recent breakthroughs computational capabilities allowed emergence Machine Learning tool decision making that expected enable seamless automation by predicting events learning optimal policies. This paper surveys contributions applying (ML) methods optimize migration, providing detailed literature review recent advances field establishing classification current research efforts with analysis their strengths limitations. Finally, provides insights main directions future research.

Язык: Английский

Процитировано

16

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

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

VM consolidation steps in cloud computing: A perspective review DOI

Seyyed Meysam Rozehkhani,

Farnaz Mahan, Witold Pedrycz

и другие.

Simulation Modelling Practice and Theory, Год журнала: 2024, Номер 138, С. 103034 - 103034

Опубликована: Ноя. 9, 2024

Язык: Английский

Процитировано

3

GWO-SA: Gray Wolf Optimization Algorithm for Service Activation Management in Fog Computing DOI
Sayed Mohsen Hashemi, Amir Sahafi, Amir Masoud Rahmani

и другие.

IEEE Access, Год журнала: 2022, Номер 10, С. 107846 - 107863

Опубликована: Янв. 1, 2022

Although fog computing is a new research topic, there are robust and integrated solutions for service activation management how to distribute IoT services over available resources. This paper presents multi-objective gray wolf optimization (GWO) solution more efficient scheduling management. utilizes function in the resource allocation process by developing improving proposed algorithm check status of resources manage tasks. The main purpose this study create trade-off between energy consumption task execution time. has presented two-stage approach solve offloading problem. First, GWO used problem, then container migration problem appropriate allocation. Container causes an idle physical server turn off, reducing power consumption, imbalance, latency, efficiency. method been compared with three scenarios 700 nodes, 1000 5000 nodes. implemented simulated iFogSim five classical algorithms. analytical results indicate better performance strategy average time host selection averages reduction 15, 20, 25, 21%, respectively, Particle swarm (PSO), Ant colony (ACO), Grasshopper (GOA), Genetic (GA), Cuckoo (COA), 12% required reallocation container, service-level agreement (SLA) violation rate maintained range 9-10/% all other solutions.

Язык: Английский

Процитировано

13

Optimization of Data and Energy Migrations in Mini Data Centers for Carbon-Neutral Computing DOI

Marcos De Melo da Silva,

Abdoulaye Gamatié, Gilles Sassatelli

и другие.

IEEE Transactions on Sustainable Computing, Год журнала: 2022, Номер 8(1), С. 68 - 81

Опубликована: Авг. 8, 2022

Due to large-scale applications and services, cloud computing infrastructures are experiencing an ever-increasing demand for resources. At the same time, overall power consumption of data centers has been rising beyond 1% worldwide electricity consumption. The usage renewable energy in contributes decreasing their carbon footprint costs. Several green-energy-aware resource allocation approaches have studied recently. None them takes advantage joint migration jobs xmlns:xlink="http://www.w3.org/1999/xlink">energy green increase efficiency. This paper presents optimization approach energy-efficient mini centers. observed momentum around edge makes design geographically distributed highly desirable. Our solution exploits both virtual machines (VMs) migrations between compute nodes These harvesting, storage, transport capabilities. They enable VMs across different nodes. Compared VM alone, joint-optimization reduces utility by up 22%. reduction can reach 28.5% system when integrating less servers. gains demonstrated using simulation a Mixed Integer Linear Programming formulation problem. Furthermore, we show how our sustaining old-generation efficient servers

Язык: Английский

Процитировано

12