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A latency-aware task scheduling algorithm for allocating virtual machines in a cost-effective and time-sensitive fog-cloud architecture

Authors:

Seyedeh Samira Mohammadi,

Fariborz Jolai,

Reza Tavakkoli-MoghaddamAuthors Info & Claims

The Journal of Supercomputing, Volume 78, Issue 1

Pages 93 - 122

https://doi.org/10.1007/s11227-021-03868-4

Published: 01 January 2022 Publication History

Abstract

Recently, with the expansion of communications and generated data, the need for processing this high volume of data in minimum time and maximum speed has increased. Also, performing this volume of computing operations requires high processing and storage resources leading to hardware cost increment. In such systems, one of the most critical challenges is the task scheduling problem, which tries to find the optimal allocation for maximum resource usage and reduce the response time. Therefore, the purpose of this study is to design an infrastructure for smart home energy management with minimum hardware cost using cloud and fog computing and to propose a latency-aware scheduling algorithm based on virtual machine matching using meta-heuristics. Among heuristic methods, Tabu search makes it a common practice because of its high expansion in various optimization issues, as well as memory and high-speed features. Thereby, a novel algorithm based on the Tabu search is proposed that is improved using approximate nearest neighbor (ANN) and fruit fly optimization (FOA) algorithms. Finally, to validate the proposed method, a case study is simulated and the proposed algorithm is implemented considering target factors of execution time, latency, allocated memory, and cost function to illustrate the performance of the algorithm. The comparison results show that the proposed algorithm outperforms the Tabu search, genetic algorithm, PSO, and simulated annealing methods.

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Cited By

Satouf AHamidoğlu AGül ÖKuusik ADurak Ata LKadry S(2025)Metaheuristic-based task scheduling for latency-sensitive IoT applications in edge computingCluster Computing10.1007/s10586-024-04878-628:2Online publication date: 1-Apr-2025
https://dl.acm.org/doi/10.1007/s10586-024-04878-6
Al-Qadhi ALatip RChiong RAthauda RHussin M(2025)Independent task scheduling algorithms in fog environments from users’ and service providers’ perspectives: a systematic reviewCluster Computing10.1007/s10586-024-04771-228:3Online publication date: 1-Jun-2025
https://dl.acm.org/doi/10.1007/s10586-024-04771-2
Mahesar ALi XSajnani D(2025)Enhancing task scheduling and QoS optimization in mobile edge computing via microservice-oriented container selectionComputing10.1007/s00607-024-01410-x107:2Online publication date: 1-Feb-2025
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A latency-aware task scheduling algorithm for allocating virtual machines in a cost-effective and time-sensitive fog-cloud architecture

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Information & Contributors

Information

Published In

cover image The Journal of Supercomputing

The Journal of Supercomputing Volume 78, Issue 1

Jan 2022

1563 pages

ISSN:0920-8542

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 January 2022

Accepted: 05 May 2021

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Cited By

Satouf AHamidoğlu AGül ÖKuusik ADurak Ata LKadry S(2025)Metaheuristic-based task scheduling for latency-sensitive IoT applications in edge computingCluster Computing10.1007/s10586-024-04878-628:2Online publication date: 1-Apr-2025
https://dl.acm.org/doi/10.1007/s10586-024-04878-6
Al-Qadhi ALatip RChiong RAthauda RHussin M(2025)Independent task scheduling algorithms in fog environments from users’ and service providers’ perspectives: a systematic reviewCluster Computing10.1007/s10586-024-04771-228:3Online publication date: 1-Jun-2025
https://dl.acm.org/doi/10.1007/s10586-024-04771-2
Mahesar ALi XSajnani D(2025)Enhancing task scheduling and QoS optimization in mobile edge computing via microservice-oriented container selectionComputing10.1007/s00607-024-01410-x107:2Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1007/s00607-024-01410-x
Maashi MAlabdulkreem EMaray MShankar KDarem AAlzahrani AYaseen I(2024)Elevating Survivability in Next-Gen IoT-Fog-Cloud Networks: Scheduling Optimization With the Metaheuristic Mountain Gazelle AlgorithmIEEE Transactions on Consumer Electronics10.1109/TCE.2024.337177470:1(3802-3809)Online publication date: 8-Mar-2024
https://dl.acm.org/doi/10.1109/TCE.2024.3371774
Daghayeghi ANickray M(2024)Delay-Aware and Energy-Efficient Task Scheduling Using Strength Pareto Evolutionary Algorithm II in Fog-Cloud Computing ParadigmWireless Personal Communications: An International Journal10.1007/s11277-024-11510-8138:1(409-457)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s11277-024-11510-8
Chowdhary SRao A(2024)QoS and reliability aware matched bald eagle task scheduling framework based on IoT-cloud in educational applicationsCluster Computing10.1007/s10586-024-04415-527:6(8141-8158)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s10586-024-04415-5
Entrialgo JMoreno-Vozmediano RDíaz J(2024)Cost and response time optimization of edge architecturesCluster Computing10.1007/s10586-024-04359-w27:6(7757-7773)Online publication date: 1-Sep-2024
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Zuo H(2023)Towards optimal virtual machine placement methods in cloud environmentsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-22289644:5(8663-8696)Online publication date: 1-Jan-2023
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Vispute SVashisht P(2023)Energy-Efficient Task Scheduling in Fog Computing Based on Particle Swarm OptimizationSN Computer Science10.1007/s42979-022-01639-34:4Online publication date: 11-May-2023
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Mukherjee AGhosh SGhosh SBuyya R(2023)Mobi-Sense: mobility-aware sensor-fog paradigm for mission-critical applications using network coding and steganographyThe Journal of Supercomputing10.1007/s11227-023-05300-579:15(17495-17518)Online publication date: 8-May-2023
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