research-article

An improved list-based task scheduling algorithm for fog computing environment

Authors:

B. Lydia Elizabeth,

V. Rhymend UthariarajAuthors Info & Claims

Computing, Volume 103, Issue 7

Pages 1353 - 1389

https://doi.org/10.1007/s00607-021-00935-9

Published: 01 July 2021 Publication History

Abstract

A high-performance execution of programs predominately depends on the efficient scheduling of tasks. An application consists of a sequence of tasks that can be represented as a directed acyclic graph (DAG). The tasks in the DAG have precedence constraints between them and each task has a different timeline on different processors. In this paper, a new list-based scheduling algorithm is proposed which schedules the tasks which are represented as a DAG structure. The main focus of this algorithm is to schedule the tasks to the suitable processing node in fog environment as the fog nodes have limited processing capacity. The assignment of tasks on the fog node should consider both the computation cost of the node and the execution finishing time of the node. The proposed algorithm has three phases. (1) the level sorting phase, where the independent tasks are identified (2) in the Task prioritization phase the proposed algorithm assigns priority to the task which has more successors so that more tasks in the next level can start their execution and (3) in the task selection phase a balanced combination of local optimal and global optimal approach is considered to assign a task to a suitable processor which further enhances the processor selection phase results in minimizing both the makespan and overall computation cost of the processors. Extensive experiments are carried out using randomly generated graphs and graphs from the real-world to analyze the performance of the proposed algorithm. The results show that the proposed algorithm outperforms all other well-known algorithms like predict earliest finish time, heterogeneous earliest finish time algorithm, minimal optimistic processing time, and SDBBATS in terms of performance matrices like average scheduling length ratio, speedup, and makespan.

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Cited By

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Afzali MMohammad Vali Samani ANaji H(2024)An efficient resource allocation of IoT requests in hybrid fog–cloud environmentThe Journal of Supercomputing10.1007/s11227-023-05586-580:4(4600-4624)Online publication date: 1-Mar-2024
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Hussain MNabi SHussain M(2024)RAPTS: resource aware prioritized task scheduling technique in heterogeneous fog computing environmentCluster Computing10.1007/s10586-024-04612-227:9(13353-13377)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10586-024-04612-2
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An improved list-based task scheduling algorithm for fog computing environment
1. Theory of computation
  1. Design and analysis of algorithms
  2. Theory and algorithms for application domains

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Published In

cover image Computing

Computing Volume 103, Issue 7

Jul 2021

258 pages

ISSN:0010-485X

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2021.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 July 2021

Accepted: 02 March 2021

Received: 10 December 2019

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Cited By

Acharya BPanda SRay N(2024)Multiprocessor Task Scheduling Optimization for Cyber-Physical System Using an Improved Salp Swarm Optimization AlgorithmSN Computer Science10.1007/s42979-023-02517-25:1Online publication date: 8-Jan-2024
https://dl.acm.org/doi/10.1007/s42979-023-02517-2
Afzali MMohammad Vali Samani ANaji H(2024)An efficient resource allocation of IoT requests in hybrid fog–cloud environmentThe Journal of Supercomputing10.1007/s11227-023-05586-580:4(4600-4624)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s11227-023-05586-5
Hussain MNabi SHussain M(2024)RAPTS: resource aware prioritized task scheduling technique in heterogeneous fog computing environmentCluster Computing10.1007/s10586-024-04612-227:9(13353-13377)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10586-024-04612-2
Bansal SAggarwal H(2024)A multiobjective optimization of task workflow scheduling using hybridization of PSO and WOA algorithms in cloud-fog computingCluster Computing10.1007/s10586-024-04522-327:8(10921-10952)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s10586-024-04522-3
Khaledian NVoelp MAzizi SShirvani M(2024)AI-based & heuristic workflow scheduling in cloud and fog computing: a systematic reviewCluster Computing10.1007/s10586-024-04442-227:8(10265-10298)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s10586-024-04442-2
Hashemi SSahafi ARahmani ABohlouli M(2024)A new approach for service activation management in fog computing using Cat Swarm Optimization algorithmComputing10.1007/s00607-024-01302-0106:11(3537-3572)Online publication date: 4-Jul-2024
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Jeyaraj RBalasubramaniam AM.A. AGuizani NPaul A(2023)Resource Management in Cloud and Cloud-influenced Technologies for Internet of Things ApplicationsACM Computing Surveys10.1145/357172955:12(1-37)Online publication date: 2-Mar-2023
https://dl.acm.org/doi/10.1145/3571729
Yao YSong YHuang YNi WZhang D(2023)A Memory-Constraint-Aware List Scheduling Algorithm for Memory-Constraint Heterogeneous Muti-Processor SystemIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2022.322937334:4(1082-1099)Online publication date: 1-Apr-2023
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Beikzadeh Abbasi FRezaee AAdabi SMovaghar A(2023)Fault-tolerant scheduling of graph-based loads on fog/cloud environments with multi-level queues and LSTM-based workload predictionComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2023.109964235:COnline publication date: 1-Nov-2023
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Talha AMalki M(2023)PPTS-PSO: a new hybrid scheduling algorithm for scientific workflow in cloud environmentMultimedia Tools and Applications10.1007/s11042-023-14739-w82:21(33015-33038)Online publication date: 4-Mar-2023
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