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IoT 24(1):

Editorial

A Systematic Review on Various Task Scheduling Algorithms in Cloud Computing

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  • @ARTICLE{10.4108/eetiot.4548,
        author={Mallu Shiva Rama Krishna and Sudheer Mangalampalli},
        title={A Systematic Review on Various Task Scheduling Algorithms in Cloud Computing},
        journal={EAI Endorsed Transactions on Internet of Things},
        volume={10},
        number={1},
        publisher={EAI},
        journal_a={IOT},
        year={2023},
        month={12},
        keywords={Task Schedduling, Machine Learning, Cloud Computing, Nature-inspired algorithms},
        doi={10.4108/eetiot.4548}
    }
    
  • Mallu Shiva Rama Krishna
    Sudheer Mangalampalli
    Year: 2023
    A Systematic Review on Various Task Scheduling Algorithms in Cloud Computing
    IOT
    EAI
    DOI: 10.4108/eetiot.4548
Mallu Shiva Rama Krishna1, Sudheer Mangalampalli1,*
  • 1: Vellore Institute of Technology University
*Contact email: sudheerkietmtech@gmail.com

Abstract

Task scheduling in cloud computing involves allocating tasks to virtual machines based on factors such as node availability, processing power, memory, and network connectivity. In task scheduling, we have various scheduling algorithms that are nature-inspired, bio-inspired, and metaheuristic, but we still have latency issues because it is an NP-hard problem. This paper reviews the existing task scheduling algorithms modelled by metaheuristics, nature-inspired algorithms, and machine learning, which address various scheduling parameters like cost, response time, energy consumption, quality of services, execution time, resource utilization, makespan, and throughput, but do not address parameters like trust or fault tolerance. Trust and fault tolerance have an impact on task scheduling; trust is necessary for tasks and assigning responsibility to systems, while fault tolerance ensures that the system can continue to operate even when failures occur. A balance of trust and fault tolerance gives a quality of service and efficient task scheduling; therefore, this paper has analysed parameters like trust and fault tolerance and given research directions.

Keywords
Task Schedduling, Machine Learning, Cloud Computing, Nature-inspired algorithms
Received
2023-09-14
Accepted
2023-11-19
Published
2023-12-06
Publisher
EAI
http://dx.doi.org/10.4108/eetiot.4548

Copyright © 2023 M. S. Rama Krishna et al., licensed to EAI. This is an open access article distributed under the terms of the CC BYNC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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