Efficient job scheduling paradigm based on hybrid sparrow search algorithm and differential evolution optimization for heterogeneous cloud computing platforms

Khaleel, Mustafa Ibrahim

doi:10.1016/j.iot.2023.100697

Cited by 25 publications

(15 citation statements)

References 44 publications

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“… Balances the demand and reduces resource loss Efficient resource forming Requires GEO distributed mobility support. Low latency and load balance [ 164 ] Energy management in remote IoT applications. Deep Q network-based flow work scheduling algorithm.…”

Section: State Of Art Meh System For Iot Devicesmentioning

confidence: 99%

Micro energy harvesting for IoT platform: Review analysis toward future research opportunities

Sarker,

Riaz,

Lipu

et al. 2024

Heliyon

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Section: State Of Art Meh System For Iot Devicesmentioning

confidence: 99%

Micro energy harvesting for IoT platform: Review analysis toward future research opportunities

Sarker,

Riaz,

Lipu

et al. 2024

Heliyon

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“…A combined method of the cellular automata and the bat algorithm (BatCL) was presented in paper [28] which aimed to reduce the cost and time of completion of tasks. Paper [29] proposes a hybrid approach for job scheduling in cloud computing, utilizing a combination of sparrow search algorithm and differential evolution optimization. A combination of the genetic algorithm and the gravitational emulation local search is presented in [30] for task scheduling in the cloud.…”

Section: -Related Workmentioning

confidence: 99%

TPALA: Two Phase Adaptive Algorithm based on Learning Automata for job scheduling in cloud Environment

Esfandi,

Akbari Torkestani,

Karimi

et al. 2024

jist

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Due to the completely random and dynamic nature of the cloud environment, as well as the high volume of jobs, one of the significant challenges in this environment is proper online job scheduling. Most of the algorithms are presented based on heuristic and meta-heuristic approaches, which result in their inability to adapt to the dynamic nature of resources and cloud conditions. In this paper, we present a distributed online algorithm with the use of two different learning automata for each scheduler to schedule the jobs optimally. In this algorithm, the placed workload on every virtual machine is proportional to its computational capacity and changes with time based on the cloud and submitted job conditions. In proposed algorithm, two separate phases and two different LA are used to schedule jobs and allocate each job to the appropriate VM, so that a two phase adaptive algorithm based on LA is presented called TPALA. To demonstrate the effectiveness of our method, several scenarios have been simulated by CloudSim, in which several main metrics such as makespan, success rate, average waiting time, and degree of imbalance will be checked plus their comparison with other existing algorithms. The results show that TPALA performs at least 4.5% better than the closest measured algorithm.

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“…The results confirm minimized energy consumption and makespan with maximized fitness function during the TS process. Khaleel 37 has proposed a Hybrid DE and Sparrow Search Algorithm‐based TS (HDESSATS) algorithm for reducing the degree of energy consumption which helps in achieving minimized resource leakage and better load balance in clouds. The algorithm utilizes a dual‐phase meta‐heuristic algorithm that helps in performing clustering such that the collection of computing nodes can be better organized in the environment.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Guided Differential Evolution‐based Slime Mould Algorithm‐based efficient Multi‐objective Task Scheduling for Cloud Computing Environments

Nithiavathy,

Janakiraman,

Deva Priya

2023

Trans Emerging Tel Tech

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In a Cloud Computing (CC) environment, users are charged based on on‐demand resource utilization and expected Quality of Service (QoS). Multi‐Objective‐based Task Scheduling (MOTS) problem formulated for achieving the expected QoS in a CC scenario is always NP‐complete. The majority of existing Task Scheduling (TS) algorithms are not efficient in determining global optimal solutions due to huge space offered by large‐scale problem instances and characteristics inherent in NP‐complete problems. In this article, an Adaptive Guided Differential Evolution‐based Slime Mould Algorithm (AGDESMA) is proposed for dealing with the problem of Multi‐Objective large‐scale TS optimization over Infrastructure‐as‐a‐Service (IaaS) CC environment. The proposed AGDESMA is formulated with the merits of Slime Mould Algorithm (SMA) and Adaptive Guided Differential Evolution (AGDE) for achieving a proper balance amid exploitation and exploration phases and preventing the solutions from getting trapped into minimum local regions. It specifically adopts the method of AGDE for preventing premature convergence with maximized diversity of population and local search of swarm agents. The experimental validation of the proposed AGDESMA is conducted using CloudSim based on the synthesized and standard workload traces with larger instances up to a maximum of 5000. The results confirm the predominance of the proposed AGDESMA scheme over benchmarked schemes based on computational overhead and trade‐off between makespan and financial cost.

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Efficient job scheduling paradigm based on hybrid sparrow search algorithm and differential evolution optimization for heterogeneous cloud computing platforms

Cited by 25 publications

References 44 publications

Micro energy harvesting for IoT platform: Review analysis toward future research opportunities

Micro energy harvesting for IoT platform: Review analysis toward future research opportunities

TPALA: Two Phase Adaptive Algorithm based on Learning Automata for job scheduling in cloud Environment

Adaptive Guided Differential Evolution‐based Slime Mould Algorithm‐based efficient Multi‐objective Task Scheduling for Cloud Computing Environments

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