A many-objective optimized task allocation scheduling model in cloud computing

Xu, Jialei; Zhang, Zhixia; Hu, Zhaoming; Du, Lei; Cai, Xingjuan

doi:10.1007/s10489-020-01887-x

Cited by 27 publications

(15 citation statements)

References 49 publications

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“…Many research studies have been conducted to overcome the shortcoming of task scheduling in cloud data centres considering single and multiple objectives [16][17][18][19]. Tawfeek et al [20] proposed a novel task scheduling strategy based on the Ant Colony Optimisation (ACO) algorithm.…”

Section: Related Workmentioning

confidence: 99%

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MOTS‐ACO: An improved ant colony optimiser for multi‐objective task scheduling optimisation problem in cloud data centres

Elsedimy

Algarni

2022

IET Networks

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Task scheduling in cloud data centres is an optimisation problem that aims to minimise power consumption and task makespan as well as ensures the quality of service delivered to cloud consumers. Although there are several existing task scheduling approaches, these methods mainly focus on optimising makespans of tasks while ignoring critical issues. This paper presents a comprehensive multi-objective task scheduling model based on an improved Ant Colony Optimisation (ACO) algorithm, referred to as MOTS-ACO. In order to promote the diversity of the Pareto set and accelerate the convergence speed, adaptive distribution probability is incorporated into the proposed algorithm, specifically in the process of updating the global rule. The performance of MOTS-ACO is compared with several existing multi-objectives task scheduling algorithms based on the makespan time, turnaround time, power efficiency and load balancing parameters. The results show the superiority of MOTS-ACO in terms of the makespan time, turnaround time, power efficiency and load balancing. Moreover, the proposed MOTS-ACO algorithm introduces more diversity in the search and accelerates the convergence speed towards the Pareto optimal solution. K E Y W O R D S ACO, cloud computing, load balancing, makespan, power efficiency, task scheduling, turnaround time This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Related Workmentioning

confidence: 99%

“…After the completion of each iteration, MOTS-ACO updates both the local and global pheromones. Let τ i;j ðt þ 1Þ be the local pheromone, which is applied to all edges at iteration t + 1 updated by Equation (16).…”

Section: The Pheromone Update Rulementioning

confidence: 99%

MOTS‐ACO: An improved ant colony optimiser for multi‐objective task scheduling optimisation problem in cloud data centres

Elsedimy

Algarni

2022

IET Networks

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“…In [ 39 ], Xu et al proposed a new multiobjective task scheduling model to obtain a suitable task allocation strategy. Peng et al [ 40 ] proposed an online resource scheduling framework based on the deep Q-network (DQN) algorithm.…”

Section: Related Workmentioning

confidence: 99%

GCWOAS2: Multiobjective Task Scheduling Strategy Based on Gaussian Cloud‐Whale Optimization in Cloud Computing

Sun

et al. 2021

Computational Intelligence and Neuroscience

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An important challenge facing cloud computing is how to correctly and effectively handle and serve millions of users’ requests. Efficient task scheduling in cloud computing can intuitively affect the resource configuration and operating cost of the entire system. However, task and resource scheduling in a cloud computing environment is an NP-hard problem. In this paper, we propose a three-layer scheduling model based on whale-Gaussian cloud. In the second layer of the model, a whale optimization strategy based on the Gaussian cloud model (GCWOAS2) is used for multiobjective task scheduling in a cloud computing which is to minimize the completion time of the task via effectively utilizing the virtual machine resources and to keep the load balancing of each virtual machine, reducing the operating cost of the system. In the GCWOAS2 strategy, an opposition-based learning mechanism is first used to initialize the scheduling strategy to generate the optimal scheduling scheme. Then, an adaptive mobility factor is proposed to dynamically expand the search range. The whale optimization algorithm based on the Gaussian cloud model is proposed to enhance the randomness of search. Finally, a multiobjective task scheduling algorithm based on Gaussian whale-cloud optimization (GCWOA) is presented, so that the entire scheduling strategy can not only expand the search range but also jump out of the local maximum and obtain the global optimal scheduling strategy. Experimental results show that compared with other existing metaheuristic algorithms, our strategy can not only shorten the task completion time but also balance the load of virtual machine resources, and at the same time, it also has a better performance in resource utilization.

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“…The comparative study constructed by Cheng et al (2016) has validated that the RVEA demonstrates promising convergence and diversity on multiple benchmark tests. Furthermore, the effectiveness of the RVEA has been confirmed through real-life applications such as traveling salesman problem, car side impact problem (Dhiman and Kaur, 2019) and cloud computing (Xu et al, 2020). Nevertheless, on account of the no free lunch (NFL) theorem (Wolpert and Macready, 1997), no algorithms can guarantee good performance for addressing all optimization problems.…”

Section: Introductionmentioning

confidence: 98%

Synergetic energy-conscious scheduling optimization of part feeding systems via a novel chaotic reference-guided policy

Zhou

et al. 2022

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PurposeThis paper aims to investigate a multi-objective electric vehicle’s (EV’s) synergetic scheduling problem in the automotive industry, where a synergetic delivery mechanism to coordinate multiple EVs is proposed to fulfill part feeding tasks.Design/methodology/approachA chaotic reference-guided multi-objective evolutionary algorithm based on self-adaptive local search (CRMSL) is constructed to deal with the problem. The proposed CRMSL benefits from the combination of reference vectors guided evolutionary algorithm (RVEA) and chaotic search. A novel directional rank sorting procedure and a self-adaptive energy-efficient local search strategy are then incorporated into the framework of the CRMSL to obtain satisfactory computational performance.FindingsThe involvement of the chaotic search and self-adaptive energy-efficient local search strategy contributes to obtaining a stronger global and local search capability. The computational results demonstrate that the CRMSL achieves better performance than the other two well-known benchmark algorithms in terms of four performance metrics, which is inspiring for future researches on energy-efficient co-scheduling topics in manufacturing industries.Originality/valueThis research fully considers the cooperation and coordination of handling devices to reduce energy consumption, and an improved multi-objective evolutionary algorithm is creatively applied to solve the proposed engineering problem.

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A many-objective optimized task allocation scheduling model in cloud computing

Cited by 27 publications

References 49 publications

MOTS‐ACO: An improved ant colony optimiser for multi‐objective task scheduling optimisation problem in cloud data centres

MOTS‐ACO: An improved ant colony optimiser for multi‐objective task scheduling optimisation problem in cloud data centres

GCWOAS2: Multiobjective Task Scheduling Strategy Based on Gaussian Cloud‐Whale Optimization in Cloud Computing

Synergetic energy-conscious scheduling optimization of part feeding systems via a novel chaotic reference-guided policy

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