Task scheduling of cloud computing using integrated particle swarm algorithm and ant colony algorithm

Chen, Xuan; Da, Long

doi:10.1007/s10586-017-1479-y

Cited by 45 publications

(23 citation statements)

References 11 publications

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“…To further improve the accuracy and efficiency of the above described metaheuristics in cloud computing, some works have tried to propose hybrid methods to leverage the strengths of the existing ones. Chen et al [37] proposed a PSO-ACO method for task scheduling, showing it performs better than a standalone algorithm on makespan. To minimize task execution time, Liu et al [38] presented a algorithm that makes use of the global search capability of genetic algorithm, and then converts the achieved results into the initial pheromone of ACO for further optimization.…”

Section: Related Workmentioning

confidence: 99%

A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems

Chen

Cheng

Liu

et al. 2020

IEEE Systems Journal

Self Cite

212

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Task scheduling in cloud computing can directly affect the resource usage and operational cost of a system. To improve the efficiency of task executions in a cloud, various metaheuristic algorithms, as well as their variations, have been proposed to optimize the scheduling. In this work, for the first time, we apply the latest metaheuristics WOA (the whale optimization algorithm) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that basis, we propose an advanced approach called IWC (Improved WOA for Cloud task scheduling) to further improve the optimal solution search capability of the WOA-based method. We present the detailed implementation of IWC and our simulation-based experiments show that the proposed IWC has better convergence speed and accuracy in searching for the optimal task scheduling plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource utilization, in the presence of both small and large-scale tasks. Index Terms-Cloud computing; task scheduling; whale optimization algorithm; metaheuristics; multi-objective optimization I. INTRODUCTION W Ith the ubiquitous growth of Internet access and big data, cloud computing becomes more and more popular in today's business world [1]. Compared to other distributed computing techniques (e.g., cluster and grid computing), cloud computing has provided an elastic and scalable way on delivering services to consumers. Namely, consumers do not need to possess the underlying technology and they can make use of computing resources and platforms in a pay-per-use fashion [2], [3]. The basic mechanism of cloud computing is to dispatch computing tasks to a resource pooling constituting of a

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Section: Related Workmentioning

confidence: 99%

A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems

Chen

Cheng

Liu

et al. 2020

IEEE Systems Journal

Self Cite

212

View full text Add to dashboard Cite

show abstract

“…The above results show that using Artificial Bee Colony can effectively reduce energy consumption and save user costs. Literature [12] proposed the use of a hybrid Shuffled Frog Leaping Algorithm for resource and workflow scheduling in cloud computing; Literature [13] proposed a cloud computing task scheduling algorithm based on ACO and PSO. Experiments show that this algorithm can help improve the efficiency of cloud computing scheduling.…”

Section: Related Workmentioning

confidence: 99%

Cloud Computing Task Scheduling Model Based on Improved Whale Optimization Algorithm

Jia

Shi

2021

Wireless Communications and Mobile Computing

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The efficiency of task scheduling under cloud computing is related to the effectiveness of users. Aiming at the problems of long scheduling time, high cost consumption, and large virtual machine load in cloud computing task scheduling, an improved scheduling efficiency algorithm (called the improved whale optimization algorithm, referred to as IWC) is proposed. Firstly, a cloud computing task scheduling and distribution model with time, cost, and virtual machines as the main factors is constructed. Secondly, a feasible plan for each whale individual corresponding to cloud computing task scheduling is to find the best whale individual, which is the best feasible plan; in order to better find the optimal individual, we use the inertial weight strategy for the whale optimization algorithm to improve the local search ability and effectively prevent the algorithm from reaching premature convergence; we use the add operator and delete operator to screen individuals after each iteration which is completed and updated to improve the quality of understanding. In the simulation experiment, IWC was compared with the ant colony algorithm, particle swarm algorithm, and whale optimization algorithm under a different number of tasks. The results showed that the IWC algorithm has good results in terms of task scheduling time, scheduling cost, and virtual machine. The application is in cloud computing task scheduling.

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“…Based on the particle swarm optimization algorithm, Mapetu et al [31] designed a load balancing strategy for updating particle location, which has better performance in task scheduling and load balancing. Chen and Long [32] combine particle swarm algorithm and ant colony algorithm by adjusting the learning factors to optimize the task scheduling on fitness, cost, and operation cycle. Panda and Jana [33] focused on the problem of load balancing, and proposed a cloud task scheduling algorithm based on probability theory.…”

Section: Related Workmentioning

confidence: 99%

Many-Objective Cloud Task Scheduling

Geng

Wang

et al. 2020

IEEE Access

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Task scheduling problem refers to how to reasonably arrange many tasks provided by users in virtual machines, which is very important in the cloud computing. And the quality of the scheduling performance directly affects the customer satisfaction and the provider benefits. In order to describe the task scheduling problem of cloud computing more precisely and improve the scheduling performance. This paper establishes many-objective cloud model, including four objectives: minimizing time, minimizing costs, maximizing resource utilization, and balancing load. At the same time, a many-objective optimization algorithm based on hybrid angles (MaOEA-HA) is proposed to solve this model. Hybrid angle strategy is designed to optimize the algorithm better, which combines two angle strategies: individual-to-individual angle and individual-to-reference point angle. One by one elimination strategy was introduced to remain individuals with better performance. By comparing with five other advanced many-objective optimization algorithms, MaOEA-HA shows the best performance on the DTLZ test suite. Moreover, different algorithms are applied to solve the cloud task scheduling problem, and MaOEA-HA algorithm achieves best results.INDEX TERMS Cloud computing, hybrid-angle strategy, many-objective algorithm, task scheduling.

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Task scheduling of cloud computing using integrated particle swarm algorithm and ant colony algorithm

Cited by 45 publications

References 11 publications

A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems

A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems

Cloud Computing Task Scheduling Model Based on Improved Whale Optimization Algorithm

Many-Objective Cloud Task Scheduling

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