TOPSIS–PSO inspired non-preemptive tasks scheduling algorithm in cloud environment

Panwar, Neelam; Negi, Sarita; Rauthan, Man Mohan Singh; Vaisla, Kunwar Singh

doi:10.1007/s10586-019-02915-3

Cited by 39 publications

(20 citation statements)

References 23 publications

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“…According to the simulation results, the MPSO method gave a better performance than Max‐Min, standard PSO, and Min‐Min scheduling methods. In Panwar et al, 24 the authors proposed the technique of order precedence by similarity of ideal solution (TOPSIS‐PSO) algorithm. The proposed algorithm works on two stages.…”

Section: Related Workmentioning

confidence: 99%

Hybrid swarm optimization algorithm based on task scheduling in a cloud environment

Eldesokey

El-atty

El‐Shafai

et al. 2021

Int J Communication

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Summary Cloud computing is the current computing standard, which provides information technology (IT) services over the Internet on demand. In the cloud environment, a task is mapped with an available resource to attain a good result. Task scheduling is the technique that is used to allocate tasks on virtual machines (VMs) of a server based on its capacity of workload. Tasks are scheduled to the server in such a way to minimize traffic and time delay. Particle swarm optimization (PSO) is the best existing algorithm used to schedule a task to an existing resource on the environment of the cloud. By PSO, the task is scheduled for an existing resource to reduce computational cost. In this paper, a hybrid swarm optimization (HSO) algorithm, which is the combination of PSO and salp swarm optimization (SSO), is proposed to resolve task scheduling issues in the cloud environment. The main goal of HSO is to schedule the task to the available resource in such a way to reduce the execution time and computation cost. Multilayer logistic regression (MLR) is an approach used to detect the overloaded VMs, so that a task can be scheduled to a VM according to its capacity of workload. The proposed HSO algorithm with MLR is simulated on the cloudsim toolkit, and the results reveal the efficiency of the proposed algorithm in terms of cost, execution time, and makespan. Compared to the existing algorithms such as the genetic algorithms (GAs), the improved efficiency evolution (IDEA), and the PSO, the proposed algorithm reveals superiority in terms of efficiency, resource utilization, and speed.

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Section: Related Workmentioning

confidence: 99%

Hybrid swarm optimization algorithm based on task scheduling in a cloud environment

Eldesokey

El-atty

El‐Shafai

et al. 2021

Int J Communication

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“…Dileep Varapravathu Kunhambu et al [10] first screened the service application hierarchical clustering algorithm, and then used the PSO algorithm for processing, thereby ameliorating the efficiency of the PSO algorithm. Considering the different influences of various factors in the optimization process, Neelam Panwar et al [11] built the TOPSIS-PSO, a cloud computing resource scheduling algorithm based on a combination of TOPSIS and PSO. This scheduling method uses the TOPSIS algorithm to comprehensively evaluate the execution time, completion time and transmission time of the scheduling strategy solved by the PSO, so as to solve the multi-objective scheduling problem in the cloud environment.…”

Section: Related Workmentioning

confidence: 99%

“…The total transfer time is the longest transfer time in each computing resource. Calculation of the transmission time is performed in equation (11).…”

Section: ) Task Completion Timementioning

confidence: 99%

Collaborative Optimization Scheduling of Cloud Service Resources Based on Improved Genetic Algorithm

Wang

2020

IEEE Access

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With the wide application of cloud computing technology, the services provided by cloud systems have become increasingly diverse, thus these systems are required to solve tasks of high variety and complexity, with a tremendously extensive amount of task data involved. That is why reasonable scheduling system resources are particularly important in cloud computing research. In this paper, a cloud computing system needs to take into account a wider range of cloud service resource types and collaborative optimization scheduling issues in order to solve the tasks at hand. Firstly, a new adaptive genetic algorithm (NAGA) was proposed. By improving the crossover mutation genetic operator, the algorithm was able to save excellent individuals as much as possible, enhance the algorithm's optimization ability, and greatly reduce the probability of the algorithm falling into the local optimal solution. Secondly, focusing on the main factors affecting service quality, such as task completion time, system load, and network bandwidth, an upgraded fitness operator method for the cloud resource collaborative optimization scheduling problem is set forth. Finally, an algorithm of cloud service resources based on an improved genetic algorithm (OSIG) is proposed. Experiments on the CloudSim cloud computing simulation platform demonstrate that the OSIG algorithm proposed in this paper can effectively optimize the resource scheduling strategy, shorten the task completion time, facilitate the system load balancing, and boost the system's service quality. The theoretical analysis was consistent with the experimental results.

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“…Panwar et al proposed a hybrid precedence‐based similarity task scheduling approach using PSO algorithm to minimize execution and transmission time of the resources in cloud computing.…”

Section: Related Workmentioning

confidence: 99%

An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

Ghobaei‐Arani

Souri

Safara

et al. 2019

Trans Emerging Tel Tech

145

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With the rapid development of in modern technologies has led to a novel generation of cyberspace equipment and secure analysis methods such as cyber‐physical system (CPS). The CPS system refers to an intelligent dependable system of embedded smart devices used to monitor and control the cyberspace conditions in a fog computing environment. The fog computing environment connects resources even human beings together to enhance the quality of life through performing of CPS applications using computation resources at the network edge. The optimization of task scheduling for the CSP applications as a challenging issue has been considered as the NP‐hard problem in fog computing environment which is not trivial. On the one hand, reasonable tasks scheduling can increase resource utilization, which can avoid less idle resource. This paper presents a task scheduling algorithm based on moth‐flame optimization algorithm to assign an optimal set of tasks to fog nodes to meet the satisfaction of quality of service requirements of CPS applications in such a way that the total execution time of tasks is minimized. The minimization of task execution and transfer time in the proposed algorithm are considered as objective functions. The experimental testing of the proposed solution is carried out in iFogSim toolkit. As a result of the simulation based on proposed algorithm found, the optimal solution for the scheduling of tasks and equal distribution of tasks to fog nodes has been provided, and less total execution time consumption has been achieved compared with other algorithms.

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TOPSIS–PSO inspired non-preemptive tasks scheduling algorithm in cloud environment

Cited by 39 publications

References 23 publications

Hybrid swarm optimization algorithm based on task scheduling in a cloud environment

Hybrid swarm optimization algorithm based on task scheduling in a cloud environment

Collaborative Optimization Scheduling of Cloud Service Resources Based on Improved Genetic Algorithm

An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

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