A modified PSO algorithm for task scheduling optimization in cloud computing

Zhou, Zhou; Chang, Jian; Hu, Zhigang; Yu, Junyang; Li, Fangmin

doi:10.1002/cpe.4970

Cited by 51 publications

(23 citation statements)

References 19 publications

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“…The VNPSO algorithm is based on repeated explorations of growing neighborhoods to identify better local optima using the so-called shaking strategy. Zhou et al 20 propose an improved PSO algorithm for cloud computing; task scheduling optimization is proposed. Unlike the PSO algorithm, the algorithm can dynamically adjust the inertia weight coefficient according to the number of iterations to improve the convergence rate.…”

Section: Related Workmentioning

confidence: 99%

K‐PSO: An improved PSO‐based container scheduling algorithm for big data applications

Liu

Lin

et al. 2020

Int J Network Mgmt

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In recent years, Docker container technology is being applied in the field of cloud computing at an explosive speed. The scheduling of Docker container resources has gradually become a research hotspot. Existing big data computing and storage platforms apply with traditional virtual machine technology, which often results in low resource utilization, a long time for flexible scaling and expanding clusters. In this paper, we propose an improved container scheduling algorithm for big data applications named Kubernetes-based particle swarm optimization(K-PSO). Experimental results show that the proposed K-PSO algorithm converges faster than the basic PSO algorithm, and the running time of the algorithm is cut in about half. The K-PSO container scheduling algorithm and algorithm experiment for big data applications are implemented in the Kubernetes container cloud system. Our experimental results show that the node resource utilization rate of the improved scheduling strategy based on K-PSO algorithm is about 20% higher than that of the Kube-scheduler default strategy, balanced QoS priority strategy, ESS strategy, and PSO strategy, while the average I/O performance and average computing performance of Hadoop cluster are not degraded.

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Section: Related Workmentioning

confidence: 99%

K‐PSO: An improved PSO‐based container scheduling algorithm for big data applications

Liu

Lin

et al. 2020

Int J Network Mgmt

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“…It gives the optimal solutions in task running time, cost and running time. Zhou, et al, [22] proposed a task scheduling algorithm which can be used to address the energy consumption problem in cloud computing. The proposed algorithm was simulated on the cloudsim.…”

Section: Ms Sudheer Drm Vamsi Krishna mentioning

confidence: 99%

“…This was simulated on the cloudsim and the proposed algorithm achieved a good results for the makespan, standard deviation and degree of imbalance when compared with the Honey bee and ant colony algorithms. PSO Algorithm Workflow Scheduling Mathematical Model --- [5] SA and PSO algorithms Task Scheduling Simulation Cloudsim [6] PSO algorithm Task Scheduling Simulation Cloudsim [7] PSO Algorithm Task Scheduling Simulation Cloudsim [8] PSO Algorithm Task Scheduling Simulation Cloudsim [9] Modified PSOAlgorithm Task Scheduling Simulation Cloudsim [10] MDAPSO algorithm Task Scheduling Simulation Cloudsim [11] LBMPSO algorithm Task Scheduling Simulation Cloudsim [12] PSO Algorithm Task Scheduing Mathematical Model ---- [13] Enhanced PSO algorithm, Task Scheduling Mathematical Model --- [14] BPSO Algorithm Workflow Scheduling Mathematical Model --- [15] GA-PSO Algorithm Workflow Scheduling Simulation Workflowsim [16] ACO and PSO Algorithms Workflow Scheduling Simulation Workflowsim [17] Modified PSO algorithm Workflow Scheduling Simulation Workflowsim [18] LDIW model Task Scheduling Simulation Cloudsim [19] PSO and GA Algorithms Task Scheduling Simulation Cloudsim [20] Symbiotic Organism Search Task Scheduling Simulation Cloudsim [21] Extended PSO Task Scheduling Simulation Cloudsim [22] modified PSO Task Scheduling Simulation Cloudsim [23] PSO Algorithm Task Scheduling Simulation Cloudsim [24] modified PSO Task Scheduling Real time implementation Amazon EC2 and Google cloud [25] GA and PSO Task Scheduling Mathematical Model --- [26] PSO and Hill Climbing Algorithms Task Scheduling Mathematical Model --- [27] Modified PSO Task Scheduling Mathematical Model --- [28] Modified PSO Task Scheduling Simulation Cloudsim [29] PSO Algorithm Task Scheduling Simulation Cloudsim [30] IPSO Algorithm Task Scheduling Simulation Cloudsim…”

Section: Ms Sudheer Drm Vamsi Krishna mentioning

confidence: 99%

Dynamic PSO for Task Scheduling Optimization in Cloud Computing

2019

IJRTE

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Task scheduling is still a challenge in cloud computing as no existing scheduling algorithms are not effectively provisioning and scheduling the resources in the cloud. Existing authors considered only metrics like makespan, execution time and turnaround time etc. and the previous authors concentrated only to optimize the above mentioned metrics. But no existing authors were considered about the effective provisioning of the resources in the cloud i.e, compute, storage and network capacities and still many resources in the cloud were underutilized. In this paper, we want to propose an algorithm which can effectively utilize the resources in the cloud by extending Particle Swarm Optimization by addressing the metrics Bandwidth utilization and Memory utilization particularly. We have simulated this algorithm by using cloudsim and compared the modified Dynamic PSO with the PSO algorithm and it outperforms in terms of Bandwidth and Memory utilization and the makespan is also optimized.

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“…Furthermore, several studies suggest that the reliability and cost should be considered when determining the optimal maintenance plan [36] and the availability for multi-period scheduling [37]. Recently, several PSO algorithms were proposed to improve standard PSO and handle some complicated multi-objective optimization problems [38,39].…”

Section: Introductionmentioning

confidence: 99%

Optimization and Selection of Maintenance Policies in an Electrical Gas Turbine Generator Based on the Hybrid Reliability-Centered Maintenance (RCM) Model

et al. 2020

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The electrical generation industry is looking for techniques to precisely determine the proper maintenance policy and schedule of their assets. Reliability-centered maintenance (RCM) is a methodology for choosing what maintenance activities have to be performed to keep the asset working within its designed function. Current developments in RCM models are struggling to solve the drawbacks of traditional RCM with regards to optimization and strategy selection; for instance, traditional RCM handles each failure mode individually with a simple yes or no safety question in which question has the possibility of major error and missing the effect of a combinational failure mode. Hence, in the present study, a hybrid RCM model was proposed to fill these gaps and find the optimal maintenance policies and scheduling by a combination of hybrid linguistic-failure mode and effect analysis (HL-FMEA), the co-evolutionary multi-objective particle swarm optimization (CMPSO) algorithm, an analytic network process (ANP), and developed maintenance decision tree (DMDT). To demonstrate the effectiveness and efficiencies of the proposed RCM model, a case study on the maintenance of an electrical generator was conducted at a Yemeni oil and gas processing plant. The results confirm that, compared with previous studies, the proposed model gave the optimal maintenance policies and scheduling for the electrical generator in a well-structured plan, economically and effectively.

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A modified PSO algorithm for task scheduling optimization in cloud computing

Cited by 51 publications

References 19 publications

K‐PSO: An improved PSO‐based container scheduling algorithm for big data applications

K‐PSO: An improved PSO‐based container scheduling algorithm for big data applications

Dynamic PSO for Task Scheduling Optimization in Cloud Computing

Optimization and Selection of Maintenance Policies in an Electrical Gas Turbine Generator Based on the Hybrid Reliability-Centered Maintenance (RCM) Model

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