Algorithm to improve job scheduling problem in cloud computing environment

Tareghian, Shahab; Bornaee, Zarrintaj

doi:10.1109/kbei.2015.7436126

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…In earlier research work particularly from 2010 it is observed that majority of researchers and research papers adopted CloudSim toolkit to act out the cloud computing results since it provides real like cloud environment in a limited way [9].Author has mentioned the essential cloud computing components which used in this paper are as follows [10].…”

Section: Figure 2 Cloudsim Toolkit Frameworkmentioning

confidence: 99%

Improved Task Scheduling using Effective Particle Swarm Optimization in Cloud Computing Environment

Tomar,

Pant,

Tripathi

et al. 2019

IJEAT

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A vibrant on demand service of today’s era is cloud computing where one can utilize computer resources without indirect active management by user where one can use computing resources to achieve coherence in economic scale. Since cloud computing feel like Everything as a service so there should be highly scalable and reliable mechanisms to distribute the load evenly across the VMs evenly. Innumerable cloudlet mapping policies are presented in various research articles to achieve the high performance, better QOS and minimized task execution time but maximum are conventional approaches. No unconventional realistic scheduling algorithms is available which can schedule the tasks in heterogeneous manner. Since cloudlet scheduling is crucial metrics of cloud computing that has to be heightened by combining the different parameters. This paper tried to provide effectiveness and improvement in task scheduling using nature inspired Particle Swarm optimization (PSO) strategy. A powerful nature inspired load balancing mechanism is proposed in this paper which optimized makespan and throughput in environment of varying cloudlets and virtual machines results as compared to other conventional approaches. Proposed (EPSO) algorithm is with four scheduling policies namely FCFS, Round Robin (RR) and Shortest Job First (SJF) and get near twice good throughput percentage and minimized makespan in two different environments. Author used Cloud sim toolkit and some Open Source cloud packages to simulate the results of various scheduling components. Experimental results of various components are tested and simulated on java based CloudSim toolkit framework.

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Section: Figure 2 Cloudsim Toolkit Frameworkmentioning

confidence: 99%

Improved Task Scheduling using Effective Particle Swarm Optimization in Cloud Computing Environment

Tomar,

Pant,

Tripathi

et al. 2019

IJEAT

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“…Change should be possible in the arranged calculation to diminish culmination time and to acquire decency was the principle worry with the procedure. Shahab et al, and Zarintaj et al (2015) [13] worked on heuristics PSO based approach for scheduling. In their research they worked on number of particles in Swarm, number of iterations, learning rate with regard to individual ability and learning rate with regards to Social ability parameters to reduce makespan and to improve memory utilization.…”

Section: Related Workmentioning

confidence: 99%

Improvised Particle Swarm Optimization Technique for Workflow Balancing in Cloud

Rani¹,

Kumar²

2018

IJCA

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Our research focuses on improvised Particle Swam Optimization Technique for workflow balancing in cloud (IPSO-WF). The suggested technique assigns a cost to each task based on the resource requirement, the algorithm takes the four linear VMs (Virtual Machines) into deliberation before assign the job to it. The swarm searches for the VM meeting the rate, the work is assigned to the selected VM and resources updated. Since resources are allotted and VM engaged with a work the rate must needed to updated which has been disregarded in most of the research work however the planned algorithm updates the resources and rate is calculated again and again every instance when the work is assigned giving it a more realistic costing.The suggested work has been tested on 25, 50 and 75 VMs for L-ACO, B and BM. All the procedures perform best when more VMs are allotted as lesser VMs takes more resources resulting about loss of energy and time too. The acquired results shows that through all the systems are competitive but the suggested technique performs much better in the terms of time and energy.

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“…Similarly, Kim et al [19] have developed biogeography-based optimization (BBO) for task scheduling, this algorithm in large size problems is performs satisfactorily than other optimization problems such as genetic algorithm (GA), particle swarm optimization (PSO) and simulated annealing (SA). Solmaz Abdi et al [11] have proposed a modified PSO algorithm to allocate tasks in cloud computing environment which combine with shortest job to fastest processor algorithm (SJFP) to initializes particles minimize the makespan. Nidhi Bansala et al [20] have introduced the cost parameter to the QoS-driven scheduling algorithm minimizing the total allocation cost.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, some research work focuses on this problem and propose several methods, most of these methods are designed based on evolutionary algorithms, as this kind of algorithm has strong heuristic algorithm optimization ability. Some evolutionary algorithms (such as genetic algorithm [6][7][8][9], particle swarm algorithm [10][11][12], ant colony algorithm [13,14], bee colony [15], and cuckoo algorithm [4,16]) has been used to solve the problem of task scheduling in cloud computing. However, above evolutionary algorithms still have some shortcomings, such as slow convergence speed, easy to fall into local optimum, etc.…”

Section: Introductionmentioning

confidence: 99%

Effective Task Scheduling in Cloud Computing Based on Improved Social Learning Optimization Algorithm

et al. 2017

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Abstract-For the typical optimal problem of task scheduling in cloud computing, this paper proposes a novel resource scheduling algorithm based on Social Learning Optimization Algorithm (SLO). SLO is a new swarm intelligence algorithm which is proposed by simulating the evolution process of human intelligence and has better optimization mechanism and optimization performance. This paper proposes two learning operators for task scheduling in cloud computing after analyzing the characteristics of the problem of task scheduling; then, by introducing the Small Position Value (SPV) method, the two learning operators with continuous nature essence are enabled to solve the problem of task scheduling, and then the improved SLO is employed to solve the problem of cloud resource optimal scheduling. Finally, the performance of improved SLO is compared with existing research work on the CloudSim platform. Experimental results show that the approach proposed in this paper has better global optimization ability and convergence speed.

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Algorithm to improve job scheduling problem in cloud computing environment

Cited by 10 publications

References 11 publications

Improved Task Scheduling using Effective Particle Swarm Optimization in Cloud Computing Environment

Improved Task Scheduling using Effective Particle Swarm Optimization in Cloud Computing Environment

Improvised Particle Swarm Optimization Technique for Workflow Balancing in Cloud

Effective Task Scheduling in Cloud Computing Based on Improved Social Learning Optimization Algorithm

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