TETS: A Genetic-Based Scheduler in Cloud Computing to Decrease Energy and Makespan

Shojafar, Mohammad; Kardgar, Maryam; Hosseinabadi, Ali Asghar Rahmani; Shamshirband, Shahab; Abraham, Ajith

doi:10.1007/978-3-319-27221-4_9

Cited by 29 publications

(12 citation statements)

References 10 publications

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“…Owing to large solution space, scheduling is categorized as an NP-hard problem, and consequently, it needs time for finding an optimal answer. In general, the task scheduling problem could be regarded and modeled as a version of the Traveling Salesman Problem (TSP) [8,9] or Vehicle Routing Problem (VRP) [10,11]. In the TSP, a salesman must travel through all the cities and return to the city of origin, provided that it passes through the city once and travels the shortest distance [8].…”

Section: Introductionmentioning

confidence: 99%

Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Pirozmand

Hosseinabadi

Farrokhzad

et al. 2021

Neural Comput & Applic

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The cloud computing systems are sorts of shared collateral structure which has been in demand from its inception. In these systems, clients are able to access existing services based on their needs and without knowing where the service is located and how it is delivered, and only pay for the service used. Like other systems, there are challenges in the cloud computing system. Because of a wide array of clients and the variety of services available in this system, it can be said that the issue of scheduling and, of course, energy consumption is essential challenge of this system. Therefore, it should be properly provided to users, which minimizes both the cost of the provider and consumer and the energy consumption, and this requires the use of an optimal scheduling algorithm. In this paper, we present a two-step hybrid method for scheduling tasks aware of energy and time called Genetic Algorithm and Energy-Conscious Scheduling Heuristic based on the Genetic Algorithm. The first step involves prioritizing tasks, and the second step consists of assigning tasks to the processor. We prioritized tasks and generated primary chromosomes, and used the Energy-Conscious Scheduling Heuristic model, which is an energy-conscious model, to assign tasks to the processor. As the simulation results show, these results demonstrate that the proposed algorithm has been able to outperform other methods.

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Section: Introductionmentioning

confidence: 99%

Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Pirozmand

Hosseinabadi

Farrokhzad

et al. 2021

Neural Comput & Applic

Self Cite

View full text Add to dashboard Cite

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“…Load balancing is should be able to distribute the load in a balanced form to all the VMs, hence reducing the makespan. Works (Soni and Kalra 2014;Xu et al 2016;Li and Zhang 2016;Abazari et al 2018;Juarez et al 2018;Shojafar et al 2016) focus on makespan reduction using different methods. Papers (Birje et al 2012;Buyya et al 2008;Achar and Thilagam 2014;Goudar et al 2017;Birje et al 2014) discuss the different strategies based on game theory for developing a pricing model.…”

Section: Related Workmentioning

confidence: 99%

Utility based load balancing using firefly algorithm in cloud

Tapale

Goudar

Birje

et al. 2020

J. of Data, Inf. and Manag.

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Scheduling and load balancing are the major challenges faced in cloud scenario due to distributed computing and heterogeneous nature of the infrastructure. Scheduling of tasks to the appropriate virtual machines (VMs) can be done using different mechanisms, but balancing the load is the major problem that occurs due to fluctuation of load, and different VM specifications. This leads to imbalanced resource utilization and performance degradation of the system. To address this issue, the paper proposes a scheme that tries to maximize resource utilization while optimizing the utility (profit) using bargaining protocol, and also balances the load across cloud system by distributing jobs to reliable VMs using firefly algorithm for better performance. The proposed scheme is simulated using CloudSim tool and the results are compared with existing work. It is observed that the proposed scheme performs better than the existing work.

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“…Thus an optimal scheduling algorithm must involve job prioritization and choose the optimal processor in such a manner that manufacturing time and energy usage is decreased. This paper [6] explains two steps in process algorithm for scheduling named as TETS in which the starting step includes prioritizing job and second includes processor allocation. There are three prioritization procedures to prioritize jobs to produce best starting chromosomes and allocating jobs to processors.…”

Section: Types Of Load Balancing Algorithmsmentioning

confidence: 99%

OCSA: Task Scheduling Algorithm in Cloud Computing Environment

Pradeep

Jacob²

2018

IJIES

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Cloud computing offers dynamic allocation of resources on demand, the feature which makes it to stand apart providing great performance, scalability, cost efficient and less maintenance, thus making it an apt choice. Task scheduling becomes the essential factor in increasing the performance for the dynamic allocation of resources which is most essential in the cloud environment to increase performance and decrease the cost. In this work, a solution is proposed using makespan and cost, taking them as important constraints for the optimization problem. We have merged two algorithms namely, cuckoo search algorithm (CSA) and oppositional based learning (OBL) and created a new hybrid algorithm called oppositional cuckoo search algorithm (OCSA) to provide solution to the above stated issue. Our proposed OCSA algorithm showed noticeable improvement over the other task scheduling algorithms. The proposed work is simulated in cloudsim programming environment and the simulation results show the effectiveness of the proposed work by minimizing cost and makespan parameters. The obtained results are better in comparison to other existing algorithms like particle swarm optimization (PSO), Improved Differential Evolution Algorithm (IDEA) and genetic algorithm (GA).

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TETS: A Genetic-Based Scheduler in Cloud Computing to Decrease Energy and Makespan

Cited by 29 publications

References 10 publications

Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Utility based load balancing using firefly algorithm in cloud

OCSA: Task Scheduling Algorithm in Cloud Computing Environment

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