Comparative Study of Task Scheduling Algorithms through Cloudsim

Pratap, Ram; Zaidi, Taskeen

doi:10.1109/icrito.2018.8748514

Cited by 15 publications

(9 citation statements)

References 9 publications

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“…The hybrid model shows improved scheduling efficiency by outperforming traditional algorithms like Min-Min and Max-Min. R. Pratap et al [6] Cloud computing offers on-demand, scalable, and flexible services with pay-per-use. Parallel computing is used for fast execution, and scheduling is crucial for ensuring service quality.…”

Section: IImentioning

confidence: 99%

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Overview on Efficient Resource Provisioning Approach Using Cloud Computing Environment

Kolte

2023

IJRASET

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The goal of this study is to examine the importance of effective resource management across different fields and the critical role that cloud computing apps have in accomplishing this effectiveness. To show how these various approaches and strategies can be used in real-world scenarios, the article looks at case studies and application usage. Optimizing resource utilization and performance in cloud computing requires effective scheduling, resource allocation, and other algorithms Using Cloud Simulation (cloud sim) Tool and other cloud environment tools. By adjusting to changing workloads, these algorithms improve fault tolerance, cost effectiveness, and load balancing. Furthermore, maintaining cloud service integrity depends heavily on algorithms for data processing, network optimization, and fault tolerance. Sufficient research in this area is essential to fully utilize cloud computing.

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Section: IImentioning

confidence: 99%

“…MCTS is known for its adaptability to various domains, efficient resource utilization, scalability, and the ability to handle situations with incomplete or uncertain information. It has achieved remarkable success in playing games like Go and chess, as well as applications in robotics, planning, recommendation systems, and more [6].…”

Section: IImentioning

confidence: 99%

Overview on Efficient Resource Provisioning Approach Using Cloud Computing Environment

Kolte

2023

IJRASET

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“…e multiobjective evolutionary algorithm based on decomposition (MOEA/D) proposed by Pratap and Zaidi [23] is particularly effective.…”

Section: Multiobjective Problemmentioning

confidence: 99%

Swarm Intelligence Algorithms for Optimal Scheduling for Cloud-Based Fuzzy Systems

AlSuwaidan

Khan

Almakki

et al. 2022

Mathematical Problems in Engineering

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A fuzzy cloud resource scheduling model with time-cost constraints is built using fuzzy triangular numbers to represent uncertain task execution time. Task scheduling reduces total time and cost spent on a project. It connects virtual machines and functions. Particle swarm optimization (HPO) is used to plan cloud resources (HSOA). The approach uses orthogonal particle swarm initialization to increase the quality of the initial particle exploration, rerandomization to regulate the particle search range, and real-time updating of inertia weights to control particle speed. The suggested problem model and optimization approach are evaluated using random simulation data provided by the CloudSim simulation platform. Less overall execution time and a lower cost are shown to have fast convergence and solution capabilities in experiments.

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“…Pratap and Zaidi [32] worked on a comparative study between three of the static task scheduling algorithms, which are FCFS, SJF, and RR by using CloudSim tools. They used Time-Shared and Space-Shared policies of execution on the cloudlet while considering the waiting time and turnaround time for each algorithm.…”

Section: Task Scheduling Evaluating Algorithmsmentioning

confidence: 99%

Modeling Algorithms for Task Scheduling in Cloud Computing Using CloudSim

Gollapalli¹,

Al-Amoudi²,

Aldossary³

et al. 2022

MMEP

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As the number of cloud users are spontaneously growing globally, there is an urgent need to constantly provide quality services to consumers. Consequently, task scheduling plays an essential role in improving the performance of the cloud computing environment. Most of the published research in this field share common goals, which can be summarized in maximizing resource utilization, reducing cost, and increasing performance. This research provides the foundation knowledge on the latest works done to enhance and optimize the existing task scheduling algorithm in cloud computing by considering various parameters. Furthermore, in this study, we have applied comparative study to analyze the performance of three task scheduling algorithms namely Max-Min, First Come First Serve (FCFS), and Round Robin (RR) in cloud computing environments based on the performance metric of the Virtual Machines (VM) resources' cost, average time and makespan to find the best performing algorithm in the cloud environment. The experimental evaluations were conducted using CloudSim simulation tool. The results show that Max-Min achieved better performance based on makespan and average waiting time than other algorithms in Space and Time-shared policies.

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Comparative Study of Task Scheduling Algorithms through Cloudsim

Cited by 15 publications

References 9 publications

Overview on Efficient Resource Provisioning Approach Using Cloud Computing Environment

Overview on Efficient Resource Provisioning Approach Using Cloud Computing Environment

Swarm Intelligence Algorithms for Optimal Scheduling for Cloud-Based Fuzzy Systems

Modeling Algorithms for Task Scheduling in Cloud Computing Using CloudSim

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