Energy aware multi objective genetic algorithm for task scheduling in cloud computing

Bindu, G.B. Hima; Ramani, K.; Bindu, C. Shoba

doi:10.1504/ijipt.2018.10016310

Cited by 4 publications

(4 citation statements)

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“…In the second stage, tasks were allocated on VMs dynamically. An algorithm for selecting VMs to provide task solutions in the cloud with minimal energy consumption was presented by Bindu et al [11].…”

Section: Related Workmentioning

confidence: 99%

Constrained k-means algorithm for resource allocation in mobile cloudlets

Alguliyev¹,

Alıguliyev²,

Alakbarov³

2023

Kybernetika

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With the rapid increase in the number of mobile devices connected to the Internet in recent years, the network load is increasing. As a result, there are significant delays in the delivery of cloud resources to mobile users. Edge computing technologies (edge, cloudlet, fog computing, etc.) have been widely used in recent years to eliminate network delays. This problem can be solved by allocating cloud resources to the cloudlets that are close to users. The article proposes a clustering-based model for the optimal allocation of cloud resources among cloudlets. The proposed model takes into account user activity, usage frequency of cloud resources, the physical distance between users and cloud resources, as well as the storage capacity of cloudlets for optimal allocation of cloud resources in cloudlets. The proposed model was formalized as a constrained k-means method and an algorithm was developed to solve it. The MATLAB 2022a toolkit was used to evaluate the efficiency of the proposed algorithm. The obtained results revealed that the algorithm is promising.

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Section: Related Workmentioning

confidence: 99%

Constrained k-means algorithm for resource allocation in mobile cloudlets

Alguliyev¹,

Alıguliyev²,

Alakbarov³

2023

Kybernetika

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“…Due to space limitations, this article only elaborates on commonly used metaheuristics. Literature [5] uses the Genetic Algorithm to optimize task scheduling with energy consumption as the main goal; Literature [4] merges Min-Min and Max-Min into the Genetic Algorithm for the cloud computing task scheduling. The above results show that the use of the Genetic Algorithm in cloud computing tasks can reduce task completion time, reduce energy consumption, and improve resource utilization.…”

Section: Related Workmentioning

confidence: 99%

Cloud Computing Task Scheduling Model Based on Improved Whale Optimization Algorithm

Jia

Shi

2021

Wireless Communications and Mobile Computing

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The efficiency of task scheduling under cloud computing is related to the effectiveness of users. Aiming at the problems of long scheduling time, high cost consumption, and large virtual machine load in cloud computing task scheduling, an improved scheduling efficiency algorithm (called the improved whale optimization algorithm, referred to as IWC) is proposed. Firstly, a cloud computing task scheduling and distribution model with time, cost, and virtual machines as the main factors is constructed. Secondly, a feasible plan for each whale individual corresponding to cloud computing task scheduling is to find the best whale individual, which is the best feasible plan; in order to better find the optimal individual, we use the inertial weight strategy for the whale optimization algorithm to improve the local search ability and effectively prevent the algorithm from reaching premature convergence; we use the add operator and delete operator to screen individuals after each iteration which is completed and updated to improve the quality of understanding. In the simulation experiment, IWC was compared with the ant colony algorithm, particle swarm algorithm, and whale optimization algorithm under a different number of tasks. The results showed that the IWC algorithm has good results in terms of task scheduling time, scheduling cost, and virtual machine. The application is in cloud computing task scheduling.

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“…Bindu, et al have presented a task scheduling mechanism focusing on minimizing energy consumption in the cloud using a genetic algorithm. Simulation results showed that this mechanism could reduce energy consumption and makespan more effectively.…”

Section: Analysis Of Task Scheduling Approachesmentioning

confidence: 99%

Task scheduling mechanisms in cloud computing: A systematic review

Motlagh

Movaghar

Rahmani

2019

Int J Communication

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Summary Today, cloud computing has developed as one of the important emergent technologies in communication and Internet. It offers on demand, pay per use access to infrastructure, platforms, and applications. Due to the increase in its popularity, the huge number of requests need to be handled in an efficient manner. Task scheduling as one of the challenges in the cloud computing supports the requests for assigning a particular resource so as to perform effectively. In the resource management, task scheduling is performed where there is the dependency between tasks. Many approaches and case studies have been developed for the scheduling of these tasks. Up to now, a systematic literature review (SLR) has not been presented to discover and evaluate the task scheduling approaches in the cloud computing environment. To overcome, this paper presents an SLR‐based analysis on the task scheduling approaches that classify into (a) single cloud environments that evaluate cost‐aware, energy‐aware, multi‐objective, and QoS‐aware approaches in task scheduling; (b) multicloud environment that evaluates cost‐aware, multi‐objective, and QoS‐aware task scheduling; and (c) mobile cloud environment that is energy‐aware and QoS‐aware task scheduling. The analytical discussions are provided to show the advantages and limitations of the existing approaches.

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Energy aware multi objective genetic algorithm for task scheduling in cloud computing

Cited by 4 publications

References 0 publications

Constrained k-means algorithm for resource allocation in mobile cloudlets

Constrained k-means algorithm for resource allocation in mobile cloudlets

Cloud Computing Task Scheduling Model Based on Improved Whale Optimization Algorithm

Task scheduling mechanisms in cloud computing: A systematic review

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