QPSO Based Load Balancing Mechanism In Cloud Environment

Sivakumar, P.; Kalaiyarasi, S.; Shilpa, S.; Swetha, D.

doi:10.1109/icscan.2019.8878747

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…x id means that task d is scheduled to execute on resource x i , and its matrix representation is as shown in formula (16). X is a 0 − 1 matrix, when x ij = 1, it means that the data to be processed by the i-th task is processed by the j-th computing resource; when x ij =0, it means that the data to be processed by the i-th task is not processed by the j-th computing resource [21][22][23][24].…”

Section: Hwqpso For Task Scheduling In Cloud Computingmentioning

confidence: 99%

A QPSO algorithm based on hierarchical weight and its application in cloud computing task scheduling

et al. 2021

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The computing method of the average optimal position is one of the most important factors that affect the optimization performance of the QPSO algorithm. Therefore, a particle position weight computing method based on particle fitness value grading is proposed, which is called HWQPSO (hierarchical weight QPSO). In this method, the higher the fitness value of a particle, the higher the level of the particle, and the greater the weight. Particles at different levels have different weights, while particles at the same level have the same weight. Through this method, the excellent particles have higher average optimal position weight, and at the same time, the absolute weight of a few particles is avoided, so that the algorithm can quickly and stably converge to the optimal solution, and improve the optimization ability and efficiency of the algorithm. In order to verify the effective ness of the method, five standard test functions are selected to test the performance of HWQPSO, QPSO, DWC-QPSO and LTQPSO algorithm, and the algorithms are applied to the task scheduling of the cloud computing platform. Through the test experiment and application comparison, the results show that the HWQPSO algorithm can converge to the optimal solution of the test function faster than the other three algorithms. It can also find the task scheduling scheme with the shortest time consumption and the most balanced computing resource load in the cloud platform. In the experiment, compared with QPSO, DWC-QPSO and LTQPSO algorithm, HWQPSO execution time of the maximum task scheduling was reduced by 35%, 23% and 21% respectively.

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Section: Hwqpso For Task Scheduling In Cloud Computingmentioning

confidence: 99%

A QPSO algorithm based on hierarchical weight and its application in cloud computing task scheduling

et al. 2021

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“…The aim of load balancing is to distribute workloads to a set of reliable servers in the system. When workloads are distributed without load balancing techniques, the user may exhibit uncomfortable computing experiences, such as quality of service (QoS) and service level agreement (SLA) violations [4,5], unreliable data processing [6,7], and a lack of responsiveness [8,9]. As the scale of the state of the art computing systems grows in size, implementing load balancing techniques becomes more important in cloud computing environments [10,11].…”

Section: Introductionmentioning

confidence: 99%

A Load Balancing Algorithm for Mobile Devices in Edge Cloud Computing Environments

Lim

Lee

2020

Electronics

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As current data centers and servers are growing in size by orders of magnitude when needed, load balancing is a great concern in scalable computing systems, including mobile edge cloud computing environments. In mobile edge cloud computing systems, a mobile user can offload its tasks to nearby edge servers to support real-time applications. However, when users are located in a hot spot, several edge servers can be overloaded due to suddenly offloaded tasks from mobile users. In this paper, we present a load balancing algorithm for mobile devices in edge cloud computing environments. The proposed load balancing technique features an efficient complexity by a graph coloring-based implementation based on a genetic algorithm. The aim of the proposed load balancing algorithm is to distribute offloaded tasks to nearby edge servers in an efficient way. Performance results show that the proposed load balancing algorithm outperforms previous techniques and increases the average CPU usage of virtual machines, which indicates a high utilization of edge servers.

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QPSO Based Load Balancing Mechanism In Cloud Environment

Cited by 2 publications

References 8 publications

A QPSO algorithm based on hierarchical weight and its application in cloud computing task scheduling

A QPSO algorithm based on hierarchical weight and its application in cloud computing task scheduling

A Load Balancing Algorithm for Mobile Devices in Edge Cloud Computing Environments

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