<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, the inertia weighting strategy of the particle swarm is improved by using the properties of periodicity and fixed upper and lower bounds of sinusoidal function to model the task scheduling problem in cloud computing as a mathematical problem, and the improved particle swarm algorithm is discretized, and the improved discrete particle swarm algorithm is applied to task scheduling by corresponding encoding method. The task scheduling algorithm (PSOACO) that fuses the fast convergence and small computational power of the particle swarm algorithm with the global exploration capability of the ant colony algorithm for scheduling tasks is proposed. Two test cases, PageRank and wordcount, are selected to measure the performance of the PSO-ACO algorithm. In the performance comparison running the PageRank test case, the PSO-ACO algorithm obtains a performance speedup ratio of 3.8 times that of the native Domino when 50,000 pages are added. In the execution time comparison for the wordcount test case with an additional data set, the PSO-ACO algorithm is nearly 2.8 times faster than the native Domino when adding 1GB of data. Thus, the fusion algorithm reduces the task completion time and achieves a balance between the algorithm’s computational effort and the scheduling’s convergence performance.</p>
</abstract>

In this paper, the inertia weighting strategy of the particle swarm is improved by using the properties of periodicity and fixed upper and lower bounds of sinusoidal function to model the task scheduling problem in cloud computing as a mathematical problem, and the improved particle swarm algorithm is discretized, and the improved discrete particle swarm algorithm is applied to task scheduling by corresponding encoding method. The task scheduling algorithm (PSOACO) that fuses the fast convergence and small computational power of the particle swarm algorithm with the global exploration capability of the ant colony algorithm for scheduling tasks is proposed. Two test cases, PageRank and wordcount, are selected to measure the performance of the PSO-ACO algorithm. In the performance comparison running the PageRank test case, the PSO-ACO algorithm obtains a performance speedup ratio of 3.8 times that of the native Domino when 50,000 pages are added. In the execution time comparison for the wordcount test case with an additional data set, the PSO-ACO algorithm is nearly 2.8 times faster than the native Domino when adding 1GB of data. Thus, the fusion algorithm reduces the task completion time and achieves a balance between the algorithm’s computational effort and the scheduling’s convergence performance.

A Study on the Impact of Cloud Computing Performance Efficiency on Task Resource Scheduling

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, the inertia weighting strategy of the particle swarm is improved by using the properties of periodicity and fixed upper and lower bounds of...

A Study on the Impact of Cloud Computing Performance Efficiency on Task Resource Scheduling

Pubblicato online: 23 set 2023

Pagine: -

Ricevuto: 31 ott 2022

Accettato: 03 apr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00356

Parole chiave
Cloud computing, Particle swarm algorithm, Ant colony algorithm, Task resource scheduling, PSOACO algorithm

© 2023 Jianling Lin, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A Study on the Impact of Cloud Computing Performance Efficiency on Task Resource Scheduling

Pubblicato online: 23 set 2023

Pagine: -

Ricevuto: 31 ott 2022

Accettato: 03 apr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00356

Parole chiaveCloud computing, Particle swarm algorithm, Ant colony algorithm, Task resource scheduling, PSOACO algorithm

© 2023 Jianling Lin, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Cloud computing, Particle swarm algorithm, Ant colony algorithm, Task resource scheduling, PSOACO algorithm