GA-based efficient resource allocation and task scheduling in a multi-cloud environment

Jena, Tamanna; Mohanty, Jnyana Ranjan

doi:10.1504/ijaip.2022.123015

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In cloud computing environments, task-to-resource scheduling is the distribution of tasks requested by users to be processed to resources with different configurations for execution according to their diverse needs [5][6]. According to the understanding of scheduling policies, many scheduling policies have been applied to task-to-resource scheduling in cloud computing environments, including traditional and heuristic scheduling policies, and good scheduling results have been achieved [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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

Lin

2023

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Lin

2023

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

show abstract

GA-based efficient resource allocation and task scheduling in a multi-cloud environment

Cited by 1 publication

References 0 publications

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

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

Contact Info

Product

Resources

About