Publication Date
Spring 2014
Degree Type
Master's Project
Degree Name
Master of Science (MS)
Department
Computer Science
Abstract
Cloud computing provides a pool of virtualized computing resources and adopts pay-per-use model. Schedulers for cloud computing make decision on how to allocate tasks of workflow to those virtualized computing resources. In this report, I present a flexible particle swarm optimization (PSO) based scheduling algorithm to minimize both total cost and makespan. Experiment is conducted by varying computation of tasks, number of particles and weight values of cost and makespan in fitness function. The results show that the proposed algorithm achieves both low cost and makespan. In addition, it is adjustable according to different QoS constraints.
Recommended Citation
Wu, Kai, "A TUNABLE WORKFLOW SCHEDULING ALGORITHM BASED ON PARTICLE SWARM OPTIMIZATION FOR CLOUD COMPUTING" (2014). Master's Projects. 358.
DOI: https://doi.org/10.31979/etd.wy2s-568v
https://scholarworks.sjsu.edu/etd_projects/358
