Title :
Stream-based Particle Swarm Optimization for data migration decision
Author :
Qiuchen Cheng;Kun Ma;Bo Yang
Author_Institution :
Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, China
Abstract :
As the load in the cloud environment is always changing, data migration become a key technology to realize the load balance of clusters. A good migration decision can make data migration more efficiency. To realize the migration decision rapidly, parallel Particle Swarm Optimization (PSO) based on stream computing technology is presented in this paper. We use PSO to get a migration plan with minimum overhead. Since the implementation of traditional PSO in serial is a huge waste of time in our scene, we design and accomplish Stream-based Particle Swarm Optimization (SPSO). SPSO utilizes stream computing technology to realize parallel PSO to make the process of data migration decision more rapidly and accurately, and realize real-time decisions on the basis of real-time status of nodes in the cloud. The average execution time of our SPSO is shorter than traditional serial PSO algorithm, and the migration cost of data migration decision result is lower.
Keywords :
"Particle swarm optimization","Real-time systems","Computers","Algorithm design and analysis","Birds","Cloud computing","Optimization"
Conference_Titel :
Soft Computing and Pattern Recognition (SoCPaR), 2015 7th International Conference of
DOI :
10.1109/SOCPAR.2015.7492818