Dynamic Load Balancing for Video Processing System in Cloud

Sandhya, S; Cauvery, N. K.

doi:10.1007/978-81-322-2196-8_22

Cited by 2 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Now a days, streaming applications [14] are on demand which causes traffic in the network load due to user's expectation in the quality of audio or video. To overcome the imbalance of the load on client and server, a generalized framework [14] is proposed in which duplication of the recent accessed files are buffered on different systems and also supports for live streaming. The files are compressed and uploaded on client and downloaded from the server respectively, so that the delay is less.…”

Section: Load Balancing and Genetic Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Load Based Migration Based on Virtualization Using Genetic Algorithm

Sandhya¹,

Usha²,

Cauvery³

2015

Emerging Research in Computing, Information, Communication and Applications

Self Cite

View full text Add to dashboard Cite

Load balancing is the process of distributing tasks among different nodes in a network. The nodes may be either on different machines or nodes (virtual machines) on the same machine. Based on the availability of the nodes, processes from fully loaded node can be migrated from one node to another having less load known as process migration. Process migration can also be done in the virtualization environment by using a hypervisor called Xen hypervisor. This hypervisor safely multiplexes the hardware resources of the physical machine leading the resource allocation in the Virtual Machine (VM) to improve the utilization and performance. To optimize the task of balancing load among nodes, Genetic algorithm (GA) may be used for selecting the destination. GA is a search algorithm, based on natural genetics and principle of evolution and is been widely used in optimization with binary and continuous variables. With the adaptive crossover operation of GA and the searching heuristic and fitness function, lots of possible solutions are searched and the best one is selected as the destination for migration. GA is been proved to be effective in discovering the global optimum even in a very complex searching space.

show abstract

Section: Load Balancing and Genetic Algorithmmentioning

confidence: 99%

“…Streaming applications [14] also improve the user experience and response time by employing load balancing on the server and client end.…”

Section: Load Balancing and Genetic Algorithmmentioning

confidence: 99%