An improved and optimized approach for pre-emptive migration of video process

Sandhya, S; Cauvery, N. K.

doi:10.1109/ic3i.2014.7019813

Cited by 1 publication

(2 citation statements)

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“…During process migration different migration stages are also considered to achieve optimization. Multimedia files [12] can also be migrated from different nodes dynamically using the same above defined approaches based on CPU usage.…”

Section: Load Balancing and Genetic Algorithmmentioning

confidence: 99%

“…Therefore this generic approach benefits from significant features of the existing algorithms providing better performance. A video process migration [12] is proposed by using sender-initiated dynamic process migration which uses post-copy mechanism with Common Internet File System (CIFS) protocol for balancing the load. The different phases consideration here are (a) Activation phaseto determine time of migration, (b) Selection phase-to select the victim process to migrate, (c) Location phase-to select the destination node, and (d) Migration phaseto migrates the process.…”

Section: Process Migrationmentioning

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

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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.

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Section: Load Balancing and Genetic Algorithmmentioning

confidence: 99%

Section: Process Migrationmentioning

confidence: 99%