Dynamic Load Balancing Using Hybrid Approach

Loadbalancinginacloudenvironmentforhandlingmultipleprocessofdifferentsizeisanimportant issue.Manyadvancedtechnologiesareincorporatedintheprocesses-basedresourceallocationwhich enhancesthesystemefficiency.Thestepsofallottingresourcestoprocesscanbedonebytakingdata whichhelpstoanalyzeandmakeimportantdecisionsatruntime.Thisarticlefocusesontheallocation ofcloudresourceswheretwomodelsweredeveloped,thefirstwasTLBO(TeacherLearningBased Optimization),ageneticalgorithmwhichfindsthecorrectpositionfortheprocesstoexecute.Here, someinformationusedforanalysiswastotalnumberofmachines,memory,executiontime,etc.So, theoutputoftheTLBOprocesssequencewasusedastraininginputfortheErrorBackPropagation NeuralNetworkforlearning.Thistrainedneuralnetworkimprovedtheworkjobsequencequality. Trainingwasdone insuchawaythatallsetsoffeatureswereutilized topairwith theirprocess requirementandcurrentposition.Forincreasingthereliabilityofthework,anexperimentwasdone onarealdataset.Resultsshowthattheproposedmodelhasovercomevariousevaluationparameters onadifferentscaleascomparedtopreviousapproachesadoptedbyresearchers. KeywoRDS Cloud Computing, Genetic Algorithm, Load Balancing, Neural Network, Virtual Machines

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