ENHANCED NEURAL NETWORK SCHEDULING FOR LOAD BALANCING IN MULTI-CLOUD

Abstract
In the cloud, numerous problems are encountered, owing to the fact that this environment is a closely connected environment. As the nodes continue to switch from one stage to the other in respect of specifications, problems can also be found in the field of service distribution and in the level of service delivered. The positioning of contact nodes remains unchanged. The efficiency of cloud service is influenced by criteria such as throughput, load, latency and even more adversely. The above parameters are linked to the user requests for a load balancing technique. Certain load balancing techniques have been addressed in this paper to increase the level of operation in the cloud world. This paper introduces and changes a structure for the provision of resource and load balancing. The system suggested relies on an optimization algorithm for binary ant colony to perform ideally as regards cost and cost. A workflows system was suggested for preferably loading and advancing the use of underused VMs. The findings show a stable low cost trend with a minimal number of VMs and steadily grows with a rise in VMs

Authors
S Selvakumar
Kalasalingam Academy of Research and Education, India

Keywords
Recurrent Neural Network, Load Balancing, Workflow Execution
Published By :
ICTACT
Published In :
ICTACT Journal on Data Science and Machine Learning
( Volume: 1 , Issue: 3 )
Date of Publication :
June 2020

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