MULTI-CLOUD FRAMEWORK ON MACHINE LEARNING RESOURCE ALLOCATION
Abstract
vioft2nntf2t|tblJournal|Abstract_paper|0xf4ffb1972b0000008324060001000500
In this paper, we suggest a multi-stage ML-RA system to increase the performance of existing and future cloud storage, which includes a QoS Resource Distribution that limits the volume of data to be shared in the Cloud to process and store. The proposed ML-RA method is capable of transferring all data from IoT to the cloud, which allows ML-RA to get server managers closer to the client. It is very difficult to grasp cloud computing without space sharing due to capableness and infrastructure costs. The paper has developed an appropriate ML-RA system and algorithm to check and test the efficiency of the cloud on the Cloudsim tool. This paper was planned and implemented Results shows an optimal algorithm for resource allocation is the suggested multi-stage ML-RA method which is easily scalable.

Authors
D Viknesh Kumar
Sriguru Institute of Technology, India

Keywords
Cloud Computing, Resource Allocation, Quality of Service, Energy Efficiency, Scheduling, Virtualization
Yearly Full Views
JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecember
000000000000
Published By :
ICTACT
Published In :
ICTACT Journal on Data Science and Machine Learning
( Volume: 1 , Issue: 3 , Pages: 92-96 )
Date of Publication :
June 2020
Page Views :
98
Full Text Views :
3

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.