Prediction based Load Balancing and VM Migration in Big Data Cloud Environment | IEEE Conference Publication | IEEE Xplore

Prediction based Load Balancing and VM Migration in Big Data Cloud Environment

Publisher: IEEE

Abstract:

In Big Data Cloud atmosphere, the cloud service provider (CSP) offers amenities to the customer with the accessible virtual cloud sources. Investigators have been provide...View more

Abstract:

In Big Data Cloud atmosphere, the cloud service provider (CSP) offers amenities to the customer with the accessible virtual cloud sources. Investigators have been provided more consideration towards the harmonizing of the load, as it has a complete impact on the system act. In this paper, Prediction based Load Balancing and Virtual Machine (VM) Migration (PLBVM) algorithm is designed for Big data cloud environments. In this algorithm, the future loads of each server are estimated. If the estimated future load is greater than an upper bound or less than a lower bound, then it indicates unbalanced load, so that VM migration is triggered. In VM migration, the VMs with minimum migration time and sufficient resources are selected. Then the task execution continues in the migrated VMs. By experimental results, it is shown that PLBVM achieves lesser response delay and execution time, among the other approaches.
Date of Conference: 19-21 January 2021
Date Added to IEEE Xplore: 25 February 2021
ISBN Information:
Publisher: IEEE
Conference Location: Dubai, United Arab Emirates

I. Introduction

Cloud computing (CC) is the Innovative networking idea that allows you to utilize any system sources accessible on the internet by requesting the access. Thus CC is a sequence of amenity which progressed from web associated mechanisms and models that contain dispersed processing, parallel processing and grid computing and so on.[1]. Cloud encompasses enormous number physical servers. It is essential to evade hotspot for effectual source usage in a cloud. Overloaded servers end in act deprivation of uses and under loaded servers end in incompetent source usage. It is essential to offer correct amount of means of seeing and examining the world. That is to determine novel remits of scale regarding attainment, probing, distributing, storage, analysis and performance of the data. This enormous volume of data must be examined and treated competently for diverse purposes in least delay [10].

References

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