An Approach on Performance Monitoring in Cloud Application

Cloud computing is now becoming most clearly the interesting concept and priority for every individual to access over the internet. The advantageous features of cloud computing in scaling up and down, flexibility in resource monitoring. The main aspect of Auto scaling is to provide the facility to e...

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Bibliographic Details
Published in2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) Vol. 1; pp. 201 - 207
Main Authors Fernandez I., George, Renjith, J. Arokia
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2019
Subjects
Auto scaling
Cloud computing
Computational modeling
Load balancing
Load management
Optimization
Response time
Servers
Time factors
Virtual machining
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Summary:Cloud computing is now becoming most clearly the interesting concept and priority for every individual to access over the internet. The advantageous features of cloud computing in scaling up and down, flexibility in resource monitoring. The main aspect of Auto scaling is to provide the facility to every users to scale up and down the resources as and when their requirements demand, provisioning only the resource which are needed in Dynamic and billed for which they have used i.e. Pay-as-you-go model. Since the Usage are in automatic nature it involve less human intervention, less prone to individual involvement by which we incur less errors and thus we reduce manpower and reduction in cost. Since the cloud computing user are elastic in nature, we have choice to go for the most promising technique called auto scaling, which has the provision to balances the workload for incoming request and reduction in total cost to meet deadline. In our work we investigated to Identify the key challenge in Load balancing for Auto scaling in AWS for various work load in computing application. Proposed Algorithm for Virtual Machine optimal load balancing Algorithm, it is found perform better in term of response time comparing with the existing algorithm.
DOI:10.1109/ICONSTEM.2019.8918800