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Improve Load Balancing Performance and Efficiency Using Equally Spread Current Execution Algorithm working with response time clustering in Microservices

Agavane, Prashant Bharat (2020) Improve Load Balancing Performance and Efficiency Using Equally Spread Current Execution Algorithm working with response time clustering in Microservices. Masters thesis, Dublin, National College of Ireland.

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Abstract

Microservices architecture is the formation of a network of multiple services communicating with each other through service request and response messages. To achieve high availability and make business unit fault-tolerant, microservices use cloud computing services for hosting multiple instances of each service distributed geographically. In the process of handling these service API calls, an effecting load-balancing technique play a crucial role to achieve efficient performance by reducing response time. After a detailed study of available load balancing techniques and algorithms in the literature review carried out, we have proposed a combined load balancing approach that uses Equally Spread Current Execution(ESCE) algorithm and response time clustering method based on historical response time for load balancing. In this project, research on finding a performance gap between the Round robin algorithm and the proposed load balancing approach has been carried out. For this performance comparison, I have recorded response time data of multiple instances and scenarios for statistical analysis. This statistical analysis concludes the effectiveness of proposed implementation for minimizing processing delay by choosing the optimal microservice/instance.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software

T Technology > T Technology (General) > Information Technology > Cloud computing
Divisions: School of Computing > Master of Science in Cloud Computing
Depositing User: Dan English
Date Deposited: 09 Jun 2020 13:15
Last Modified: 09 Jun 2020 13:15
URI: http://trap.ncirl.ie/id/eprint/4257

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