Open Access

ARTICLE

Latency Minimization Using an Adaptive Load Balancing Technique in Microservices Applications

G. Selvakumar^1,*, L. S. Jayashree², S. Arumugam³

1 Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, 641407, India
2 Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, 641004, India
3 Department of Computer Science and Engineering, Nandha College of Engineering, Erode, 638052, India

* Corresponding Author: G. Selvakumar. Email:

Computer Systems Science and Engineering 2023, 46(1), 1215-1231. https://doi.org/10.32604/csse.2023.032509

Received 20 May 2022; Accepted 04 July 2022; Issue published 20 January 2023

Abstract

Advancements in cloud computing and virtualization technologies have revolutionized Enterprise Application Development with innovative ways to design and develop complex systems. Microservices Architecture is one of the recent techniques in which Enterprise Systems can be developed as fine-grained smaller components and deployed independently. This methodology brings numerous benefits like scalability, resilience, flexibility in development, faster time to market, etc. and the advantages; Microservices bring some challenges too. Multiple microservices need to be invoked one by one as a chain. In most applications, more than one chain of microservices runs in parallel to complete a particular requirement To complete a user’s request. It results in competition for resources and the need for more inter-service communication among the services, which increases the overall latency of the application. A new approach has been proposed in this paper to handle a complex chain of microservices and reduce the latency of user requests. A machine learning technique is followed to predict the weighting time of different types of requests. The communication time among services distributed among different physical machines are estimated based on that and obtained insights are applied to an algorithm to calculate their priorities dynamically and select suitable service instances to minimize the latency based on the shortest queue waiting time. Experiments were done for both interactive as well as non interactive workloads to test the effectiveness of the solution. The approach has been proved to be very effective in reducing latency in the case of long service chains.

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Keywords

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