Open Access

ARTICLE

Adaptive Server Load Balancing in SDN Using PID Neural Network Controller

by R. Malavika^1,*, M. L. Valarmathi²

1 Department of Information Technology, Government College of Technology, Coimbatore, 641013, India
2 Department of Computer Sceince and Engineering, Dr. Mahalingham College of Engineering and Technology, Pollachi, 642003, India

* Corresponding Author: R. Malavika. Email:

Computer Systems Science and Engineering 2022, 42(1), 229-243. https://doi.org/10.32604/csse.2022.020947

Received 16 June 2021; Accepted 23 August 2021; Issue published 02 December 2021

Abstract

Web service applications are increasing tremendously in support of high-level businesses. There must be a need of better server load balancing mechanism for improving the performance of web services in business. Though many load balancing methods exist, there is still a need for sophisticated load balancing mechanism for not letting the clients to get frustrated. In this work, the server with minimum response time and the server having less traffic volume were selected for the aimed server to process the forthcoming requests. The Servers are probed with adaptive control of time with two thresholds L and U to indicate the status of server load in terms of response time difference as low, medium and high load by the load balancing application. Fetching the real time responses of entire servers in the server farm is a key component of this intelligent Load balancing system. Many Load Balancing schemes are based on the graded thresholds, because the exact information about the network flux is difficult to obtain. Using two thresholds L and U, it is possible to indicate the load on particular server as low, medium or high depending on the Maximum response time difference of the servers present in the server farm which is below L, between L and U or above U respectively. However, the existing works of load balancing in the server farm incorporate fixed time to measure real time response time, which in general are not optimal for all traffic conditions. Therefore, an algorithm based on Proportional Integration and Derivative neural network controller was designed with two thresholds for tuning the timing to probe the server for near optimal performance. The emulation results has shown a significant gain in the performance by tuning the threshold time. In addition to that, tuning algorithm is implemented in conjunction with Load Balancing scheme which does not tune the fixed time slots.

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Keywords

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