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.

This article comprises work concerned with proposing a scheme for intercepting network traffic and directs that traffic to servers in Software defined network model.Several studies have explored the effects of average response time of user requests with different load balancing algorithms. Server load balancing is a well-recognized issue which necessitates a better approach by doing deeper understanding of the network parameters. The implications of the mean response time of client requests are investigated in this article by having a closer look on changing the important network parameter namely probing time (time to investigate the server farm in order to gain access to server data) using the concept of closed loop control theory. Our LBB-CLCT proves to be effectively distributes the user requests to the most appropriate server by timely changing the server even when the traffic exhibits inconsistency. The results reveal that LBBCLCT outperforms other methods such as round robin, LBBSRT, and SD-WLB in terms of processing the client requests with a reduced mean server response time. LBBCLCT showed the improvement of average response time by 46.2%, 25.6%, and 43.59% over round robin, LBBSRT, and SDWLB respectively.