Adaptive Server Load Balancing in SDN Using PID Neural Network Controller

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.

Fast Verification of Network Configuration Updates

“…Currently, some scholars have done verification work or network optimization for the verification of the control plane and data plane for traditional networks or SDN [20,21]. Among them, Chhabra et al [22] validated the strategy in a federated cloud environment.…”

ProbD: Faulty Path Detection Based on Probability in Software-Defined Networking

“…Compared with the traditional network, an SDN network can provide better service quality for existing fields such as Edge Computing and the Internet of Things [1]. In emerging fields such as the 5G network, Fog Computing [2], and Internet of Vehicles (IoV) [3], the SDN network has broader application prospects, and it can combine with Network Function Virtualization (NFV), Machine Learning [4], Load Balancing [5] and other technologies to generate greater potential value. Therefore, it has extremely high value to research the test work of SDN networks.…”