DServ‐LB: Dynamic server load balancing algorithm

Abstract

In recent years, web services have been largely accessed by the customer, and it increases the network traffic on the internet. To provide the services for the large number of customer, dynamic clustering concept has been implemented that provides the ability to add or remove the servers on demand. But managing and processing the large set of traffic are very complicated. Load balancing technic helps to resolve the problems of network traffic and give efficient network management. In this paper, we proposed a dynamic server load balancing algorithm (DServ-LB) using OpenFlow switches in software-defined networking. The OpenFlow switches support the dynamic programmability. Also, we used the sFlow protocol, which is used to monitor the servers resource information periodically and the controller. Based on the server resource availability, the controller installs forwarding rules in the OpenFlow switches. For implementation, we used Mininet for network emulation, POX controller, and Docker container as Mininet hosts. The result shows that the proposed DServ-LB improves the overall network performance and efficiently utilizes the server resources if compared with existing load balancing algorithms.

Leveraging Software-Defined Networks for Load Balancing in Data Centre Networks using Linear Programming

“…The work proposed by the author in [20] performs web load balancing using Open Flow switches in software-defined networking, takes server response time and switch port traffic for performing load balancing among a pool of servers. The research in [21] suggested a dynamic server load-balancing algorithm using the sFlow protocol. A dynamic load balancing mechanism that ensures service quality was suggested in [22].…”

Traffic-aware adaptive server load balancing for software defined networks

“…The main aim of load balancing [25][26][27][28] is to improve the throughput avoiding processing delays in optimal path selection to balance the load. The various factors influencing load balancing in DCN include the energy of nodes, residual bandwidth, scalability of the network, types of flows.…”