DFFR: A flow-based approach for distributed load balancing in Data Center Networks

Abstract

With the increasing need to support high performance and distributed cloud-based computing applications, data centers are employing commodity switches to build multi-rooted trees. An effective distributed, adaptive flow scheduling algorithm is needed to realize the full potential of the multiple parallel paths provided by such networks. The overall aim of this work is to design a load balancer that can maximize the aggregate network utilization. In this paper, the Distributed Flow-by-Flow Fair Routing (DFFR) algorithm is proposed for flow balancing in Data Center Networks. It is a scalable, distributed, and adaptive algorithm designed for maximizing network resources. Our analysis shows that the algorithm has proven theoretical performance bounds, which gives a low variance for the aggregate bandwidth utilization. A simulation study was conducted to compare the performance of the DFFR algorithm with other load balancing algorithms. Our simulation results reveal that the DFFR algorithm outperforms a static routing assignment protocol. It is also compared to Distributed Dynamic Flow Scheduling (DDFS), which is chosen because it is a distributed algorithm like DFFR. DFFR is shown to perform better for random traffic patterns than DDFS, but worse for patterns where hosts always send to the same receiver. The evaluation concludes that the DFFR is an effective load balancer for Data Center Networks with random traffic patterns.