State Aware Multiple Choice Routing for Load Balancing in Data

Distributed randomized in-network localised load-balancing (DRILL) is one of the most commonly used protocols and relies on the “power of two choices” algorithm. However, this is a sub-optimal load balancing solution due to under-sampling and high number of candidate ports. Along with per-packet granularity, this sub-optimality increases the occurrence of out-of-order packets. Therefore, in this paper, we proposes a novel power of two choices-based load balancing protocol to overcome these shortcomings. Designated as state aware multiple-choice routing (SAMCR) for load balancing in data centers, it builds a Kalman filtering algorithm; to approximate queue length states using the developed processes and measurement models. Furthermore, it uses the estimated queue length states in an adaptive threshold-based covariance matrix to track the most optimal decision in the system. Mathematical simulations as well as an NS-3 discrete event network simulator were used to evaluate the superior performance of SAMCR over other state-of the art approaches in terms of system load, flow completion time and throughput for most types of flows.