Bayesian Adaptive Path Allocation Techniques for Intra-Datacenter Workloads

Data center networks (DCNs) are the backbone of many cloud and Internet services. They are vulnerable to link failures, that occur on a daily basis, with a high frequency. Service disruption due to link failure may incur financial losses, compliance breaches and reputation damage. Performance metrics such as packet loss and routing flaps are negatively affected by these failure events. We propose a new Bayesian learning approach towards adaptive path allocation that aims to improve DCN performance by reducing both packet loss and routing flaps ratios. The proposed approach incorporates historical information about link failure and usage probabilities into its allocation procedure, and updates this information on-the-fly during DCN operational time. We evaluate the proposed framework using an experimental platform built with the POX controller and the Mininet emulator. Compared with a benchmark shortest path algorithm, the results show that the proposed methods perform better in terms of reducing the packet loss and routing flaps.