Online Data Center Traffic Classification Based on Inter-Flow Correlations

Abstract

Today, increasing attention is being paid to Data Center (DC) traffic classification since these infrastructures have become the heart of a variety of time-sensitive and data-intensive service platforms. Classification provides the required tools for better understanding traffic patterns in order to ensure high Quality of Service (QoS) performances and solve scalability problems. Unfortunately, existing classification algorithms cannot deal efficiently with two critical challenges in DC traffic: inter-class imbalance and critical time constraints. In this paper, we propose a novel correlation-based algorithm following a cost-sensitive approach combined with a Bagged Random Forest (BRF) ensemble algorithm, to address the inter-class imbalance problem while meeting time requirements in a data center environment. In this strategy, a new method based on Reverse k-Nearest Neighbors (RkNN) is proposed to capture the rebalancing weights expressing inter-flow correlations, in order to perform an online classification approach. We demonstrate the efficiency of the algorithm by comparing its performance to several existing methods from data level, algorithm level, and cost-sensitive strategies on four real-world datasets. The results reveal that the proposed algorithm outperforms most approaches in the different datasets in terms of precision, recall, F1 measure, AUC and Kappa, as opposed to other algorithms that result in either high precision with low recall and low precision and high recall causing congestion or resource over provisioning.