Estimating the composition ratios of network services carried in mixed traffic

Network performance management (NPM) and quality of service (QoS) guarantee are the key concerns of network operators and service providers/consumers, respectively. The basis for traffic scheduling is one of the vital elements guiding the implementation of both NPM and QoS assurance. Although traffic identification technologies can theoretically be applied to providing basis for traffic scheduling, the computational complexity caused by the flow-by-flow processing makes them unsuitable for actual large-scale high-throughput network scenarios. In this work, we introduce a new scheme to provide a basis for service-oriented NPM and QoS guarantee. The proposed scheme treats the mixed traffic as a whole and estimates the composition ratios of network services carried in mixed traffic, which makes traffic scheduling for NPM and QoS more reasonable and flexible. To estimate the composition ratios, the observed mixed traffic is expressed as a bipartite graph according to the communication relationship. Then, the bipartite graph is further projected to a multi-dimensional hyper-image based on node coding, random walking and embedding mapping. By this way, we convert the service composition ratio estimation into a problem similar to image classification and enable it to be solved by general machine learning methods like convolutional neural networks. The proposed scheme facilitates batch traffic scheduling and can reorganize the distribution of compositions of network services for each link based on given management strategies. Its computational complexity is significantly lower than that of flow-by-flow processing methods. We validate the proposed solution via real network data and evaluate its performance with some benchmark methods based on traffic identification. The experimental results show that the proposed solution can accurately infer the service composition ratios of mixed traffic with low computational complexity.