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With the rapid development of network-based services and applications, current data plane solutions of network node, which focus on packets forwarding traditionally, are not optimally addressing new requirements such as function flexibility, performance expansibility and resource utilization, etc. In this paper, we propose a novel data plane structure called Resource Pooling Switch Architecture (RPSA), which utilizes global shared resource pool to provide different processing functionalities and capacities to different packets/flows. As all network functions are instantiated by the resources connected to the switching fabric and constructed in the form of Service Function Chains (SFC), the traffic patterns changed a lot. We design Balanced Service Capacity based FIRM algorithm (BSC-FIRM) to overcome the deficiencies of classical scheduler in RPSA. Simulation results show that our algorithm outperforms on the aspect of packet switching delay and loss rate compared with FIRM and iSLIP algorithm., Comment: Switch Architecture, Scheduling Algorithm, Network Function Virtualization, Service Function Chain

The disadvantages of the combination of traditional switches and middleboxes have being exposed under the condition of increasingly various network function demands,such as function flexibility, performance scalability and resource utilization. To solve this problem, we design Resource Pooling Switch Architecture (RPSA), which separates some non-essential functions from line card and allocate them in Network Function Pool (NFP) to provide flexible services for data plane in the form of Service Function Chains (SFC). As the performance of the whole system could be decided by whether function deployment is reasonable or not, we purpose heuristic algorithm called Modified Fiduccia-Mattheyses based Two Phase Algorithm (MFMTP) to optimize the deployment of functions. The simulation results show that this algorithm performs well in throughput and convergence., Comment: 6 pages

It is becoming increasingly important for machine learning methods to make predictions that are interpretable as well as accurate. In many practical applications, it is of interest which features and feature interactions are relevant to the prediction task. We present a novel method, Selective Bayesian Forest Classifier, that strikes a balance between predictive power and interpretability by simultaneously performing classification, feature selection, feature interaction detection and visualization. It builds parsimonious yet flexible models using tree-structured Bayesian networks, and samples an ensemble of such models using Markov chain Monte Carlo. We build in feature selection by dividing the trees into two groups according to their relevance to the outcome of interest. Our method performs competitively on classification and feature selection benchmarks in low and high dimensions, and includes a visualization tool that provides insight into relevant features and interactions., Comment: R package: github.com/vkrakovna/sbfc