Network virtualization helps overcome shortcomings of the current Internet architecture. The virtualized network architecture enables coexistence of multiple virtual networks (VNs) on an existing physical infrastructure. VN embedding (VNE) problem, which deals with the embedding of VN components onto a physical network, is known to be -hard. In this paper, we propose two VNE algorithms: MaVEn-M and MaVEn-S. MaVEn-M employs the multicommodity flow algorithm for virtual link mapping while MaVEn-S uses the shortest-path algorithm. They formalize the virtual node mapping problem by using the Markov decision process (MDP) framework and devise action policies (node mappings) for the proposed MDP using the Monte Carlo tree search algorithm. Service providers may adjust the execution time of the MaVEn algorithms based on the traffic load of VN requests. The objective of the algorithms is to maximize the profit of infrastructure providers. We develop a discrete event VNE simulator to implement and evaluate performance of MaVEn-M, MaVEn-S, and several recently proposed VNE algorithms. We introduce profitability as a new performance metric that captures both acceptance and revenue to cost ratios. Simulation results show that the proposed algorithms find more profitable solutions than the existing algorithms. Given additional computation time, they further improve embedding solutions.

In this paper, we propose a Q-learning based deflection routing algorithm that may be employed to resolve contention in optical burst-switched networks. The main goal of deflection routing is to successfully deflect a burst based only on a limited knowledge that network nodes possess about their environment. Q-learning, one of the reinforcement learning algorithms, has been proposed in the past to help generate deflection decisions. The complexity of existing reinforcement learning-based deflection routing algorithms depends on the number of nodes in the network.The proposed algorithm scales well for larger networks because its complexity depends on the node degree rather than the network size. The algorithm is implemented using the ns-3 network simulator. Simulation results show that it has comparable performance to an existing reinforcement learning deflection routing scheme while having lower memory requirements.

Abstract:Traffic anomalies in communication networks greatly degrade network performance. Early detection of such anomalies alleviates their effect on network performance. A number of approaches that involve traffic modeling, signal processing, and machine learning techniques have been employed to detect network traffic anomalies.In this paper, we develop various Naive Bayes (NB) classifiers for detecting the Internet anomalies using the Routing Information Base (RIB) of the Border Gateway Protocol (BGP). The classifiers are trained on the feature sets selected by various feature selection algorithms. We compare the Fisher, minimum redundancy maximum relevance (mRMR), extended/weighted/multi-class odds ratio (EOR/WOR/MOR), and class discriminating measure (CDM) feature selection algorithms. The odds ratio algorithms are extended to include continuous features. The classifiers that are trained based on the features selected by the WOR algorithm achieve the highest F-score.

Abstract-In this paper, we introduce a predictive Q-learning deflection routing (PQDR) algorithm for buffer-less networks. Qlearning, one of the reinforcement learning (RL) algorithms, has been considered for routing in computer networks. The RL-based algorithms have not been widely deployed in computer networks where their inherent random nature is undesired. However, their randomness is sought-after in certain cases such as deflection routing, which may be employed to ameliorate packet loss caused by contention in buffer-less networks.We compare the proposed algorithm with two existing reinforcement learning-based deflection routing algorithms. Simulation results show that the proposed algorithm decreases the burst loss probability in the case of heavy traffic load while it requires fewer deflections. The PQDR algorithm is implemented using the ns-3 network simulator.

Abstract-Deflection routing is employed to ameliorate packet loss caused by contention in buffer-less architectures such as optical burst-switched (OBS) networks. The main goal of deflection routing is to successfully deflect a packet based only on a limited knowledge that network nodes possess about their environment.In this paper, we present a framework that introduces intelligence to deflection routing (iDef). iDef decouples the design of the signaling infrastructure from the underlying learning algorithm. It consists of a signaling and a decision-making module. Signaling module implements a feedback management protocol while the decision-making module implements a reinforcement learning algorithm. We also propose several learning-based deflection routing protocols, implement them in iDef using the ns-3 network simulator, and compare their performance.