We present a novel lightweight incremental quantile estimator which possesses far less complexity than the Tierney's estimator and its extensions. Notably, our algorithm relies only on tuning one single parameter which is a plausible property which we could only find in the discretized quantile estimator Frugal. This makes our algorithm easy to tune for better performance. Furthermore, our algorithm is multiplicative which makes it highly suitable to handle quantile estimation in systems in which the underlying distribution varies with time. Unlike Frugal and our legacy work which are randomized algorithms, we suggest deterministic updates where the step size is adjusted in a subtle manner to ensure the convergence. The deterministic algorithm is more efficient since the estimate is updated at every iteration. The convergence of the proposed estimator is proven using the theory of stochastic learning. Extensive experimental results show that our estimator clearly outperforms legacy works.

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In this paper, we propose a novel solution to the problem of identifying services of high quality. The reported solutions to this problem have, in one way or the other, resorted to using so-called "Reputation Systems" (RSs). Although these systems can offer generic recommendations by aggregating user-provided opinions about the quality of the services under consideration, they are, understandably, prone to "ballot stuffing" and "badmouthing" in a competitive marketplace. In general, unfair ratings may degrade the trustworthiness of RSs, and additionally, changes in the quality of service, over time, can render previous ratings unreliable. As opposed to the reported solutions, in this paper, we propose to solve the problem using tools provided by Learning Automata (LA), which have proven properties capable of learning the optimal action when operating in unknown stochastic environments. Furthermore, they combine rapid and accurate convergence with low computational complexity. In addition to its computational simplicity, unlike most reported approaches, our scheme does not require prior knowledge of the degree of any of the above mentioned problems associated with RSs. Instead, it gradually learns the identity and characteristics of the users which provide fair ratings, and of those who provide unfair ratings, even when these are a consequence of them making unintentional mistakes.Comprehensive empirical results show that our LA-based scheme efficiently handles any degree of unfair ratings (as long as these ratings are binary-the extension to non-binary ratings is "trivial", if we use the S-model of LA computations instead of the P -model). Furthermore, if the quality of services and/or the trustworthiness of the users change, our scheme is able to robustly track such changes over time. Finally, the scheme is ideal for decentralized processing. Accordingly, we believe that our LA-based scheme forms a promising basis for improving the performance of RSs in general.

Abstract-SDN network's policies are updated dynamically at a high pace. As a result, conflicts between policies are prone to occur. Due to the large number of switches and heterogeneous policies within a typical SDN network, detecting those conflicts is a laborious and challenging task. This paper presents two-fold contributions. First, we devise an offline method for detecting unmatched OpenFlow rules, i.e., those rules that are never fired. At the heart of our scheme is a formal approach for predicting the packet's path inside a SDN network. In this perspective, we proffer the taxonomy: invalid and irrelevant anomalies for the unmatched rules. Second, we introduce a new set of definitions for the intra-anomalies, which might occur when using the OpenFlow rule's multi-action feature. We provide some comprehensive experimental results that show the feasibility of our approach and its ability to scale within large SDN network.

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