Abstract

Forecasting Internet traffic is receiving an increasing attention from the computer networks domain. Indeed, by improving this task efficient traffic engineering and anomaly detection tools can be developed, leading to economic gains due to better resource management. This paper presents a Neural Network (NN) approach to predict TCP/IP traffic for all links of a backbone network, using both univariate and multivariate strategies. The former uses only past values of the forecasted link, while the latter is based on the neighbor links of the backbone topology. Several experiments were held by considering real-world data from the UK education and research network. Also, different time scales (e.g. every ten minutes and hourly) were analyzed. Overall, the proposed NN approach outperformed other forecasting methods (e.g. Holt-Winters). R&D Algoritmi centre Document type: Part of book or chapter of book

Abstract

Optimizing the way traffic is routed over networks has been of interest since we first started using computer networks to communicate and exchange information. Network technologies are constantly evolving and so do the underlying problems that need to be solved. This thesis focus on studying the recently proposed segment routing technology. In a traditional IP network, packets are routed using shorted paths according to weights that are configured on the network links. Segment routing offers a new way of routing traffic by allowing packets to do some detours on their way. Between these detours, traditional shortest path routing is used, making this technology have almost no usage overhead. We aim at providing a mathematical formalization of segment routing and showcase several of its use cases. We believe that such a formalization is going to help advance the research of the algorithmic aspects of segment routing by providing a solid mathematical foundation and notations which can serve as a starting point for others to research this topic. We leverage segment routing to solve traffic engineering problems, perform network monitoring and provide traffic duplication services that are robust to link failures. (FSA - Sciences de l'ingénieur) -- UCL, 2020

Abstract

Nowadays, huge efforts are made to modernize the air traffic management systems to cope with uncertainty, complexity and sub-optimality. An answer is to enhance the information sharing between the stakeholders. This paper introduces a framework that bridges the gap between air traffic management and air traffic control on the one hand, and bridges the gap between the ground, the approach and the en-route centers on the other hand. An original system is presented, that has three essential components: the trajectory models, the optimization process, and the monitoring process. The uncertainty of the trajectory is modeled with a Bayesian Network, where the nodes are associated to two types of random variables: the time of overflight on metering points of the airspace, and the traveling time of the routes linking these points. The resulting Bayesian Network covers the complete airspace, and Monte- Carlo simulations are done to estimate the probabilities of sector congestion and delays. On top of this trajectory model, an optimization process minimizes these probabilities by tuning the parameters of the Bayesian trajectory model related to overflight times on metering points. The last component is the monitoring process, that continuously updates the situation of the airspace, modifying the trajectories uncertainties according to actual positions of aircraft. After each update, a new optimal set of overflight times is computed, and can be communicated to the controllers as clearances for the aircraft pilots. The paper presents a formal specification of this global optimization problem, whose underlying rationale was derived with the help of air traffic controllers at Thales Air Systems. Comment: SESAR 2nd Innovation Days (2012)

Abstract

The vision of self-driving networks integrates network measurements with network control. Processing data for each of the tasks comprising network control separately might be prohibitive due to the large volume and waste of computational resources. In this work we make the case of using the Weighted Stochastic Block Model (WSBM), a probabilistic model, to learn a task independent representation. In particular, we consider a case study of real-world IP-to-IP communication. The learned representation provides higher level-features for traffic engineering, anomaly detection, or other tasks, and reduces their computational effort. We find that the WSBM is able to accurately model traffic and structure of communication in the considered trace.

Abstract

The advent of Software-Defined Networking with OpenFlow first, and subsequently the emergence of programmable data planes, has boosted lot of research around many networking aspects: monitoring, security, traffic engineering. In the context of network monitoring, most of the proposed solutions show the benefits of data plane programmability by simplifying the complexity of the network with a one big-switch abstraction. Only few papers look at network-wide solutions, but consider the network as non heterogeneous: only composed by programmable devices. In this paper, we argue that the primary challenge for a successful adoption of those solutions is the deployment problem: how to compose and monitor a network consisting of both legacy and programmable switches? We propose an approach for incrementally deploy programmable devices in an ISP network with the goal of monitoring as many distinct network flows as possible. While assessing the benefits of our solution, we realized that proposed network-wide monitoring algorithms might not be optimized for a partial deployment scenario. We then also developed a novel strategy capable of detecting network-wide heavy flows with the same accuracy of state-of-the-art solutions but by relying on less information from the data plane.