173 research outputs found

    Robust routing

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    In a network, traffic demands are known with a degree of uncertainty, traffic engineering should take into account the traffic variability. In this research work we focus on the robust routing under changing network conditions. Daily Internet traffic pattern shows that network is vulnerable to malicious attacks, denial of service attacks, worms and viruses. Oblivious routing has a substantially better performance than open shortest path first [OSPF] routing for different level of uncertainty. We propose a theoretical framework for Robust Routing aiming to improve online and offline traffic engineering approaches

    Robust traffic engineering

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    Phenomenal growth of Internet applications in recent years have made it difficult to forecast traffic patterns. Daily Internet traffic patterns shows that the network is vulnerable to malicious attacks, flash crowds and denial of service attacks (DDoS). In this paper, we present a robust routing technique (RRT) that attempts to deal with both normal routing conditions and transient failures. Our simulation results are compared with OSPF-TE. The key advantage of RRT is its convergence to generate the solution. It converges quickly to produce the simulation result on the family of topologies we consider in this paper. We are aiming to combine the best of proactive and reactive traffic engineering in RRT

    Robust routing under dynamic traffic demands

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    In order to provide service reliability with reasonable quality, it is essential for the network operator to manage the traffic flows in the core network. Managing traffic in the network is performed as routing function. In the traditional traffic management, network operator can tune routing parameters to simply manage the traffic. But traditional routing methods are not designed to handle the sudden fluctuations in the traffic. As a result, this may apparently lead to the traffic congestions in some parts of the core network, leaving other part underutilized. In this thesis we explore issues related to the routing robustness in the face of traffic demand variations. We investigate different routing methods for efficient routing using maximum link utilization (MLU) as a performance metric. The primary advantage of using link utilization is its ease to compute the network performance on real network data and synthetic data. Overloaded links might result in Quality of Service degradation (e.g. larger packet delay, packet losses etc.), so MLU might be a useful measure of network performance. For the experimentation, we have used unique data from the real operational network available in the public domain and the random data for large network topology instances. Furthermore, we propose a simple routing algorithm called Robust Routing Technique (RRT) to implement a robust routing mechanism. This mechanism allows network operator to satisfy the networking goals such as load balancing, routing robustness to the range of traffic demand matrices, link failures or to the traffic changes caused by uncertain traffic demands. Simulation experiments with real network topologies and random topologies demonstrate that our routing solution is simple (for routing) and flexible (for forwarding). K-Shortest path implementation in RRT can be extended for Multi Protocol Label Switching. Finally, we evaluate the performance of robust routing under dynamic traffic demands. We formulate the problem as a multi commodity flow problem using linear programming. We use congestion ratio to define the robust routing performance. We provide a variant to the existing robust routing mechanisms by modelling traffic demand due to Distributed Denial of service attacks or worms. Simulation results are compared with the popular OSPF traffic engineering algorithm to provide effectiveness to the proposed routing scheme. Simulation results are compared with the popular OSPF traffic engineering algorithm to provide effectiveness to the proposed routing scheme

    Early Experiences in Traffic Engineering Exploiting Path Diversity: A Practical Approach

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    Recent literature has proved that stable dynamic routing algorithms have solid theoretical foundation that makes them suitable to be implemented in a real protocol, and used in practice in many different operational network contexts. Such algorithms inherit much of the properties of congestion controllers implementing one of the possible combination of AQM/ECN schemes at nodes and flow control at sources. In this paper we propose a linear program formulation of the multi-commodity flow problem with congestion control, under max-min fairness, comprising demands with or without exogenous peak rates. Our evaluations of the gain, using path diversity, in scenarios as intra-domain traffic engineering and wireless mesh networks encourages real implementations, especially in presence of hot spots demands and non uniform traffic matrices. We propose a flow aware perspective of the subject by using a natural multi-path extension to current congestion controllers and show its performance with respect to current proposals. Since flow aware architectures exploiting path diversity are feasible, scalable, robust and nearly optimal in presence of flows with exogenous peak rates, we claim that our solution rethinked in the context of realistic traffic assumptions performs as better as an optimal approach with all the additional benefits of the flow aware paradigm

    Exploiting the power of multiplicity: a holistic survey of network-layer multipath

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    The Internet is inherently a multipath network: For an underlying network with only a single path, connecting various nodes would have been debilitatingly fragile. Unfortunately, traditional Internet technologies have been designed around the restrictive assumption of a single working path between a source and a destination. The lack of native multipath support constrains network performance even as the underlying network is richly connected and has redundant multiple paths. Computer networks can exploit the power of multiplicity, through which a diverse collection of paths is resource pooled as a single resource, to unlock the inherent redundancy of the Internet. This opens up a new vista of opportunities, promising increased throughput (through concurrent usage of multiple paths) and increased reliability and fault tolerance (through the use of multiple paths in backup/redundant arrangements). There are many emerging trends in networking that signify that the Internet's future will be multipath, including the use of multipath technology in data center computing; the ready availability of multiple heterogeneous radio interfaces in wireless (such as Wi-Fi and cellular) in wireless devices; ubiquity of mobile devices that are multihomed with heterogeneous access networks; and the development and standardization of multipath transport protocols such as multipath TCP. The aim of this paper is to provide a comprehensive survey of the literature on network-layer multipath solutions. We will present a detailed investigation of two important design issues, namely, the control plane problem of how to compute and select the routes and the data plane problem of how to split the flow on the computed paths. The main contribution of this paper is a systematic articulation of the main design issues in network-layer multipath routing along with a broad-ranging survey of the vast literature on network-layer multipathing. We also highlight open issues and identify directions for future work

    Experience-driven Control For Networking And Computing

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    Modern networking and computing systems have become very complicated and highly dynamic, which makes them hard to model, predict and control. In this thesis, we aim to study system control problems from a whole new perspective by leveraging emerging Deep Reinforcement Learning (DRL), to develop experience-driven model-free approaches, which enable a network or a device to learn the best way to control itself from its own experience (e.g., runtime statistics data) rather than from accurate mathematical models, just as a human learns a new skill (e.g., driving, swimming, etc). To demonstrate the feasibility and superiority of this experience-driven control design philosophy, we present the design, implementation, and evaluation of multiple DRL-based control frameworks on two fundamental networking problems, Traffic Engineering (TE) and Multi-Path TCP (MPTCP) congestion control, as well as one cutting-edge application, resource co-scheduling for Deep Neural Network (DNN) models on mobile and edge devices with heterogeneous hardware. We first propose DRL-TE, a DRL-based framework that enables experience-driven networking for TE. DRL-TE maximizes a widely-used utility function by jointly learning network environment and its dynamics, and making decisions under the guidance of powerful DNNs. We propose two new techniques, TE-aware exploration and actor-critic-based prioritized experience replay, to optimize the general DRL framework particularly for TE. Furthermore, we propose an Actor-Critic-based Transfer learning framework for TE, ACT-TE, which solves a practical problem in experience-driven networking: when network configurations are changed, how to train a new DRL agent to effectively and quickly adapt to the new environment. In the new network environment, ACT-TE leverages policy distillation to rapidly learn a new control policy from both old knowledge (i.e., distilled from the existing agent) and new experience (i.e., newly collected samples). In addition, we propose DRL-CC to enable experience-driven congestion control for MPTCP. DRL-CC utilizes a single (instead of multiple independent) DRL agent to dynamically and jointly perform congestion control for all active MPTCP flows on an end host with the objective of maximizing the overall utility. The novelty of our design is to utilize a flexible recurrent neural network, LSTM, under a DRL framework for learning a representation for all active flows and dealing with their dynamics. Moreover, we integrate the above LSTM-based representation network into an actor-critic framework for continuous congestion control, which applies the deterministic policy gradient method to train actor, critic, and LSTM networks in an end-to-end manner. With the emergence of more and more powerful chipsets and hardware and the rise of Artificial Intelligence of Things (AIoT), there is a growing trend for bringing DNN models to empower mobile and edge devices with intelligence such that they can support attractive AI applications on the edge in a real-time or near real-time manner. To leverage heterogeneous computational resources (such as CPU, GPU, DSP, etc) to effectively and efficiently support concurrent inference of multiple DNN models on a mobile or edge device, in the last part of this thesis, we propose a novel experience-driven control framework for resource co-scheduling, which we call COSREL. COSREL has the following desirable features: 1) it achieves significant speedup over commonly-used methods by efficiently utilizing all the computational resources on heterogeneous hardware; 2) it leverages DRL to make dynamic and wise online scheduling decisions based on system runtime state; 3) it is capable of making a good tradeoff among inference latency, throughput and energy efficiency; and 4) it makes no changes to given DNN models, thus preserves their accuracies. To validate and evaluate the proposed frameworks, we conduct extensive experiments on packet-level simulation (for TE), testbed with modified Linux kernel (for MPTCP), and off-the-shelf Android devices (for resource co-scheduling). The results well justify the effectiveness of these frameworks, as well as their superiority over several baseline methods
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