Vinh Bui

A Markovian Approach to Multipath Data Transfer in Overlay Networks

The use of multipath routing in overlay networks is a promising solution to improve performance and availability of Internet applications, without the replacement of the existing TCP/IP infrastructure. In this paper, we propose an approach to distribute data over multiple overlay paths that is able to improve Quality of Service (QoS) metrics, such as the data transfer time, loss, and throughput. By using the Imbedded Markov Chain technique, we demonstrate that the system under analysis, observed at specific instants, possesses the Markov property. We therefore cast the data distribution problem into the Markov Decision Process (MDP) framework, and design a computationally efficient algorithm named Online Policy Iteration (OPI), to solve the optimization problem on the fly. The proposed approach is applied to the problem of multipath data distribution in various wired/wireless network scenarios, with the objective of minimizing the data transfer time as well as the delay and losses. Through both intensive ns-2 simulations with data collected from real heterogeneous networks and experiments over real networks, we show the superior performance of the proposed traffic control mechanism in comparison with two classical schemes, that are Weighted Round Robin and Join the Shortest Queue.

Evolving stories: Grammar evolution for automatic plot generation

In this paper, we propose a computational framework for automated story-based scenario generation. Under this framework, a regular grammar is developed to model various causal relationships inside a given story world. The grammar is then evolved using evolutionary computation techniques to generate novel story plots, i.e. story-based scenarios. To evaluate these newly generated scenarios, a human-in-the-loop model is used. An experimental study was carried out, in which the proposed framework was used to create novel plots based on the famous Little Red Riding Hood fairy tale. The experimental study demonstrated that evolutionary computation can potentially contribute significantly to story generations. Some challenges were identified including the difficulty to quantify such subjective measures as plot interestingness and creativity.

An MDP-Based Approach for Multipath Data Transmission over Wireless Networks

Maintaining performance and reliability in wireless networks is a challenging task due to the nature of wireless channels. Multipath data transmission has been used in wired scenarios to reduce latency, improve throughput, and - when/where possible - balance the load. In this paper, we propose an approach for multipath data transmission over wireless networks. We demonstrate that the problem under study can be formulated as a Markov decision process (MDP) and we propose an algorithm called On-line Policy Iteration (OPI), to solve the formulated MDP in real time. We verified the proposed approach using simulations with ns-2 and data collected from real heterogeneous wired/wireless networks. The results indicate that we improve both delay and loss characteristics of end-to-end wireless communications outperforming the classical multi-path schemes including Round Robin and Join the Shortest Queue.

Long Horizon End-to-End Delay Forecasts: A Multi-Step-Ahead Hybrid Approach

A long horizon end-to-end delay forecast, if possible, will be a breakthrough in traffic engineering. This paper introduces a hybrid approach to forecast end-to-end delays using wavelet transforms in combination with neural network and pattern recognition techniques. The discrete wavelet transform is implemented to decompose delay time series into a set of wavelet components, which is comprised of an approximate component and a number of detail components. Thus, it turns the problem of long horizon delay forecasting into a set of shorter horizon wavelet coefficient forecasting problems. A recurrent multi-layered perceptron neural network is applied to forecast coefficients of the wavelet approximate component, which represents the trend of the delay series. The k-nearest neighbors technique is used to forecast coefficients of the wavelet detail components, which reflect the burstiness of background traffic. The proposed approach has been verified in both simulation and over real heterogeneous networks showing promising results in terms of averaged normalized root mean square error. In addition, when compared to some existing and well known approaches it presents the superior performance.

Improving multipath live streaming performance with Markov Decision Processes

Recent studies have demonstrated the benefit of multipath live contents streaming in the Internet best-effort environment. In this paper, we propose an approach to enhance performance of the multipath streaming by using Markov decision processes. We formulate the multipath live streaming problem with the minimum distortion rate objective as a Markov decision process (MDP) and solve the formulated MDP on the fly using a computational effective algorithm namely OPI i.e. Online Policy Iteration. We demonstrate the superior performance of the proposed approach against the classical schemes including weighted round robin (WRR) and join the shortest queue (JSQ) through intensive simulation using Ns-2. The approach proposed is transport protocol independent, which implies that it can be used friendly with both UDP and TCP streaming.

From subjective to objective metrics for evolutionary story narration using event permutations

The use of evolutionary computation to automatically narrate a story in a natural language, such as English, is a very daunting task. Two main challenges are addressed in this paper. First, how to represent a story in a form that is simple for evolution to work on? Second, how to evaluate stories using proper objective metrics? We address the first challenge by introducing a permutation-based linear representation that relies on capturing the events in a story in a genome, and on transforming any sequence represented by this genome into a valid story using a genotype-phenotype mapping. This mapping uses causal relationships in a story as constraints. The second challenge is addressed by conducting human-based experiments to collect subjective measurements of two categories of familiar and unfamiliar stories to the participants. The data collected from this exercise are then correlated with objective metrics that we designed to capture the quality of a story. Results reveal interesting relationships that are discussed in details in the paper.

On the role of information networks in logistics: An evolutionary approach with military scenarios

This paper proposes a framework, that incorporates evolutionary computation and wargame simulation, to investigate the role of information networks in organizing efficient supply chains for military logistics. Under the proposed framework, evolutionary computation is used to evolve the information networks, which are subsequently evaluated by playing simulation wargames. Through a series of simulation studies, in which various supply scenarios have been simulated, we have found that information networks play a substantial role in efficient demand estimation. Depending on the level of information uncertainty, i.e. the hostile force strength distribution, different topological characteristics of the information networks, i.e. different information relationships between supply nodes, are favored. The objective of the paper is to discover the fundamental principles for information networks and their interaction with supply chains. These principles are significant for new and/or future military concepts such as network centric warfare. We believe that the proposal of the framework and the discovery of those emergent topological characteristics would significantly contribute to the organizing of efficient supply chains for military logistic operations.

A Game Theoretic Framework for Multipath Optimal Data Transfer in Multiuser Overlay Networks

In this paper, we study the problem of optimal data transfer over multiple overlay paths. Instead of solving the problem from the single controller point of view, we adopt the game theory perspective to consider the problem from a more realistic view where multiple traffic controllers competing for the shared bandwidth. We formulate the problem as a general- sum stochastic game, and a reinforcement learning technique namely Correlated-Q Learning is implemented to derive the best- possible strategy, i.e. the strategy to play correlated equilibrium (CE) for each controller. Through a proof-of-concept simulation scenario with 2 overlay paths and 2 controllers, we show that by playing cooperative strategies, e.g. CE, the controllers can achieve superior performance compared to acting selfishly. The result emphasizes that considering the problem of optimal multipath data transfer from the single controller perspective is inadequate.

Optimal Relay Placement for Maximizing Path Diversity in Multipath Overlay Networks

Providing a high level of the path diversity is a substantial criterion in designing an overlay network. Given a underlying network infrastructure, the diversity of the overlay paths largely depends on how the overlay nodes, i.e. relays, are placed. In this paper, the problem of optimal relay placement is studied, and the goal is to maximize the diversity of the overlay paths while minimizing the number of relays. To tackle this multi-objective optimization problem, a novel genetic algorithm (GA) is applied hybridizing the standard GA and k-shortest path algorithms. By studying 6 real ISP topologies, we find that there exist non-unique solutions, which provide nearly the same level of the path diversity. In those solutions, only few locations are not replaceable allowing a great flexibility in the overlay network deployment. Perhaps surprisingly, these places do not include all those nodes with the highest number of links. The reason is that those nodes with the highest number of links are usually crossed by the default routing paths. Therefore, the overlay paths tend not to crossing them. We also notice that the number of relays required to maintain a high level of the path diversity is only 5% - 10% of the total number of network nodes depending on the topologies. We believe that our findings are useful for designing and deploying multi-path overlay networks.

Modelling Internet End-to-End Loss Behaviours: A New Approach

This paper proposes a new approach to modelling Internet end-to-end loss behaviours. Rather than select the model structure from the loss observations like being done previously, we construct a new loss model based on the TCP congestion control mechanism. Thus, the model can explicitly reflect the correlation between end-to-end loss observations and network flow level activities. Besides simulation, the model has been tested in both wired and wireless Internet environments. The result shows that, unless the losses due to the transmission errors are excessive e.g. in some lossy wireless channels, the model can correctly capture end-to-end loss behaviours not only in terms of average rates but also in terms of loss patterns i.e. loss and good runlengths. This implies a good connection between the model structure and network flow level activities, which makes the model attractive for assisting traffic control