Wing Cheong Lau

Self-similar traffic generation: the random midpoint displacement algorithm and its properties

Recent measurement studies have shown that the burstiness of packet traffic is associated with long-range correlations that can be efficiently described in terms of fractal or self-similar models e.g., fractional Brownian motion (FBM). While FBM models are an attractive alternative to traditional models in terms of parsimony, comprehensive queueing solutions of these models are lacking at present. For this reason, simulation studies that make use of synthetically generated traces from FBM-based traffic processes become crucially important for gaining a better understanding of queueing and network-related performance issues. To this end, it is essential to be able to accurately and quickly generate long traces from FBM processes, In this paper, we consider an approximate FBM generation method known as the random midpoint displacement (RMD) algorithm and perform extensive statistical analyses on a variety of traces generated via RMD. Our analysis indicates that (i) RMD is attractive for qualitative studies (ii) for quantitative studies the parameters of the generated traces may differ from their target values. Such discrepancies can partly be explained by the natural variability of the FBM process, and parameter estimation errors. The approximation to FBM can be improved by using aggregated versions of the traces generated by RMD. We also discuss the application of RMD to traffic interpolation, i.e., inferring traffic measurements on fine time scales from actual measurements made over coarse time scales.

Anonymous Tracking Using RFID Tags

The increasing use of RFID tags in many applications have brought forth valid concerns of privacy and anonymity among users. One of the primary concerns with RFID tags is their ability to track an individually tagged entity. While this capability is currently thought to be necessary for supporting some features of RFID systems, such practice can lead to potential privacy violations. In this paper, we propose a privacy-preserving scheme that enables anonymous estimation of the cardinality of a dynamic set of RFID tags, while allowing the set membership to vary in both the spatial and temporal domains. In addition, the proposed scheme can identify the dynamics of the changes in the tag set population. The main idea of the scheme is to avoid explicit identification of tags. We demonstrate that the proposed scheme is highly adaptive and can accurately estimate tag populations across many orders of magnitude, ranging from a few tens to millions of tags. The associated probing latency is also substantially lower (les 10%) than that of the schemes which require explicit tag identification. We also show that our proposed scheme performs well even in highly dynamic environments, where the tag set keeps changing rapidly.

PacketScore: a statistics-based packet filtering scheme against distributed denial-of-service attacks

Distributed denial-of-service (DDoS) attacks are a critical threat to the Internet. This paper introduces a DDoS defense scheme that supports automated online attack characterizations and accurate attack packet discarding based on statistical processing. The key idea is to prioritize a packet based on a score which estimates its legitimacy given the attribute values it carries. Once the score of a packet is computed, this scheme performs score-based selective packet discarding where the dropping threshold is dynamically adjusted based on the score distribution of recent incoming packets and the current level of system overload. This paper describes the design and evaluation of automated attack characterizations, selective packet discarding, and an overload control process. Special considerations are made to ensure that the scheme is amenable to high-speed hardware implementation through scorebook generation and pipeline processing. A simulation study indicates that packetscore is very effective in blocking several different attack types under many different conditions

Analytical Models and Performance Evaluation of Drive-thru Internet Systems

Drive-thru Internet systems are multiple-access wireless networks in which users in moving vehicles can connect to a roadside access point (AP) to obtain Internet connectivity for some period of time as the vehicles pass through the APs coverage range. In order to evaluate the type of communication services and the quality-of-service that these systems can provide, in this paper, we investigate the data communication performance of a vehicle in Drive-thru Internet systems. In particular, we derive analytical models with tractable solutions to characterize the average and the distribution of the number of bytes downloaded by a vehicle by the end of its sojourn through an APs coverage range, in the presence of other vehicles contending for the same APs resources. Our models are able to quantify the impact of road traffic density, vehicle speed, service penetration rate, APs transmission range and the corresponding bit rate, on the amount of data downloaded by an individual vehicle. In terms of analysis technique, we map the study of our vehicular data downloading process into the transient analysis of a series of Markov reward processes. Our use of Markov reward model is novel in the sense that we only select from the corresponding Markov chain, a subset of relevant sample paths that matches the required behavior of our vehicular flow model. We also validate our proposed analytical models through extensive simulations, driven by empirical vehicular traffic traces. We believe our work offers a unique analytical framework based on which the interplay between vehicular traffic parameters and a vehicles data communication performance in a Drive-thru Internet system can be studied and optimized in a systematic, quantitative manner.

An Empirical Study on the Capacity and Performance of 3G Networks

This paper presents the findings of an extensive measurement study on multiple commercial third-generation (3G) networks. We have investigated the performance of those 3G networks in terms of their data throughput, latency, video and voice call handling capacities, and their ability to provide service guarantees to different traffic classes under saturated and lightly loaded network conditions. Our findings point to the diverse nature of the network resources allocation mechanisms and the call admission control policies adopted by different operators. It is also found that the 3G network operators seem to have extensively customized their network configurations in a cell-by-cell manner according to the individual sites local demographics, projected traffic demand, and the target coverage area of the cell. As such, the cell capacity varies widely not only across different operators but also across different measurement sites of the same operator. The results also show that it is practically impossible to predict the actual capacity of a cell based on known theoretical models and standard parameters, even when supplemented by key field measurements such as the received signal-to-noise ratio Ec/N0.

ALPi: A DDoS Defense System for High-Speed Networks

Distributed denial-of-service (DDoS) attacks pose a significant threat to the Internet. Most solutions proposed to-date face scalability problems as the size and speed of the network increase, with no widespread DDoS solution deployed in the industry. PacketScore has been proposed as a proactive DDoS defense scheme, which detects DDoS attacks, differentiates attack packets from legitimate ones with the use of packet scoring (where the score of a packet is calculated based on attribute values it possesses), and discards packets whose scores are lower than a dynamic threshold. In this paper, we propose ALPi, a new scheme which extends the packet scoring concept with reduced implementation complexity and enhanced performance. More specifically, a leaky-bucket overflow control scheme simplifies the score computation, and facilitates high-speed implementation. An attribute-value-variation scoring scheme analyzes the deviations of the current traffic attribute values, and increases the accuracy of detecting and differentiating attacks. An enhanced control-theoretic packet discarding method allows both schemes to be more adaptive to challenging attacks such as those with ever-changing signatures and intensities. When combined together, the proposed extensions not only greatly reduce the memory requirement and implementation complexity but also substantially improve the accuracies in attack detection and packet differentiation. This makes ALPi an attractive DDoS defense system amenable for high-speed hardware implementation

An Empirical Study on 3G Network Capacity and Performance

This paper presents the findings of an extensive measurement study on multiple commercial 3G UMTS networks. We have investigated the performances of those 3G networks in terms of their data throughput, latency, video and voice calls handling capacities, and their ability to provide service guarantees to different traffic classes under various loading conditions. Our findings indicate the diverse nature of network resources allocation and call admission control policies employed by different operators. It is also found that the 3G network operators seem to have extensively customized their network configurations in a cell-by-cell manner according to the individual sites local demographics, projected traffic demand and the target coverage area of the cell. As such, the cell capacity varies widely not only across different operators but also across different measurement sites of the same operator. Even for the same site, the capacity can easily change by more than 10% across multiple measurements taken at different time of the day. The results also show that it is practically impossible to predict the actual capacity of a cell based on known theoretical models and standard parameters, even when supplemented by key field measurements such as the received signal-to-noise ratio (Ec/N0).

Performance Modeling of Epidemic Routing with Heterogeneous Node Types

The delay performance of delay tolerant networks (DTN) can be improved by adding or replacing mobile nodes with higher mobility or transmit power. In this paper, we examine the design trade-offs in heterogeneous DTNs with two types of mobile relay nodes: normal and super. First we present the range of parameters in the Random Direction (RD) mobility model in which we have validated the Markovian assumption on the node inter-encounter intervals. Next, we describe the two-dimensional continuous time Markov chain (CTMC) model with absorption state, used for evaluating the performance of the heterogeneous DTNs. We demonstrate that the performance improvement of adding super nodes is not linear. For example, replacing 10% of the normal nodes with super nodes ones can achieve 40% of the delay reduction versus replacing all of them. Finally, Fluid Flow Approximation (FFA) and Moment Closure Methods for solving the CTMC with various error rates (about 10%) were developed to allow faster analysis of networks with large number of nodes.

An Analysis of Opportunistic Routing in Wireless Mesh Network

Recently, the idea of opportunistic routing has been widely explored to improve the performance of multi-hop wireless mesh networks. Most of the previous studies use simulations or empirical measurements to evaluate the performance gain of opportunistic routing and therefore are limited to relatively few types of scenarios. In this paper, we take an analytical approach to study the potential gain of opportunistic routing in multi- hop wireless networks. Unlike other analytical studies which use a deterministic channel model, our approach captures the key characteristics of opportunistic routing, i.e. its ability to take advantage of the numerous, yet unreliable wireless links in the network in a probabilistic manner and study the effectiveness of opportunistic routing under diverse radio propagation environment using lognormal shadowing and Rayleigh fading models. Our results show that, under typical network configurations and neglect overhead, the average progress per transmission of opportunistic routing in lognormal shadowing (Rayleigh fading) environment is about 3 (1.5) times higher than that of traditional unicast routing. Finally, we also demonstrate the potential benefits of using different forwarding regions and directional antennas in opportunistic routing.

Traffic analysis in large-scale high-speed integrated networks: validation of nodal decomposition approach

The conditions under which nodal decomposition can be applied for networkwide, multimedia traffic analysis are determined. Through extensive simulation studies of individual departure source characteristics and intersource cross-correlation at the output side of a network node, the nodal decomposition approach is validated for large-scale high-speed, integrated networks. Both homogeneous and heterogeneous traffic environments in which individual sources are modeled as various two-state/multiple-state Markov-modulated processes are considered. By applying the validated nodal decomposition approach, the problem of analyzing the performance of a multimedia network as a whole becomes tractable. Each ATM node is modeled by a queue with infinite buffers and a deterministic server.<<ETX>>