This paper proposes a dynamic vehicle routing problem (DVRP) model with nonstationary stochastic travel times under traffic congestion. Depending on the traffic conditions, the travel time between two nodes, particularly in a city, may not be proportional to distance and changes both dynamically and stochastically over time. Considering this environment, we propose a Markov decision process model to solve this problem and adopt a rollout-based approach to the solution, using approximate dynamic programming to avoid the curse of dimensionality. We also investigate how to estimate the probability distribution of travel times of arcs which, reflecting reality, are considered to consist of multiple road segments. Experiments are conducted using a real-world problem faced by Singapore logistics/delivery company and authentic road traffic information.

Lately, the Vehicle Routing Problem (VRP) in the city, known as City VRP, has gained popularity with its importance in city logistics. Similar to city logistics, City VRP mainly differs from conventional VRP in terms of the stakeholders involved, namely the shipper, carrier, resident, and administrator. Accordingly, this paper surveys the City VRP literature categorized by stakeholders and summarizes the constraints, models, and solution methods for VRP in urban cities. City VRPs are also analyzed based on the problem of interest considered by the stakeholders and the corresponding models that have been proposed in response. Through this review, we identify the state of the art of City VRP, highlight the core challenging issues, and suggest some potential research area in this field that have remained underexplored.

Denial of Service (DoS) attacks have proven to be a challenging issue for the Internet community. We present a novel approach, active ingress monitoring (AIM), to effectively isolate DoS attacks that use randomly forged source IP addresses. Unlike the existing approaches, AIM reduces the computational overloads by executing the monitoring and filtering operations on selected packet streams only when needed. In addition, our scheme does not require complicated requirements or mandatory participation from every individual network in the Internet. AIM is based on the active networks environment and operates in the network layer based on passive traffic monitoring.

As client/server-based network communications becomes ever-increasingly widespread with the rapid growth of the Internet, security problems have emerged as one of the most pressing issues in the Internet community. Despite the best effort of the Internet community to minimize security problems, vulnerabilities in the network still exist, and various lapses in security have demonstrated the destruction of data integrity and resource availability in the Internet. In particular, a series of occurrences of distributed denial of service (DDoS) have proven to be a challenging issue for Internet service providers (ISPs) and content providers alike. We present a novel approach, ACtive edgeTagging (ACT), which facilitates the handling of source-spoofed attacks by effectively detecting, identifying, and isolating intrusions in the network layer. Unlike the existing solutions, ACT deals with the targeted attacks efficiently without over-bearing requirements or mandatory participation of every individual network in the Internet. ACT is particularly effective for identifying and isolating attackers employing DDoS type of intrusion schemes, and it is highly scalable and extensible as well as feasible to implement in large-scale networks such as the global Internet.

Network performance monitoring is essential for managing a network efficiently and for ensuring reliable operation of the network. In this paper we introduce a scalable network monitoring framework, (NOMAD), that detects network anomalies through the characterization of the dynamic statistical properties of network traffic. NOMAD relies on high resolution measurements and on-line analysis of network traffic to provide real-time alarms in the incipient phase of network anomalies. It incorporates a suite of anomaly identification algorithms based on path changes, flow shift, and packet delay variance, and relies extensively on IP packet header information, such as TTL, source/destination address and packet length, and router's timestamps. NOMAD can be deployed in a single backbone router or incrementally in a regional or large scale network for detecting and locating network anomalies by correlating spatial and temporal network state information.

In this paper we overview the implementation of TCP Boston-a novel fragmentation-tolerant transport protocol, especially suited for ATM's 53-byte cell-oriented switching architecture. TCP Boston integrates a standard TCP/IP protocol, such as Reno or Vegas, with a powerful redundancy control mechanism based on AIDA-an adaptive version of Rabin's IDA dispersal and reconstruction algorithms. Our results show that TCP Boston improves TCP/IP's performance over ATMs for both network-centric metrics (e.g., effective throughput) and application-centric metrics (e.g., response time).

We propose a new transport protocol, TCP Boston, that turns the ATM's 53-byte cell-oriented switching architecture into an advantage for TCP/IP. At the core of TCP Boston is the adaptive information dispersal algorithm (AIDA), an efficient encoding technique that allows for dynamic redundancy control. AIDA makes the TCP/IP's performance less sensitive to cell losses, thus ensuring a graceful degradation of the TCP/IP's performance when faced with congested resources. We introduce AIDA and overview the main features of TCP Boston. We present detailed simulation results that show the superiority of our protocol when compared to other adaptations of TCP/IP over ATMs.

Measurements of LAN and WAN traffic show that network traffic exhibits variability on different scales. We examine a mechanism that gives rise to self-similar network traffic and discuss performance. The mechanism we study is the transfer of files or messages whose size is drawn from a heavy-tailed distribution. In a realistic client/server network the degree to which file sizes are heavy-tailed can directly determine the degree of traffic self-similarity at the link level. This causal relationship is robust relative to changes in network resources, network topology, the influence of cross-traffic, and the distribution of interarrival times. Properties of the transport layer play an important role in preserving and modulating this relationship. The reliable transmission and flow control mechanisms of TCP serve to maintain the long-range dependency structure induced by heavy-tailed file size distributions. In contrast, if a non-flow-controlled and unreliable (UDP-based) transport protocol is used, the resulting traffic shows little self-similarity: although still bursty at short time scales, it has little long-range dependence. Performance implications of self-similarity are discussed as represented by various performance measures. Increased self-similarity as expected, results in degradation of performance. Queueing delay, in particular is discussed. Throughput-related measures such as packet loss and retransmission rate, however increase only gradually with increasing traffic self-similarity as long as reliable, flow-controlled transport protocol is used.