With the widespread adoption of location tracking technologies like GPS, the domain of intelligent transportation services has seen growing interest in the last few years. Services in this domain make use of real-time location-based data from a variety of sources, combine this data with static location-based data such as maps and points of interest databases, and provide useful information to end-users. Some of the major challenges in this domain include i) scalability, in terms of processing large volumes of real-time and static data; ii) extensibility, in terms of being able to add new kinds of analyses on the data rapidly, and iii) user interaction, in terms of being able to support different kinds of one-time and continuous queries from the end-user. In this paper, we demonstrate the use of IBM InfoSphere Streams, a scalable stream processing platform, for tackling these challenges. We describe a prototype system that generates dynamic, multi-faceted views of transportation information for the city of Stockholm, using real vehicle GPS and road-network data. The system also continuously derives current traffic statistics, and provides useful value-added information such as shortest-time routes from real-time observed and inferred traffic conditions. Our performance experiments illustrate the scalability of the system. For instance, our system can process over 120000 incoming GPS points per second, combine it with a map containing over 600,000 links, continuously generate different kinds of traffic statistics and answer user queries.

Blind scheduling policies schedule tasks without knowledge of the tasks' remaining processing times. Existing blind policies, such as FCFS, PS, and LAS, have proven useful in network and operating system applications, but each policy has a separate, vastly differing description, leading to separate and distinct implementations. This paper presents the design and implementation of a configurable blind scheduler that contains a continuous, tunable parameter. By merely changing the value of this parameter, the scheduler's policy exactly emulates or closely approximates several existing standard policies. Other settings enable policies whose behavior is a hybrid of these standards. We demonstrate the practical benefits of such a configurable scheduler by implementing it into the Linux operating system. We show that we can emulate the behavior of Linux's existing, more complex scheduler with a single (hybrid) setting of the parameter. We also show, using synthetic workloads, that the best value for the tunable parameter is not unique, but depends on distribution of the size of tasks arriving to the system. Finally, we use our formulation of the configurable scheduler to contrast the behavior of various blind schedulers by exploring how various properties of the scheduler change as we vary our scheduler's tunable parameter.

It is a commonly held belief that IPv6 provides greater security against random-scanning worms by virtue of a very sparse address space. We show that an intelligent worm can exploit the directory and naming services necessary for the functioning of any network, and we model the behavior of such a worm in this paper. We explore via analysis and simulation the spread of such worms in an IPv6 Internet. Our results indicate that such a worm can exhibit propagation speeds comparable to an IPv4 random-scanning worm. We develop a detailed analytical model that reveals the relationship between network parameters and the spreading rate of the worm in an IPv6 world. We also develop a simulator based on our analytical model. Simulation results based on parameters chosen from real measurements and the current Internet indicate that an intelligent worm can spread surprising fast in an IPv6 world by using simple strategies. The performance of the worm depends heavily on these strategies, which in turn depend on how secure the directory and naming services of a network are. As a result, additional work is needed in developing detection and defense mechanisms against future worms, and our work identifies directory and naming services as the natural place to do it.

A novel single-shot trapezoidal-gradient-based Lissajous trajectory is described and implemented on a 3-tesla magnetic resonance (MR) scanner. A feature of this trajectory is that its sampling points are located on a nonequidistant rectangular grid, which permits the usage of one-dimensional optimal algorithms to increase the robustness and speed of image reconstruction. Another advantage of the trajectory is that two images with different effective echo times can be obtained within a single excitation, which might be used for fast T2* mapping, in functional MR imaging scanning of brain activity associated with mental processes. Potential artifacts in reconstructed images were investigated and methods for suppressing these artifacts were developed. Experiments on normal subjects at rest and during brain activation were performed to demonstrate the feasibility of the new sequence.