DTM, Dynamic synchronous Transfer Mode, is a broadband network architecture based on fast circuit-switching augmented with dynamic reallocation of resources. It provides a service based on multicast, multi-rate channels with short set-up delay, and supports applications with real-time requirements

DTM, Dynamic synchronous Transfer Mode, is a broadband network architecture based on fast circuit-switching augmented with dynamic reallocation of resources. It provides a service based on multicast, multi-rate channels with short set-up delay, and supports applications with real

Recently, a number of researchers have investigated a class of graph partitioning algorithms that reduce the size of the graph by collapsing vertices and edges, partition the smaller graph, and then uncoarsen it to construct a partition for the original graph [Bui and Jones, Proc.

One role for workload generation is as a means for understanding how servers and networks respond to variation in load. This enables management and capacity planning based on current and projected usage. This paper applies a number of observations of Web server usage to create a realistic Web

SIS is an interactive tool for synthesis and optimization of sequential circuits. Given a state transition table, a signal transition graph, or a logic-level description of a sequential circuit, it produces an optimized net-list in the target technology while preserving the sequential input

Abstract: Ethernet is a branching broadcast communication system for carrying digital data packets among locally distributed computing stations. The packet transport mechanism provided by Ethernet has been used to build systems which can be viewed as either local computer networks or loosely

the results of the competition, give data for other benchmark domains, and investigate the reasons for the runtime performance of FF compared to HSP.

modifications that may be required both at the transport and network layers to provide good end-to-end performance over high-speed WANs.

To achieve high throughput rates today's computers perform several operations simultaneously. Not only are I/O operations performed concurrently with computing, but also, in multiprocessors, several computing

Many existing rule learning systems are computationally expensive on large noisy datasets. In this paper we evaluate the recently-proposed rule learning algorithm IREP on a large and diverse collection of benchmark problems. We show that while IREP is extremely efficient, it frequently gives error rates higher than those of C4.5 and C4.5rules. We then propose a number of modifications resulting in an algorithm RIPPERk that is very competitive with C4.5rules with respect to error rates, but much more efficient on large samples. RIPPERk obtains error rates lower than or equivalent to C4.5rules on 22 of 37 benchmark problems, scales nearly linearly with the number of training examples, and can efficiently process noisy datasets containing hundreds of thousands of examples.