18579380
On the Equivalence of Forward and Reverse Query Caching in Peer-to-Peer Overlay Networks
Peer-to-peer systems such as Gnutella and Kazaa are used by millions of people for sharing music and many other files over the Internet, and they account for a significant portion of the Internet traffic. The traffic in a peer-to-peer overlay network is different from that in WWW in that each peer is both a client and a server. This suggests that one can deploy a forward cache at the Internet gateway of a network to reduce the amount of queries going outside, or a revere cache at the gateway to reduce the amount of queries going inside, which in turn reduces the queries that are forwarded outside. In this paper, we study the effectiveness of forward and reverse caching at the gateway via analysis and experimental measurement. Our study shows that forward caching and reverse caching at the gateway are equally effective at reducing query and query reply traffic across the gateway.
43789567
A Self-Organizing Flock of Condors
1998178
A Hierarchical Approach to Internet Distance Prediction
Internet distance prediction gives pair-wise latency information with limited measurements. Recent studies have revealed that the quality of existing prediction mechanisms from the application perspective is short of satisfactory. In this paper, we explore the root causes and remedies for this problem. Our experience with different landmark selection schemes shows that although selecting nearby landmarks can increase the prediction accuracy for short distances, it can cause the prediction accuracy for longer distances to degrade. Such uneven prediction quality significantly impacts application performance. Instead of trying to select the landmark nodes in some "intelligent" fashion, we propose a hierarchical prediction approach with straightforward landmark selection. Hierarchical prediction utilizes multiple coordinate sets at multiple distance scales, with the "right" scale being chosen for prediction each time. Experiments with Internet measurement datasets show that this hierarchical approach is extremely promising for increasing the accuracy of network distance prediction.
481550
Improving Fine-Grained Irregular Shared-Memory Benchmarks by Data Reordering
We demonstrate that data reordering can substantially improve the performance of fine-grained irregular shared-memory benchmarks, on both hardware and software shared-memory systems. In particular, we evaluate two distinct data reordering techniques that seek to co-locate in memory objects that are in close proximity in the physical system modeled by the computation. The effects of these techniques are increased spatial locality and reduced false sharing. We evaluate the effectiveness of the data reordering techniques on a set of five irregular applications from SPLASH-2 and Chaos. We implement both techniques in a small library, allowing us to enable them in an application by adding less than 10 lines of code. Our results on one hardware and two software shared-memory systems show that, with data reordering during initialization, the performance of these applications is improved by 12%-99% on the Origin 2000, 30%-366% on TreadMarks, and 14%-269% on HLRC.
1334946
Program counter based techniques for dynamic power management
Reducing energy consumption has become one of the major challenges in designing future computing systems. We propose a novel idea of using program counters to predict I/O activities in the operating system. We present a complete design of program-counter access predictor (PCAP) that dynamically learns the access patterns of applications and predicts when an I/O device can be shut down to save energy. PCAP uses path-based correlation to observe a particular sequence of program counters leading to each idle period, and predicts future occurrences of that idle period. PCAP differs from previously proposed shutdown predictors in its ability to: (1) correlate I/O operations to particular behavior of the applications and users, (2) carry prediction information across multiple executions of the applications, and (3) attain better energy savings while incurring low mispredictions.
463081
Topology-Aware Routing in Structured Peer-to-Peer Overlay Networks
Structured peer-to-peer (p2p) overlay networks like CAN, Chord, Pastry and Tapestry offer a novel platform for a variety of scalable and decentralized distributed applications. They provide efficient and fault-tolerant routing, object location and load balancing within a self-organizing overlay network. One important aspect of these systems is how they exploit network proximity in the underlying Internet. We present a study of topology-aware routing approaches in p2p overlays, identify proximity neigbor selection as the most promising technique, and present an improved design in Pastry. Results obtained via analysis and via simulation of two large-scale topology models indicate that it is possible to efficiently exploit network proximity in self-organizing p2p substrates. Proximity neighbor selection incurs only a modest additional overhead for organizing and maintaining the overlay network. The resulting locality properties improve application performance and reduce network usage in the Internet substantially. Finally, we show that the impact of proximity neighbor selection on the load balancing in the p2p overlay is minimal.
5144773
Region-of-interest based image resolution adaptation for MPEG-21 digital item
The upcoming MPEG-21 standard proposes a general framework for augmented use of multimedia services in different network environments, for various users with various terminal devices. In the context of image adaptation, terminals with different screen size limitation require the multimedia adaptation engine to adapt image resources intelligently. Saliency map based visual attention analysis provides some intelligence for finding the attention area within the image. In this paper, we improved the standard MPEG-21 metadata driven adaptation engine by using enhanced saliency map based visual attention model which provides a mean to intelligently adapt JPEG2000 image resolution for different terminal devices with varying screen size according to human visual attention.
5939586
Simultaneous time slack budgeting and retiming for dual-Vdd FPGA power reduction
Field programmable dual-Vdd interconnects are effective to reduce FPGA power. Assuming uniform length interconnects, existing work has developed time slack budgeting to minimize power based on estimating the lower bound of power reduction using dual-Vdd for given time slack. In this paper, we show that such lower bound estimation cannot be extended to mixed length interconnects that are used in modern FPGAs. We develop a technique to estimate power reduction using dual-Vdd for mixed length interconnects, and apply linear programming (LP) to solve slack budgeting to minimize power for mixed length interconnects. Experiments show 53% power reduction on average compared to single-Vdd interconnects. Furthermore, this paper presents a simultaneous retiming and slack budgeting algorithm to reduce power in dual-Vdd FPGAs considering placement and flip-flop binding constraints. The algorithm is based on mixed integer and linear programming (MILP) and achieves up to 20% power reduction compared to retiming followed by slack budgeting. We propose a runtime efficient flow to apply simultaneous retiming and slack budgeting only when it is necessary. To the best of our knowledge, this paper is the first; in-depth study of simultaneous retiming and slack budgeting for dual-Vdd programmable FPGA power reduction while considering layout constraints
30629384
Ordering Unsymmetric Matrices into Bordered Block Diagonal Form for Parallel Processing
Large, sparse, unsymmetric systems of linear equations appear frequently in areas such as chemical engineering. One way of speeding up the solution of these linear systems is to solve them in parallel by reordering the unsymmetric matrix into a bordered block-diagonal (BBD) form. A multilevel ordering algorithm is presented in this paper. Numerical results of the algorithm are given to demonstrate that this algorithm gives better orderings than existing algorithms.
14292213
Title extraction from bodies of HTML documents and its application to web page retrieval
This paper is concerned with automatic extraction of titles from the bodies of HTML documents. Titles of HTML documents should be correctly defined in the title fields; however, in reality HTML titles are often bogus. It is desirable to conduct automatic extraction of titles from the bodies of HTML documents. This is an issue which does not seem to have been investigated previously. In this paper, we take a supervised machine learning approach to address the problem. We propose a specification on HTML titles. We utilize format information such as font size, position, and font weight as features in title extraction. Our method significantly outperforms the baseline method of using the lines in largest font size as title (20.9%-32.6% improvement in F1 score). As application, we consider web page retrieval. We use the TREC Web Track data for evaluation. We propose a new method for HTML documents retrieval using extracted titles. Experimental results indicate that the use of both extracted titles and title fields is almost always better than the use of title fields alone; the use of extracted titles is particularly helpful in the task of named page finding (23.1% -29.0% improvements).
28386762
Taxonomy Building and Machine Learning Based Automatic Classification for Knowledge-Oriented Chinese Questions
In this paper, we propose a taxonomy for knowledge-oriented question, and study the machine learning based classification for knowledge-oriented Chinese questions. By knowledge-oriented questions, we mean questions carrying information or knowledge about something, which cannot be well described by previous taxonomies. We build the taxonomy after the study of previous work and analysis of 6776 Chinese knowledge-oriented questions collected from different realistic sources. Then we investigate the new task of knowledge-oriented Chinese questions classification based on this taxonomy. In our approach, the popular SVM learning method is employed as classification algorithm. We explore different features and their combinations and different kernel functions for the classification, and use different performance metrics for evaluation. The results demonstrate that the proposed approach is desirable and robust. Thorough error analysis is also conduced.