14398394
Optimizing routing in structured peer-to-peer overlay networks using routing table redundancy
Structured peer-to-peer (p2p) overlay networks like CAN, Chord, Pastry and Tapestry [3, 6, 5, 9] provide a self-organizing substrate for large-scale peer-to-peer applications. These systems provide efficient, fault-tolerant routing, object location and load balancing within a self-organization overlay network. In this paper, we show how redundant information that is collected as part of the normal overlay maintenance protocol can be exploited to improve the performance of routing, in terms of both the number of routing hops and routing delay penalty. We use Pastry as a concrete example to describe the set of optimizations and to evaluate their improvement in routing performance via a large scale simulation using a realistic network topology model. We then discuss how these optimizations can be applied to other structured p2p overlays.
8326799
Topology-Aware Peer-to-Peer On-demand Streaming
In this paper, we consider large-scale high-bandwidth on-demand media streaming in a dynamic and heterogeneous environment. We present MetaStream, a scalable and distributed content discovery protocol that enables clients across the Internet to self-organize into a topology-aware overlay network, in which they can “cache and relay” a stream among nearby peers. We present the design and implementation of MetaStream and show experimental results obtained in a large-scale emulated environment. Our evaluation shows that MetaStream distributes relaying load among the clients in a topology-aware manner, imposing low link cost to the underlying network and low streaming load on the media server. MetaStream is also highly resilient to node or network failures and is capable of quickly recovering the streaming quality at clients even at high failure rates.
2476194
Assisted Peer-to-Peer Search with Partial Indexing
In the past few years, peer-to-peer (P2P) networks have become a promising paradigm for building a wide variety of distributed systems and applications. The most popular P2P application till today is file sharing, e.g., Gnutella, Kazza, etc. These systems are usually referred to as unstructured, and search in unstructured P2P networks usually involves flooding or random walking. On the other hand, in structured P2P networks (DHTs), search is usually performed by looking up a distributed inverted index. The efficiency of the search mechanism is the key to the scalability of a P2P content sharing system. So far, neither unstructured nor structured P2P networks alone can solve the search problem in a satisfactory way. In this paper, we propose to combine the strengths of both unstructured and structured P2P networks to achieve more efficient search. Specifically, we propose to enhance search in unstructured P2P overlay networks by building a partial index of shared data using a structured P2P network. The index maintains two types of information: the top interests of peers and globally unpopular data, both characterized by data properties. The proposed search protocol, assisted search with partial indexing, makes use of the index to improve search in three ways: first, the index assists peers to find other peers with similar interests and the unstructured search overlay is formed to reflect peer interests. Second, the index also provides search hints for those data difficult to locate by exploring peer interest locality, and these hints can be used for second-chance search. Third, the index helps to locate unpopular data items. Experiments based on a P2P file sharing trace show that the assisted search with a lightweight partial indexing service can significantly improve the success rate in locating data than Gnutella and a hit-rate-based protocol in unstructured P2P systems, while incurring low search latency and overheads.
10950819
An Efficient Sequential RBF Network for Gene Expression-Based Multi-category classification
This paper presents a fast and efficient sequential learning method for RBF networks that can perform classification directly for multi-category cancer diagnosis problems based on microarray data. The recently developed algorithm, referred to as Fast Growing And Pruning-RBF (FGAP-RBF) can perform incremental learning on the future data directly. No training of all the previous data is needed. This character can reduce the learning complexity and improve the learning efficiency and is greatly favored in the real implementation of a gene expression-based cancer diagnosis system. We have evaluated FGAP-RBF algorithm on a benchmark multi-category cancer diagnosis problem based on microarray data, namely GCM dataset. The results indicate that compared with the results available in literature FGAP-RBF algorithm produces a higher classification accuracy with reduced training time and implementation complexity.
195908742
Threshold network synthesis and optimization and its application to nanotechnologies
We propose an algorithm for efficient threshold network synthesis of arbitrary multioutput Boolean functions. Many nanotechnologies, such as resonant tunneling diodes, quantum cellular automata, and single electron tunneling, are capable of implementing threshold logic efficiently. The main purpose of this work is to bridge the current wide gap between research on nanoscale devices and research on synthesis methodologies for generating optimized networks utilizing these devices. While functionally-correct threshold gates and circuits based on nanotechnologies have been successfully demonstrated, there exists no methodology or design automation tool for general multilevel threshold network synthesis. We have built the first such tool, threshold logic synthesizer (TELS), on top of an existing Boolean logic synthesis tool. Experiments with 56 multioutput benchmarks indicate that, compared to traditional logic synthesis, up to 80.0% and 70.6% reduction in gate count and interconnect count, respectively, is possible with the average being 22.7% and 12.6%, respectively. Furthermore, the synthesized networks are well-balanced structurally. The novelty of this work lies in the introduction of the first comprehensive synthesis methodology and tool for general multilevel threshold logic design.
1035628
Joint categorization of queries and clips for web-based video search
Building a video search engine on the Web is a very challenging problem. Compared with web page search, video search has its unique characteristics (such as high volume of data for each video, existence of multi-modal information including meta-data, visual content, audio, closed caption, etc). In this paper, we investigate some promising approaches to boosting the search relevance of a large scale video search engine on the Web. The contribution of our work is three-fold. (1) We developed a specialized video categorization framework which combines multiple classifiers based on different modalities. (2) By learning users' querying history and clicking log, we proposed an automatic query profile generation technique and applied the profile to query categorization. (3) A highly scalable system was developed, which integrates the online query categorization and offline video categorization. Naive Bayes with mixture of multinomials, Maximum Entropy, and Support Vector Machine categorization methods and the profile learn-ing technique were evaluated on a large scale set of video data on the Web. The evaluation of the developed system and user study has indicated that the joint categorization of queries and video data boosts the video search relevance and user search experience. The high efficiency of our approaches is also demonstrated by the good responsiveness of the system for the video search engine on the Web.
8272087
OGSA-based grid workload monitoring
In heterogeneous and dynamic distributed systems like the grid, detailed monitoring of workload and its resulting system performance (e.g. response time) is required to facilitate performance diagnosis and adaptive performance tuning. In this paper, we present a workload monitoring infrastructure for this purpose. The infrastructure classifies and monitors workload across components in grids based on the open grid service architecture (OGSA) in an end-to-end manner. It provides the abilities to assess what components are involved in processing a work unit, to report time elapsed at these components, and to capture concurrency and isolate which components are critical to overall performance observed. These are enclosed in an automatically constructed Response Time Service Petri Net (RT-SPN) model. A tool is provided to accept queries about work units and visualise corresponding RTSPNs. The infrastructure is also designed and implemented so as to be portable, scalable and lightweight.
5416143
A clustering based approach to efficient image retrieval
This paper addresses the issue of effective and efficient content based image retrieval by presenting a novel indexing and retrieval methodology that integrates color, texture, and shape information for the indexing and retrieval, and applies these features in regions obtained through unsupervised segmentation, as opposed to applying them to the whole image domain. In order to address the typical color feature "inaccuracy" problem in the literature, fuzzy logic is applied to the traditional color histogram to solve for the problem to a certain degree. The similarity is defined through a balanced combination between global and regional similarity measures incorporating all the features. In order to further improve the retrieval efficiency, a secondary clustering technique is developed and employed to significantly save query processing time without compromising the retrieval precision. An implemented prototype system has demonstrated a promising retrieval performance for a test database containing 2000 general-purpose color images, as compared with its peer systems in the literature.