9238814
Building Efficient Overlays
Abstract
Peer-to-peer (P2P) and Grid computing systems have emerged as popular models aiming at further utilizing Internet information and resources, complementing the available client–server services. However, the mechanism of peers randomly choosing logical neighbors without any knowledge about underlying physical topology can cause a serious topology mismatch problems between the overlay network and the physical underlying network. The topology mismatch problem brings a great stress in the Internet infrastructure and greatly limits the performance gain from various search or routing techniques in P2P and Grid systems. Aiming at alleviating the mismatch problem and reducing the unnecessary traffic, we have proposed two approaches, adaptive overlay topology optimization (AOTO) and location-aware topology matching (LTM) techniques, to reduce the total traffic cost and average query response time. Both AOTO and LTM are scalable and completely distributed in the sense that they do not require any global knowledge of the whole overlay network when each node is optimizing the organization of its logical neighbors. This paper shows the effectiveness of AOTO and LTM and compares the performance of these two approaches through simulation studies.

35965067
Dynamic Layer Management in Superpeer Architectures
Superpeer unstructured P2P systems have been found to be very effective by dividing the peers into two layers, superlayer and leaf-layer, in which message flooding is only conducted among superlayer and all leaf-peers are represented by corresponding superpeers. However, current superpeer systems do not employ any effective layer management schemes, so the transient and low-capacity peers are allowed to act as superpeers. Moreover, the lack of an appropriate size ratio maintenance mechanism on superlayer to leaf-layer makes the system's search performance far from being optimal. We present one workload model aimed at reducing the weighted overhead of a network. Using our proposed workload model, a network can determine an optimal layer size ratio between leaf-layer and superlayer. We then propose a dynamic layer management algorithm, DLM, which can maintain an optimal layer size ratio and adaptively elect and adjust peers between superlayer and leaf-layer. DLM is completely distributed in the sense that each peer decides to be a superpeer or a leaf-peer independently without global knowledge. DLM could effectively help a superpeer P2P system maintain the optimal layer size ratio and designate peers with relatively long lifetime and large capacities as superpeers, and the peers with short lifetime and low capacities as leaf-peers under highly dynamic network situations. We demonstrate that the quality of a superpeer system is significantly improved under the DLM scheme by comprehensive simulations.
12060317
DiCAS: An Efficient Distributed Caching Mechanism for P2P Systems
Peer-to-peer networks are widely criticized for their inefficient flooding search mechanism. Distributed hash table (DHT) algorithms have been proposed to improve the search efficiency by mapping the index of a file to a unique peer based on predefined hash functions. However, the tight coupling between indices and hosting peers incurs high maintenance cost in a highly dynamic network. To properly balance the tradeoff between the costs of indexing and searching, we propose the distributed caching and adaptive search (DiCAS) algorithm, where indices are passively cached in a group of peers based on a predefined hash function. Guided by the same function, adaptive search selectively forwards queries to "matched" peers with a high probability of caching the desired indices. The search cost is reduced due to shrunk searching space. Different from the DHT solutions, distributed caching loosely maps the index of a file to a group of peers in a passive fashion, which saves the cost of updating indices. Our simulation study shows that the DiCAS protocol can significantly reduce the network search traffic with the help of small cache space contributed by each individual peer
18230
Indexable PLA for Efficient Similarity Search
Similarity-based search over time-series databases has been a hot research topic for a long history, which is widely used in many applications, including multimedia retrieval, data mining, web search and retrieval, and so on. However, due to high dimensionality (i.e. length) of the time series, the similarity search over directly indexed time series usually encounters a serious problem, known as the "dimensionality curse". Thus, many dimensionality reduction techniques are proposed to break such curse by reducing the dimensionality of time series. Among all the proposed methods, only Piecewise Linear Approximation (PLA) does not have indexing mechanisms to support similarity queries, which prevents it from efficiently searching over very large time-series databases. Our initial studies on the effectiveness of different reduction methods, however, show that PLA performs no worse than others. Motivated by this, in this paper, we re-investigate PLA for approximating and indexing time series. Specifically, we propose a novel distance function in the reduced PLA-space, and prove that this function indeed results in a lower bound of the Euclidean distance between the original time series, which can lead to no false dismissals during the similarity search. As a second step, we develop an effective approach to index these lower bounds to improve the search efficiency. Our extensive experiments over a wide spectrum of real and synthetic data sets have demonstrated the efficiency and effectiveness of PLA together with the newly proposed lower bound distance, in terms of both pruning power and wall clock time, compared with two state-of-the-art reduction methods, Adaptive Piecewise Constant Approximation (APCA) and Chebyshev Polynomials (CP).
848425
Location awareness in unstructured peer-to-peer systems
Peer-to-peer (P2P) computing has emerged as a popular model aiming at further utilizing Internet information and resources. However, the mechanism of peers randomly choosing logical neighbors without any knowledge about underlying physical topology can cause a serious topology mismatch between the P2P overlay network and the physical underlying network. The topology mismatch problem brings great stress in the Internet infrastructure. It greatly limits the performance gain from various search or routing techniques. Meanwhile, due to the inefficient overlay topology, the flooding-based search mechanisms cause a large volume of unnecessary traffic. Aiming at alleviating the mismatching problem and reducing the unnecessary traffic, we propose a location-aware topology matching (LTM) technique. LTM builds an efficient overlay by disconnecting slow connections and choosing physically closer nodes as logical neighbors while still retaining the search scope and reducing response time for queries. LTM is scalable and completely distributed in the sense that it does not require any global knowledge of the whole overlay network. The effectiveness of LTM is demonstrated through simulation studies.
6911930
LANDMARC: Indoor Location Sensing Using Active RFID
Growing convergence among mobile computing devices and embedded technology sparks the development and deployment of “context-aware” applications, where location is the most essential context. In this paper we present LANDMARC, a location sensing prototype system that uses Radio Frequency Identification (RFID) technology for locating objects inside buildings. The major advantage of LANDMARC is that it improves the overall accuracy of locating objects by utilizing the concept of reference tags. Based on experimental analysis, we demonstrate that active RFID is a viable and cost-effective candidate for indoor location sensing. Although RFID is not designed for indoor location sensing, we point out three major features that should be added to make RFID technologies competitive in this new and growing market.
15475945
Performance prediction of component-based applications
One of the major problems in building large-scale enterprise systems is anticipating the performance of the eventual solution before it has been built. The fundamental software engineering problem becomes more difficult when the systems are built on component technology. This paper investigates the feasibility of providing a practical solution to this problem. An empirical approach is proposed to determine the performance characteristics of component-based applications by benchmarking and profiling. Based on observation, a model is constructed to act as a performance predictor for a class of applications based on the specific component technology. The performance model derived from empirical measures is necessary to make the problem tractable and the results relevant. A case study applies the performance model to an application prototype implemented by two component infrastructures: CORBA and J2EE.
903407
On integrating fluid models with packet simulation
Fluid models have been shown to he efficient and accurate in modelling large IP networks. However, unlike packet models, it is difficult to extract packet-level information from them. In this paper, we present a hybrid simulation method that maintains the performance advantage of fluid models while providing detailed packet level information for selected packet traffic flows. We propose two models to account for the interaction between background TCP traffic in a fluid network and foreground packet traffic of interest. The first assumes that the packet traffic poses a negligible load on the fluid network whereas the second accounts for the added load by transforming the packet traffic into fluid flows and solving the resulting enhanced fluid model. The first of these yields an efficient one pass solution algorithm whereas the second requires an additional pass to account for the packet traffic load. We establish the correctness of both approaches and present their implementation within ns-2. Comparisons between the hybrid models and a classical packet simulation show the two pass approach to be quite accurate and computationally efficient.
7717376
Enriching speech recognition with automatic detection of sentence boundaries and disfluencies
Effective human and automatic processing of speech requires recovery of more than just the words. It also involves recovering phenomena such as sentence boundaries, filler words, and disfluencies, referred to as structural metadata. We describe a metadata detection system that combines information from different types of textual knowledge sources with information from a prosodic classifier. We investigate maximum entropy and conditional random field models, as well as the predominant hidden Markov model (HMM) approach, and find that discriminative models generally outperform generative models. We report system performance on both broadcast news and conversational telephone speech tasks, illustrating significant performance differences across tasks and as a function of recognizer performance. The results represent the state of the art, as assessed in the NIST RT-04F evaluation
1073181
Keeping Secrets within a Family: Rediscovering Parnas
David Parnas wrote several papers in the 1970’s and 1980’s that are now considered classics. The concepts he advocated such as information hiding and use of abstract interfaces are generally accepted as the appropriate way to design nontrivial software systems. However, not all of what he proposed has been fully appreciated and assimilated into our practices. Many of his simple, elegant ideas have been lost amongst the hype surrounding the technologies and methods that have arisen in the past two decades. This paper examines Parnas’s ideas, especially his emphasis on program families, and proposes that college-level computing science and software engineering curricula should renew their attention to these very important principles and techniques and present them in the context of contemporary software development.
11940833
Efficient trust management policy analysis from rules
This paper describes a systematic method for deriving efficient algorithms and precise time complexities from extended Datalog rules as it is applied to the analysis of trust management policies specified in SPKI/SDSI, a well-known trust management framework designed to facilitate the development of secure and scalable distributed computing systems. The approach of expressing policy analysis problems as extended Datalog rules is much simpler than previous techniques for analysis of SPKI/SDSI policies. Our method also derives better, more precise time complexities than before in addition to generating complete algorithms and data structures. The method is general, with many applications beyond policy analysis. It extends our previous method for Datalog to handle list constructors, external functions, and queries.