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.
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).
132343
Approaching optimal peer-to-peer overlays
In unstructured peer-to-peer (P2P) systems, there exists a serious topology mismatch problem between physical and logical network. We first analyze the relationship between the property of the overlay and the corresponding message duplications incurred by queries in a given overlay, and prove that computing an optimal overlay with global knowledge is an NP-hard problem. Motivated by the analysis results, we design a distributed overlay optimization algorithm, THANCS, to attack topology mismatch. We demonstrate its performance by comprehensive simulations in dynamic environments. The proposed THANCS has three major strengths. First, it does not need any global knowledge. Second, its optimization convergent speed is fast. Third, it is orthogonal with other types of advanced search approaches.
14694684
A mutual anonymous peer-to-peer protocol design
Peer-to-peer (P2P) computing has become a popular application model because of its easy resource sharing pattern and powerful query search scheme. However, decentralized P2P architecture is scarcely seen to deploy the anonymity on its peers. In this paper, we propose a mutual anonymity protocol, called Secret-sharing-based Mutual Anonymity Protocol (SSMP), for decentralized P2P systems. SSMP employs Shamirs' secret sharing scheme to allow peers to issue queries and responders to deliver requested files anonymously. Compared with previous designs, SSMP achieves mutual anonymity in P2P systems with a high degree of anonymity and a low cryptography processing overhead. We evaluate SSMP by comprehensive simulations.
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.
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
12544972
Fluid models and solutions for large-scale IP networks
In this paper we present a scalable model of a network of Active Queue Management (AQM) routers serving a large population of TCP flows. We present efficient solution techniques that allow one to obtain the transient behavior of the average queue lengths, packet loss probabilities, and average end-to-end latencies. We model different versions of TCP as well as different versions of RED, the most popular AQM scheme currently in use. Comparisons between our models and <tt>ns</tt> simulation show our models to be quite accurate while at the same time requiring substantially less time to solve, especially when workloads and bandwidths are high.
1409797
On the TCP-Friendliness of VoIP Traffic
Recently, some concerns have been raised regarding whether the rapid growth of the UDP traffic due to the Skype-like applications will throttle down regular TCP users because UDP flows are unresponsive to network congestion, i.e., UDP senders do not reduce their sending rates when congestion occurs in the network. However, most UDP based real time applications, such as Voiceover-IP (VoIP), drop out completely if the user perceived quality becomes unacceptable as the network congestion increases. This user back-off mechanism effectively reduces UDP traffic rate at the aggregated level when the network is congested. In this paper, we investigate the fairness issue between VoIP flows and TCP flows under different network environment. Our study explicitly takes into account both transport layer congestion control mechanism built-into TCP protocol stack and the self-adaptiveness of VoIP users. Various models are developed to characterize VoIP user back-off behaviors in response to call quality degradation resulted from packet loss and delay inside the network. The comparison between VoIP user back-off and TCP rate adaption shows that not only VoIP flows consume much less bandwidth than TCP flows, but also VoIP traffic is indeed very responsive to congestion when the network is overloaded.
3170428
Fitting B-spline curves to point clouds by curvature-based squared distance minimization
Computing a curve to approximate data points is a problem encountered frequently in many applications in computer graphics, computer vision, CAD/CAM, and image processing. We present a novel and efficient method, called squared distance minimization (SDM), for computing a planar B-spline curve, closed or open, to approximate a target shape defined by a point cloud, that is, a set of unorganized, possibly noisy data points. We show that SDM significantly outperforms other optimization methods used currently in common practice of curve fitting. In SDM, a B-spline curve starts from some properly specified initial shape and converges towards the target shape through iterative quadratic minimization of the fitting error. Our contribution is the introduction of a new fitting error term, called the squared distance (SD) error term, defined by a curvature-based quadratic approximant of squared distances from data points to a fitting curve. The SD error term faithfully measures the geometric distance between a fitting curve and a target shape, thus leading to faster and more stable convergence than the point distance (PD) error term, which is commonly used in computer graphics and CAGD, and the tangent distance (TD) error term, which is often adopted in the computer vision community. To provide a theoretical explanation of the superior performance of SDM, we formulate the B-spline curve fitting problem as a nonlinear least squares problem and conclude that SDM is a quasi-Newton method which employs a curvature-based positive definite approximant to the true Hessian of the objective function. Furthermore, we show that the method based on the TD error term is a Gauss-Newton iteration, which is unstable for target shapes with high curvature variations, whereas optimization based on the PD error term is the alternating method that is known to have linear convergence.
15838270
Eliminating Dead Code on Recursive Data
This paper describes a general and powerful method for dead code analysis and elimination in the presence of recursive data constructions. We represent partially dead recursive data using liveness patterns based on general regular tree grammars extended with the notion of live and dead, and we formulate the analysis as computing liveness patterns at all program points based on program semantics. This analysis yields a most precise liveness pattern for the data at each program point, which is significantly more precise than results from previous methods. The analysis algorithm takes cubic time in terms of the size of the program in the worst case but is very efficient in practice, as shown by our prototype implementation. The analysis results are used to identify and eliminate dead code. The general framework for representing and analyzing properties of recursive data structures using general regular tree grammars applies to other analyses as well.
389729
Predicting protein folds with structural repeats using a chain graph model
Protein fold recognition is a key step towards inferring the tertiary structures from amino-acid sequences. Complex folds such as those consisting of interacting structural repeats are prevalent in proteins involved in a wide spectrum of biological functions. However, extant approaches often perform inadequately due to their inability to capture long-range interactions between structural units and to handle low sequence similarities across proteins (under 25% identity). In this paper, we propose a chain graph model built on a causally connected series of segmentation conditional random fields (SCRFs) to address these issues. Specifically, the SCRF model captures long-range interactions within recurring structural units and the Bayesian network backbone decomposes cross-repeat interactions into locally computable modules consisting of repeat-specific SCRFs and a model for sequence motifs. We applied this model to predict β-helices and leucine-rich repeats, and found it significantly outperforms extant methods in predictive accuracy and/or computational efficiency.