11787758
Simplification of network dynamics in large systems
We show that when networks are large significant simplicity can be achieved for pricing-based control. We first consider a general loss network with Poisson arrivals and arbitrary holding time distributions. In dynamic pricing schemes, the network provider can charge different prices to the user according to the current utilization level of the network and also other factors. We show that when the system becomes large the performance (in terms of expected revenue) of an appropriately chosen static pricing scheme, whose price is independent of the current network utilization, will approach that of the optimal dynamic pricing scheme. Further, we show that under certain conditions, this static price is independent of the route that the flows take. We then extend the result to the case of dynamic routing, and show that the performance of an appropriately chosen static pricing scheme with bifurcation probability determined by average parameters can also approach that of the optimal dynamic routing scheme when the system is large. These results deepen our understanding of pricing-based network control. In particular, they provide us with the insight that, when the system is large, an appropriate pricing strategy based on the average network conditions (hence, slowly changing) can approach optimality.
7052862
Impact of the Inaccuracy of Distance Prediction Algorithms on Internet Applications - an Analytical and Comparative Study
Distance prediction algorithms use O(N) round trip time (RTT) measurements to predict the N2 RTTs among N nodes. Distance prediction can be applied to improve the performance of a wide variety of Internet applications: for instance, to guide the selection of a download server from multiple replicas, or to guide the construction of overlay networks or multicast trees. Although the accuracy of existing prediction algorithms has been extensively compared using the relative prediction error metric, their impact on applications has not been systematically studied. In this paper, we consider distance prediction algorithms from an application's perspective to answer the following questions: (1) Are existing prediction algorithms adequate for the applications? (2) Is there a significant performance difference between the different prediction algorithms, and which is the best from the application perspective? (3) How does the prediction error propagate to affect the user perceived application performance? (4) How can we address the fundamental limitation (i.e., inaccuracy) of distance prediction algorithms? We systematically experiment with three types of representative applications (overlay multicast, server selection, and overlay construction), three distance prediction algorithms (GNP, IDES, and the triangulated heuristic), and three real-world distance datasets (King, PlanetLab, and AMP). We find that, although using prediction can improve the performance of these applications, the achieved performance can be dramatically worse than the optimal case where the real distances are known. We formulate statistical models to explain this performance gap. In addition, we explore various techniques to improve the prediction accuracy and the performance of prediction-based applications. We find that selectively conducting a small number of measurements based on prediction-based screening is most effective.
2200679
A Distributed Joint Channel-Assignment, Scheduling and Routing Algorithm for Multi-Channel Ad-hoc Wireless Networks
The capacity of ad hoc wireless networks can be substantially increased by equipping each network node with multiple radio interfaces that can operate on multiple non-overlapping channels. However, new scheduling, channel-assignment, and routing algorithms are required to fully utilize the increased bandwidth in multi-channel multi-radio ad hoc networks. In this paper, we develop a fully distributed algorithm that jointly solves the channel-assignment, scheduling and routing problem. Our algorithm is an online algorithm, i.e., it does not require prior information on the offered load to the network, and can adapt automatically to the changes in the network topology and offered load. We show that our algorithm is provably efficient. That is, even compared with the optimal centralized and offline algorithm, our proposed distributed algorithm can achieve a provable fraction of the maximum system capacity. Further, the achievable fraction that we can guarantee is larger than that of some other comparable algorithms in the literature.
19601209
Pricing-Based Control of Large Networks
In this work we study pricing as a mechanism to control large networks. Our model is based on revenue maximization in a general loss network with Poisson arrivals and arbitrary holding time distributions. In dynamic pricing schemes, the network provider can charge different prices to the user according to the current utilization level of the network. We show that, when the system becomes large, the performance of an appropriately chosen static pricing scheme, whose price is independent of the current network utilization, will approach that of the optimal dynamic pricing scheme. Further, we show that under certain conditions, this static price is independent of the route that the connections take. This result has the important implication that distance-independent pricing that is prevalent in current domestic telephone networks in the U.S. may in fact be appropriate not only from a simplicity, but also a performance point of view. We also show that in large systems prior knowledge of the connection holding time is not important from a network revenue point of view.
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.
1863895
Low-Complexity and Distributed Energy Minimization in Multihop Wireless Networks
In this work, we study the problem of minimizing the total power consumption in a multihop wireless network subject to a given offered load. It is well-known that the total power consumption of multihop wireless networks can be substantially reduced by jointly optimizing power control, link scheduling, and routing. However, the known optimal cross-layer solution to this problem is centralized and with high computational complexity. In this paper, we develop a low-complexity and distributed algorithm that is provably power-efficient. In particular, under the node-exclusive interference model and with suitable assumptions on the power-rate function, we can show that the total power consumption of our algorithm is at most (2+¿) times as large as the power consumption of the optimal (but centralized and complex) algorithm, where ¿ is an arbitrarily small positive constant. Our algorithm is not only the first such distributed solution with provable performance bound, but its power-efficiency ratio is also tighter than that of another suboptimal centralized algorithm in the literature.
14356230
Delay Optimizations in Quorum Consensus
The management of replicated data in distributed database systems is a classic problem with great practical importance. Quorum consensus is one of the popular methods, combining with eager replication, for managing replicated data. In this paper, we will investigate the problems of delay-optimal quorum consensus. Firstly, we will show that the problem of minimizing the total-delay (or mean-delay) is NP-hard. However, we can show that the problem restricted to some specific network topology, such as trees, rings, and meshes, can be solved in polynomial time. We also developed an approximate algorithm for a general case. The algorithm gives an approximate ratio less than 2. Secondly, we will present an efficient algorithm, based on the dynamic programming technique, to solve the problem of minimizing the maximal-delay.
21113165
Graphical representations of electronic search patterns
Increasing numbers of researchers and developers are collecting data that trace the processes of humancomputer interactions. Although a variety of methods are used to collect such data, all researchers share common problems in representing and analyzing continuous streams of data. A set of methods and tools for graphically representing the search patterns of users interacting with a variety of databases and hypertexts is under development as part of ongoing research in electronic information seeking. The methodology described in this article involves defining state spaces of user options within a system, capturing keystrokes or mouse moves as raw data, coding these data into the state spaces, and analyzing user moves through the state space as evidence of goal-directed informationseeking strategies and patterns. A tool for displaying user moves through a state space has been developed and is described. The tool graphically represents data in a variety of formats, allowing human pattern recognition capabilities to be applied for data analysis. Graphs can be represented for individual searches or for multiple searches done by either a single searcher or by several searchers working on the same search task.
18690541
Real-time author co-citation mapping for online searching
Author searching is traditionally based on the matching of name strings. Special characteristics of authors as personal names and subject indicators are not considered. This makes it difficult to identify a set of related authors or to group authors by subjects in retrieval systems. In this paper, we describe the design and implementation of a prototype visualization system to enhance author searching. The system, called AuthorLink, is based on author co-citation analysis and visualization mapping algorithms such as Kohonen's feature maps and Pathfinder networks. AuthorLink produces interactive author maps in real time from a database of 1.26 million records supplied by the Institute for Scientific Information. The maps show subject groupings and more fine-grained intellectual connections among authors. Through the interactive interface the user can take advantage of such information to refine queries and retrieve documents through point-and-click manipulation of the authors' names.
46289674
Applying On-line Bitmap Indexing to Reduce Counting Costs in Mining Association Rules
Abstract Counting costs have a great impact on efficiency of mining association rules in a large database of sales transactions. In this paper, we first formally analyze the facts that determine counting costs. Secondly, we present an on-line bitmap indexing technique to speed-up the counting process. Besides theoretical analysis, our implementation reports suggest that this indexing technique may reduce counting costs up to 50%, and almost at no costs.
5163377
Handwritten numeral recognition using MFNN based multiexpert combination strategy
A novel unconstrained numeral recognition system using an MFNN (multilayered feedforward neural net)-based multi-expert combination strategy is proposed. Taking advantage of every method's confidence value as well as its output label, this strategy can improve the system's performance significantly with as few as two experts. Another novel feature of the system is that the multi-expert combination problem is converted into a new classification problem, and then the MFNNs are used to handle the combination adaptively. The proposed system has achieved promising results on the NIST database.