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.
1773095
Delay Optimization in Quorum Consensus
Abstract
 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, combined with eager replication, for
managing replicated data. In this paper we investigate the problems of delay-optimal quorum consensus.
Firstly, we show that the problem of minimizing the total delay (or mean delay) restricted to a ring can be
solved in a constant time in contrast to the existing approximation results. Secondly, we show that the problem
of minimizing the total delay (or mean delay) is NP-hard. Thirdly, we present an approximate algorithm with
an approximate ratio 2; and the approximate algorithm can guarantee the exact solutions for some specific
network topology, such as trees and meshes. Finally, we present an improvement on the existing algorithm
to solve the problem of minimizing the maximal delay; this reduces the time complexity from O(n3 log n) to
O(n3) where n is the number of nodes.
783838
Practical Indexing XML Document for Twig Query
Answering structural queries of XML with index is an important approach of efficient XML query processing. Among existing structural indexes for XML data, F&B index is the smallest index that can answer all branching queries. However, an F&B index for less regular XML data often contains a large number of index nodes, and hence a large amount of main memory. If the F&B index cannot be accommodated in the available memory, its performance will degrade significantly. This issue has practically limited wider application of the F&B index. 
 
In this paper, we propose a disk organization method for the F&B index which shift part of the leave nodes in the F&B index to the disk and organize them judiciously on the disk. Our method is based on the observation that the majority of the nodes in a F&B index is often the leaf nodes, yet their access frequencies are not high. 
 
We select some leaves to output to disk. With the support of reasonable storage structure in main memory and in disk, we design efficient query processing method). We further optimize the design of the F&B index based on the query workload. Experimental results verified the effectiveness of our proposed approach.
7595419
Efficient Computation of k-Medians over Data Streams Under Memory Constraints
In this paper, we study the problem of efficiently computing k-medians over high-dimensional and high speed data streams. The focus of this paper is on the issue of minimizing CPU time to handle high speed data streams on top of the requirements of high accuracy and small memory. Our work is motivated by the following observation: the existing algorithms have similar approximation behaviors in practice, even though they make noticeably different worst case theoretical guarantees. The underlying reason is that in order to achieve high approximation level with the smallest possible memory, they need rather complex techniques to maintain a sketch, along time dimension, by using some existing off-line clustering algorithms. Those clustering algorithms cannot guarantee the optimal clustering result over data segments in a data stream but accumulate errors over segments, which makes most algorithms behave the same in terms of approximation level, in practice. We propose a new grid-based approach which divides the entire data set into cells (not along time dimension). We can achieve high approximation level based on a novel concept called (1−∊)-dominant. We further extend the method to the data stream context, by leveraging a density-based heuristic and frequent item mining techniques over data streams. We only need to apply an existing clustering once to computing k-medians, on demand, which reduces CPU time significantly. We conducted extensive experimental studies, and show that our approaches outperform other well-known approaches.
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.
32681772
Visual MeSH
Visual MeSH is a graphical interface developed to interact with real world databases such as the National Library of Medicine’s MetaThesaurus and MEDLINE. Visual MeSH makes it easy for the user to browse complex relationships through visual representation of thesaurus structures. It helps the user to select controlled vocabulary terms for online searching in the click-andchoose environment.