10161187
Accuracy of link capacity estimates using passive and active approaches with CapProbe
CapProbe is an inexpensive and accurate means to estimate capacity. CapProbe combines both dispersion and end-to-end delay to estimate the capacity of the narrowest link on a path. We evaluate in this paper the accuracy of CapProbe estimation, and its dependence on end systems speed, packet sizes, narrow link speeds, and other system parameters. We test kernel and user level implementations of CapProbe and find the kernel implementation to be much more accurate. We also evaluate through experiments the effect of probing packet size on the accuracy of CapProbe estimation. Finally, we explore the idea of a "passive CapProbe" within the context of a TCP flow. Passive here means that the dispersion and delay observed for the TCP flow data and ACK packets, without introducing any additional probing packets. We test active and passive versions of CapProbe with TCP. The active version is found to produce more accurate capacity estimates than the passive version.
62256007
CapProbe: a simple and accurate capacity estimation technique
We present a new capacity estimation technique, called CapProbe. CapProbe combines delay as well as dispersion measurements of packet pairs to filter out samples distorted by cross-traffic. CapProbe algorithms include convergence tests and convergence speed-up techniques by varying probing parameters. Our study of CapProbe includes a probability analysis to determine the time it takes CapProbe to converge on the average. Through simulations and measurements, we found CapProbe to be quick and accurate across a wide range of traffic scenarios. We also compared CapProbe with two previous well-known techniques, pathchar and pathrate. We found CapProbe to be much more accurate than pathchar and similar in accuracy to pathrate, while providing faster estimation than both. Another advantage of CapProbe is its lower computation cost, since no statistical post processing of probing data is required.
8697404
Audio streaming over Bluetooth: an adaptive ARQ timeout approach
Streaming audio has become a popular form of media on the Internet. As wireless personal area network architectures (e.g. Bluetooth) are now targeted to support multimedia traffic, streaming audio over these technologies give rise to interesting applications. But, the variable nature of the wireless medium will not lend itself easily to supporting audio streaming. We focus on Bluetooth and propose, an enhancement to the Bluetooth link layer ARQ mechanism to compensate for channel degradation and to better support audio streaming. Specifically, our scheme adoptively sets the ARQ timeout value based on current channel measurements. We show through testbed experiments that the adaptive ARQ improves the streaming quality significantly compared to the "vanilla" link layer of Bluetooth, especially in noisy environments. Our proposed approach is simple to implement and can actually be extended to the link layer of any wireless technology.
8213932
End-to-End Asymmetric Link Capacity Estimation
Knowledge of link capacity is important for network design, management, and utilization. With the increasing popularity of asymmetric link technologies (such as DSL, 1xRTT, and satellite links), it is desirable to have a capacity estimation technique, which can simultaneously measure forward and backward direction link capacities on an Internet path. Moreover, this estimation must often be “sender only”, because of receiver limitations or lack of standards. In this study, we propose a simple, fast and accurate technique, called AsymProbe, to estimate asymmetric link capacities. AsymProbe is a “sender only”, round trip procedure. It achieves asymmetric link capacity estimation by strategically altering the ratio of probe and acknowledgement packet sizes. Using simulation and testbed experiments, we validate AsymProbe with a variety of network configurations. The results show that AsymProbe can correctly estimate the asymmetric link capacities as long as an appropriate packet size ratio can be employed.
10353520
Finding Self-Similarities in Opportunistic People Networks
Opportunistic network is a type of delay tolerant networks (DTN) where network communication opportunities appear opportunistic. In this study, we investigate opportunistic network scenarios based on public network traces, and our contributions are the following: First, we identify the censorship issue in network traces that usually leads to strongly skewed distribution of the measurements. Based on this knowledge, we then apply the Kaplan-Meier Estimator to calculate the survivorship of network measurements, which is used in designing our proposed censorship removal algorithm (CRA) that is used to recover censored data. Second, we perform a rich set of analysis illustrating that UCSD and Dartmouth network traces show strong self-similarity, and can be modeled as such. Third, we pointed out the importance of these newly revealed characteristics in future development and evaluation of opportunistic networks.
7815226
Peak Load Forecasting Using the Self-organizing Map
This paper aims to study the short-term load forecasting of electricity by using an extended self-organizing map. We first adopt a traditional Kohonen self-organizing map (SOM) to learn time-series load data with weather information as parameters. Then, in order to improve the accuracy of the prediction, an extension of SOM algorithm based on error-correction learning rule is used, and the estimation of the peak load is achieved by averaging the output of all the neurons. Finally, as an implementation example, data of electricity demand from New York Independent System Operator (ISO) are used to verify the effectiveness of the learning and prediction for the proposed methods.
2603707
Evaluating Critiquing-based Recommender Agents
We describe a user study evaluating two critiquing-based recommender agents based on three criteria: decision accuracy. decision effort, and user confidence. Results show that user-motivated critiques were more frequently applied and the example critiquing system employing only this type of critiques achieved the best results. In particular, the example critiquing agent significantly improves users' decision accuracy with less cognitive effort consumed than the dynamic critiquing recommender with system-proposed critiques. Additionally, the former is more likely to inspire users' confidence of their choice and promote their intention to purchase and return to the agent for future use.
441449
A novel ant clustering algorithm based on cellular automata
Based on the principle of cellular automata in artificial life, an artificial ant sleeping model (ASM) and an ant algorithm for cluster analysis (A4C) are presented. Inspired by the behaviors of gregarious ant colonies, we use the ant agent to represent a data object. In ASM, each ant has two states: a sleeping state and an active state. The ant's state is controlled by a function of the ant's fitness to the environment it locates and a probability for the ants becoming active. The state of an ant is determined only by its local information. By moving dynamically, the ants form different subgroups adaptively, and hence the data objects they represent are clustered. Experimental results show that the A4C algorithm on ASM is significantly better than other clustering methods in terms of both speed and quality. It is adaptive, robust and efficient, achieving high autonomy, simplicity and efficiency.
1252625
Supporting self-adaptation in streaming data mining applications
There are many application classes where the users are flexible with respect to the output quality. At the same time, there are other constraints, such as the need for real-time or interactive response, which are more crucial. This paper presents and evaluates a runtime algorithm for supporting adaptive execution for such applications. The particular domain we target is distributed data mining on streaming data. This work has been done in the context of a middleware system called GATES (grid-based adaptive execution on streams) that we have been developing. The self-adaptation algorithm we present and evaluate in this paper has the following characteristics. First, it carefully evaluates the long-term load at each processing stage. It considers different possibilities for the load at a processing stage and its next stages, and decides if the value of an adaptation parameter needs to be modified, and if so, in which direction. To find the ideal new value of an adaptation parameter, it performs a binary search on the specified range of the parameter. To evaluate the self-adaptation algorithm in our middleware, we have implemented two streaming data mining applications. The main observations from our experiments are as follows. First, our algorithm is able to quickly converge to stable values of the adaptation parameter, for different data arrival rates, and independent of the specified initial value. Second, in a dynamic environment, the algorithm is able to adapt the processing rapidly. Finally, in both static and dynamic environments, the algorithm clearly outperforms the algorithm described in our earlier work and an obvious alternative, which is based on linear-updates.
10568129
The HIV positive selection mutation database
The HIV positive selection mutation database is a large-scale database available at that provides detailed selection pressure maps of HIV protease and reverse transcriptase, both of which are molecular targets of antiretroviral therapy. This database makes available for the first time a very large HIV sequence dataset (sequences from ∼50 000 clinical AIDS samples, generously contributed by Specialty Laboratories, Inc.), which makes possible high-resolution selection pressure mapping. It provides information about not only the selection pressure on individual sites but also how selection pressure at one site is affected by mutations on other sites. It also includes datasets from other public databases, namely the Stanford HIV database [S. Y. Rhee, M. J. Gonzales, R. Kantor, B. J. Betts, J. Ravela and R. W. Shafer (2003) Nucleic Acids Res., 31, 298–303]. Comparison between these datasets in the database enables cross-validation with independent datasets and also specific evaluation of the effect of drug treatment.