1827196
Network architecture to support QoS in mobile ad hoc networks [video streaming applications]
We present a quality of service (QoS) architecture for supporting real-time data transmission in mobile ad hoc networks (MANETs). The QoS architecture includes a QoS transport layer, QoS routing, queue management and a priority MAC protocol. Through simulations, we find that the QoS architecture reduces packet delay and greatly improves the quality of real-time video streams in MANETs.
7211665
Dynamic data dependence tracking and its application to branch prediction
To continue to improve processor performance, microarchitects seek to increase the effective instruction level parallelism (ILP) that can be exploited in applications. A fundamental limit to improving ILP is data dependences among instructions. If data dependence information is available at run-time, there are many uses to improve ILP. Prior published examples include decoupled branch execution architectures and critical instruction detection. In this paper, we describe an efficient hardware mechanism to dynamically track the data dependence chains of the instructions in the pipeline. This information is available on a cycle-by-cycle basis to the microengine for optimizing its performance. We then use this design in a new value-based branch prediction design using available register value information (ARVI). From the use of data dependence information, the ARVI branch predictor has better prediction accuracy over a comparably sized hybrid branch predictor With ARVI used as the second-level branch predictor the improved prediction accuracy results in a 12.6% performance improvement on average across the SPEC95 integer benchmark suite.
14979419
QoS-aware routing based on bandwidth estimation for mobile ad hoc networks
Routing protocols for mobile ad hoc networks (MANETs) have been explored extensively in recent years. Much of this work is targeted at finding a feasible route from a source to a destination without considering current network traffic or application requirements. Therefore, the network may easily become overloaded with too much traffic and the application has no way to improve its performance under a given network traffic condition. While this may be acceptable for data transfer, many real-time applications require quality-of-service (QoS) support from the network. We believe that such QoS support can be achieved by either finding a route to satisfy the application requirements or offering network feedback to the application when the requirements cannot be met. We propose a QoS-aware routing protocol that incorporates an admission control scheme and a feedback scheme to meet the QoS requirements of real-time applications. The novel part of this QoS-aware routing protocol is the use of the approximate bandwidth estimation to react to network traffic. Our approach implements these schemes by using two bandwidth estimation methods to find the residual bandwidth available at each node to support new streams. We simulate our QoS-aware routing protocol for nodes running the IEEE 802.11 medium access control. Results of our experiments show that the packet delivery ratio increases greatly, and packet delay and energy dissipation decrease significantly, while the overall end-to-end throughput is not impacted, compared with routing protocols that do not provide QoS support.
15196694
End-to-end diagnosis of QoS violations with neural network
In this paper, we introduce a novel end-to-end approach to QoS management with respect to the diagnosis of QoS violations. We first use a set of end-to-end flow traffic statistics to describe a QoS violation. Subsequently, neural network techniques are engaged to identify and differentiate QoS violations through classification of the collected statistics. Through experiments, we find that our scheme outperforms traditional rule-based methods which require clear margins of QoS parameters in asserting a QoS violation.
14655433
Efficient join processing on uncertain data streams
Join processing in the streaming environment has many practical applications such as data cleaning and outlier detection. Due to the inherent uncertainty in the real-world data, it has become an increasingly important problem to consider the join processing on uncertain data streams, where the incoming data at each timestamp are uncertain and imprecise. Different from the static databases, processing uncertain data streams has its own requirements such as the limited memory, small response time, and so on. To tackle the challenges with respect to efficiency and effectiveness, in this paper, we formalize the problem of join on uncertain data streams (USJ), which can guarantee the accuracy of USJ answers over uncertain data, and propose effective pruning methods to filter out false alarms. We integrate the pruning methods into an efficient query procedure for incrementally maintaining USJ answers. Extensive experiments have been conducted to demonstrate the efficiency and effectiveness of our approaches.
10648050
Searching musical audio datasets by a batch of multi-variant tracks
Multi-variant music tracks are those audio tracks of a particular song which are sung and recorded by different people (i.e., cover songs). As music social clubs grow on the Internet, more and more people like to upload music recordings onto such music social sites to share their own home-produced albums and participate in Internet singing contests. Therefore it is very important to explore a computer-assisted evaluation tool to detect these audio-based multi-variant tracks. In this paper we investigate such a task: the original track of a song is embedded in datasets, with a batch of multi-variant audio tracks of this song as input, our retrieval system returns an ordered list by similarity and indicates the position of relevant audio track. To help process multi-variant audio tracks, we suggest a semantic indexing framework and propose the Federated Features (FF) scheme to generate the semantic summarization of audio feature sequences. The conjunction of federated features with three typical similarity searching schemes, K-Nearest Neighbor (KNN), Locality Sensitive Hashing (LSH), and Exact Euclidian LSH (E2LSH), is evaluated. From these findings, a computer-assisted evaluation tool for searching multi-variant audio tracks was developed to search over large musical audio datasets.
14281356
Virtual Surrounding Face Geocasting with Guaranteed Message Delivery for Ad Hoc and Sensor Networks
Geocasting in wireless sensor networks and ad hoc networks is the delivery of a message from a source to all the nodes in a given geographical region. The objectives of a geocasting protocol are two-folds: guaranteed message delivery and low transmission cost. Most of the existing protocols do not guarantee message delivery, and those that do incur a high transmission costs. In this paper, we introduce the idea of a virtual surrounding face (VSF), and present a geocasting protocol based on VSF. By using mathematical analyses and simulation studies, we show that the proposed protocol guarantees message delivery and has a significant lower transmission cost than the existing approaches.
207171808
Evaluating probability threshold k-nearest-neighbor queries over uncertain data
In emerging applications such as location-based services, sensor monitoring and biological management systems, the values of the database items are naturally imprecise. For these uncertain databases, an important query is the Probabilistic k-Nearest-Neighbor Query (k-PNN), which computes the probabilities of sets of k objects for being the closest to a given query point. The evaluation of this query can be both computationally- and I/O-expensive, since there is an exponentially large number of k object-sets, and numerical integration is required. Often a user may not be concerned about the exact probability values. For example, he may only need answers that have sufficiently high confidence. We thus propose the Probabilistic Threshold k-Nearest-Neighbor Query (T-k-PNN), which returns sets of k objects that satisfy the query with probabilities higher than some threshold T. Three steps are proposed to handle this query efficiently. In the first stage, objects that cannot constitute an answer are filtered with the aid of a spatial index. The second step, called probabilistic candidate selection, significantly prunes a number of candidate sets to be examined. The remaining sets are sent for verification, which derives the lower and upper bounds of answer probabilities, so that a candidate set can be quickly decided on whether it should be included in the answer. We also examine spatially-efficient data structures that support these methods. Our solution can be applied to uncertain data with arbitrary probability density functions. We have also performed extensive experiments to examine the effectiveness of our methods.