1741788
A Compound TCP Approach for High-Speed and Long Distance Networks
Many applications require fast data transfer over high speed and long distance networks. However, standard TCP fails to fully utilize the network capacity due to the limitation in its conservative congestion control (CC) algorithm. Some works have been proposed to improve the connection 鈂s throughput by adopt- ing more aggressive loss-based CC algorithms. These algorithms, although can effectively improve the link utilization, have the weakness of poor RTT fairness. Further, they may severely de- crease the performance of regular TCP flows that traverse the same network path. On the other hand, pure delay-based ap- proaches that improve the throughput in high-speed networks may not work well when the traffic is mixed with both delay- based and greedy loss-based flows. In this paper, we propose a novel Compound TCP (CTCP) approach, which is a synergy of delay-based and loss-based approach. Specifically, we add a scal- able delay-based component into the standard TCP Reno conges- tion avoidance algorithm (a.k.a., the loss-based component). The sending rate of CTCP is controlled by both components. This new delay-based component can rapidly increase sending rate when network path is under utilized, but gracefully retreat in a busy network when bottleneck queue is built. Augmented with this delay-based component, CTCP provides very good bandwidth scalability with improved RTT fairness, and at the same time achieves good TCP-fairness, irrelevant to the windows size. We developed an analytical model of CTCP and implemented it on the Windows operating system. Our analysis and experiment results verify the properties of CTCP.
18410848
Network-adaptive scalable video streaming over 3G wireless network
Scalable video streaming over a wireless link with quality of service (QoS) is a very challenging task due to the time-varying characteristics of the wireless channel and limited battery resource in handheld devices. This paper proposes an end-to-end network-adaptive architecture for video streaming over 3G wireless networks. The proposed architecture not only dynamically estimates the varying network status on the fly, but also simultaneously performs application-level error control and transmission-level power control to protect against the random and fading error occurring across the 3G network. Distortion/power-minimized rate allocation is presented to achieve the minimum end-to-end distortion or minimum total power consumption based on the user's requirement. Simulation results demonstrate the effectiveness of our proposed scheme.
206750751
Error robust scalable audio streaming over wireless IP networks
Streaming high-fidelity audio over wireless Internet protocol (IP) networks is a challenging task because the networks present not only packet losses, but also residual bit errors. These losses and errors have severe adverse effect on the compressed audio bitstream. To solve this problem, this paper introduces error resilience in conjunction with error protection for scalable audio streaming over wireless networks. Specifically, error resilience is achieved by performing bitstream data partitioning and reversible variable length coding in the audio coder. Error protection is provided by layered product channel code to simultaneously handle packet losses and residual bit errors. Both the row and column codes of the product code provide unequal error protection for different layers of the audio bitstream by considering the characteristics of the scalable audio. Rate-distortion optimization is performed to determine the best source-channel coding tradeoff that minimizes the average expected end-to-end distortion. Simulation results demonstrate the effectiveness of our proposed approach.
20955787
Cost-based cache replacement and server selection for multimedia proxy across wireless Internet
Multimedia proxy plays an important role in multimedia streaming over wireless Internet. Since wireless network exhibits different characteristics from the Internet, multimedia proxy caching over wireless Internet faces additional challenges. In this paper, we present a study of cache replacement for a single server and server selection for multiple servers across wireless Internet. By considering multiple objectives of multimedia proxy, we design a unified cost metric to measure proxy performance in wireless Internet. Based on the defined unified cost metric, we propose a novel replacement algorithm for single-server and a new server-selection policy for multiple servers to improve the end-to-end performance such as throughput, media quality, and start-up latency. To effectively handle errors occurred on wireless link, channel-adaptive unequal error protection is deployed according to distinct quality of service requirements of layered or scalable media. Simulation results demonstrate that our approaches achieve significantly better performance than the known cache-replacement algorithms and sever selection schemes, respectively.
3992721
Error minimization in approximate range aggregates
Histogram techniques have been used in many commercial database management systems to estimate a query result size. Recently, it has been shown that they are very effective to support approximation of query processing especially aggregates. In this paper, we investigate the problem of minimizing average errors of approximate aggregates using histogram techniques. Firstly, we present a novel linear-spline histogram model that is more accurate than the existing models. Secondly, we propose a novel histogram construction technique for minimizing such average errors, which is shown to generate a near optimal histogram. Our experiment results demonstrate that the new histogram construction techniques lead to a great accuracy improvement on the existing techniques.
10598810
An orthogonal genetic algorithm for multimedia multicast routing
Many multimedia communication applications require a source to send multimedia information to multiple destinations through a communication network. To support these applications, it is necessary to determine a multicast tree of minimal cost to connect the source node to the destination nodes subject to delay constraints on multimedia communication. This problem is known as multimedia multicast routing and has been proved to be NP-complete. The paper proposes an orthogonal genetic algorithm for multimedia multicast routing. Its salient feature is to incorporate an experimental design method called orthogonal design into the crossover operation. As a result, it can search the solution space in a statistically sound manner and it is well suited for parallel implementation and execution. We execute the orthogonal genetic algorithm to solve two sets of benchmark test problems. The results indicate that for practical problem sizes, the orthogonal genetic algorithm can find near optimal solutions within moderate numbers of generations.
9724637
Selecting Stars: The k Most Representative Skyline Operator
Skyline computation has many applications including multi-criteria decision making. In this paper, we study the problem of selecting k skyline points so that the number of points, which are dominated by at least one of these k skyline points, is maximized. We first present an efficient dynamic programming based exact algorithm in a 2d-space. Then, we show that the problem is NP-hard when the dimensionality is 3 or more and it can be approximately solved by a polynomial time algorithm with the guaranteed approximation ratio 1-1/e. To speed-up the computation, an efficient, scalable, index-based randomized algorithm is developed by applying the FM probabilistic counting technique. A comprehensive performance evaluation demonstrates that our randomized technique is very efficient, highly accurate, and scalable.
16148368
A model-based evolutionary algorithm for bi-objective optimization
The Pareto optimal solutions to a multi-objective optimization problem often distribute very regularly in both the decision space and the objective space. Most existing evolutionary algorithms do not explicitly take advantage of such a regularity. This paper proposed a model-based evolutionary algorithm (M-MOEA) for bi-objective optimization problems. Inspired by the ideas from estimation of distribution algorithms, M-MOEA uses a probability model to capture the regularity of the distribution of the Pareto optimal solutions. The local principal component analysis (local PCA) and the least-squares method are employed for building the model. New solutions are sampled from the model thus built. At alternate generations, M-MOEA uses crossover and mutation to produce new solutions. The selection in M-MOEA is the same as in non-dominated sorting genetic algorithm-II (NSGA-II). Therefore, MOEA can be regarded as a combination of EDA and NSGA-II. The preliminary experimental results show that M-MOEA performs better than NSGA-II.
15055893
On the convergence of a class of estimation of distribution algorithms
We investigate the global convergence of estimation of distribution algorithms (EDAs). In EDAs, the distribution is estimated from a set of selected elements, i.e., the parent set, and then the estimated distribution model is used to generate new elements. In this paper, we prove that: 1) if the distribution of the new elements matches that of the parent set exactly, the algorithms will converge to the global optimum under three widely used selection schemes and 2) a factorized distribution algorithm converges globally under proportional selection.