14574047
LRED: a robust active queue management scheme based on packet loss ratio
Active queue management (AQM) is an effective method to enhance congestion control, and to achieve tradeoff between link utilization and delay. The de facto standard, random early detection (RED), and most of its variants use queue length as a congestion indicator to trigger packet dropping. The proportional-integral (PI), use both queue length and traffic input rate as congestion indicators; effective stability model and practical design rules built on the TCP control model and abstracted AQM model reveal that such schemes enhance the stability of a system. In this paper, we propose an AQM scheme with fast response time, yet good robustness. The scheme, called loss ratio based RED (LRED), measures the latest packet loss ratio, and uses it as a complement to queue length in order to dynamically adjust packet drop probability. Employing the closed-form relationship between packet loss ratio and the number of TCP flows, this scheme is responsive even if the number of TCP flows varies significantly. We also provide the design rules for this scheme based on the well-known TCP control model. This scheme's performance is examined under various network configurations, and compared to existing AQM schemes, including PI, random exponentially marking (REM), and adaptive virtual queue (AVQ). Our simulation results show that, with comparable complexity', this scheme has short response time, better robustness, and more desirable tradeoff than PI, REM, and AQV, especially under highly dynamic network and heavy traffic load.
62585345
An effective collision resolution mechanism for wireless LAN
Based on the current standardized IEEE 802.11 distributed coordination function (DCF) protocol, this paper proposes a new efficient collision resolution mechanism, called GDCF (gentle DCF). Our main motivation is based on the observation that 802.11 DCF decreases the contention window to the initial value after each success transmission, which essentially assumes that each successful transmission is an indication that the system is under low traffic loading. GDCF takes a more conservative measure by halving the contention window size after c consecutive successful transmissions. This "gentle" decrease lowers the collision probability, especially when the competing node number is large. We compute the optimal value for c, and the numerical results from both analysis and simulation demonstrate that GDCF significantly improve the performance of 802.11 DCF including throughput, fairness, and energy efficiency. In addition, GDCF is flexible for supporting priority access by selecting different values of c for different traffic types; it is fully compatible with the original 802.11 DCF, and simple to implement, as it does not requires changes in control message structure and access procedures in DCF.
17891437
Call admission control for voice/data integrated cellular networks: performance analysis and comparative study
In this paper, we propose a new call admission control scheme called dual threshold bandwidth reservation, or DTBR scheme. The main novelty is that it builds upon a complete sharing approach, in which the channels in each cell are shared among the different traffic types and multiple thresholds are used to meet the specific quality-of-service (QoS) requirements. We present a detailed comparative study based on mathematical and simulation models, and quantitatively demonstrate that the DTBR is capable of providing the QoS guarantee for each type of traffic, while at the same time leading to much better channel efficiency. We further show that the DTBR scheme with elastic data service can offer both service guarantee and service differentiation for voice and data services, and enhance the bandwidth utilization.
13758087
Support cluster machine
For large-scale classification problems, the training samples can be clustered beforehand as a downsampling pre-process, and then only the obtained clusters are used for training. Motivated by such assumption, we proposed a classification algorithm, Support Cluster Machine (SCM), within the learning framework introduced by Vapnik. For the SCM, a compatible kernel is adopted such that a similarity measure can be handled not only between clusters in the training phase but also between a cluster and a vector in the testing phase. We also proved that the SCM is a general extension of the SVM with the RBF kernel. The experimental results confirm that the SCM is very effective for largescale classification problems due to significantly reduced computational costs for both training and testing and comparable classification accuracies. As a by-product, it provides a promising approach to dealing with privacy-preserving data mining problems.
3489230
Efficient mart-aided modeling for microarchitecture design space exploration and performance prediction
Computer architects usually evaluate new designs by cycle-accurate processor simulation. This approach provides detailed insight into processor performance, power consumption and complexity. However, only configurations in a subspace can be simulated in practice due to long simulation time and limited resource, leading to suboptimal conclusions which might not be applied in a larger design space. In this paper, we propose an automated performance prediction approach which employs state-of-the-art techniques from experiment design, machine learning and data mining. Our method not only produces highly accurate estimations for unsampled points in the design space, but also provides interpretation tools that help investigators to understand performance bottlenecks. According to our experiments, by sampling only 0.02% of the full design space with about 15 millions points, the median percentage errors, based on 5000 independent test points, range from 0.32% to 3.12% in 12 benchmarks. Even for the worst-case performance, the percentage errors are within 7% for 10 out of 12 benchmarks. In addition, the proposed model can also help architects to find important design parameters and performance bottlenecks.
5438961
A dynamic multiple-threshold bandwidth reservation (DMTBR) scheme for QoS provisioning in multimedia wireless networks
Next-generation wireless networks target to provide quality of service (QoS) for multimedia applications. We study the wireless systems that support two QoS requirements: keeping the handoff dropping probability less than a predefined QoS threshold while maintaining relative priorities for different traffic classes based on blocking probability. To achieve this goal, a dynamic multiple-threshold bandwidth reservation (DMTBR) scheme, which is capable of granting differential priorities to different traffic classes and to new and handoff traffic for each class by dynamically adjusting bandwidth reservation thresholds, is proposed. In this scheme, the thresholds are obtained in two steps. The initial values are estimated based on instantaneous network traffic situation, then the thresholds will be further adapted according to the instantaneous network QoS status. In times of network congestion, a preventive measure is taken to throttle the new connections. Another contribution of this paper is to generalize the concept of relative priority and hence give the network operator more flexibility to adjust admission control policy by taking into account some dynamic factors such as offered load. The extensive simulations are conducted for two purposes. First, we verify the performance of the proposed scheme and show our scheme performs well under various traffic loads. Second, we demonstrate that the DMTBR scheme gains more advantages when taking the offered load into consideration.
38238585
Exploiting multi-scale support vector regression for image compression
Unlike traditional neural networks that require predefined topology of the network, support vector regression (SVR) approach can model the data within the given level of accuracy with only a small subset of the training data, which are called support vectors (SVs). This property of sparsity has been exploited as the basis for image compression. In this paper, for still image compression, we propose a multi-scale support vector regression (MS-SVR) approach, which can model the images with steep variations and smooth variations very well resulting in good performance. We test our proposed MS-SVR based algorithm on some standard images. The experimental results verify that the proposed MS-SVR achieves better performance than standard SVR. And in a wide range of compression ratio, MS-SVR is very close to JPEG in terms of peak signal-to-noise ratio (PSNR) but exhibits better subjective quality. Furthermore, MS-SVR even outperforms JPEG on both PSNR and subjective quality when the compression ratio is higher enough, for example 25:1 for Lena image. Even when compared with JPEG-2000, the results show greatly similar trend as those in JPEG experiments, except that the compression ratio is a bit higher where our proposed MS-SVR will outperform JPEG-2000.
15225105
Development of an OpenGL based multi-robot simulating platform
Simulating platform plays a crucial role in multi-robot research, as a tool to quickly and efficiently test new concepts, strategies, and algorithms. In this paper, OpenGL and Visual C++ based modeling method has been discussed in detail to establish a platform for the 3D simulating of multi-link mobile robots and their collaboration. The cooperation strategies, the control methods and the communication mechanisms of multi-robot system can be explored and verified by this platform. A planetary robot system is used to verify the validity of the developed platform. The effectiveness and the practicability of the platform are successfully demonstrated by a four-robot motion coordination simulating results.