8733679
Stochastic Fluid Theory for P2P Streaming Systems
We develop a simple stochastic fluid model that seeks to expose the fundamental characteristics and limitations of P2P streaming systems. This model accounts for many of the essential features of a P2P streaming system, including the peers' realtime demand for content, peer churn (peers joining and leaving), peers with heterogeneous upload capacity, limited infrastructure capacity, and peer buffering and playback delay. The model is tractable, providing closed-form expressions which can be used to shed insight on the fundamental behavior of P2P streaming systems. The model shows that performance is largely determined by a critical value. When the system is of moderate-to-large size, if a certain ratio of traffic loads exceeds the critical value, the system performs well; otherwise, the system performs poorly. Furthermore, large systems have better performance than small systems since they are more resilient to bandwidth fluctuations caused by peer churn. Finally, buffering can dramatically improve performance in the critical region, for both small and large systems. In particular, buffering can bring more improvement than can additional infrastructure bandwidth.
903407
On integrating fluid models with packet simulation
Fluid models have been shown to he efficient and accurate in modelling large IP networks. However, unlike packet models, it is difficult to extract packet-level information from them. In this paper, we present a hybrid simulation method that maintains the performance advantage of fluid models while providing detailed packet level information for selected packet traffic flows. We propose two models to account for the interaction between background TCP traffic in a fluid network and foreground packet traffic of interest. The first assumes that the packet traffic poses a negligible load on the fluid network whereas the second accounts for the added load by transforming the packet traffic into fluid flows and solving the resulting enhanced fluid model. The first of these yields an efficient one pass solution algorithm whereas the second requires an additional pass to account for the packet traffic load. We establish the correctness of both approaches and present their implementation within ns-2. Comparisons between the hybrid models and a classical packet simulation show the two pass approach to be quite accurate and computationally efficient.
12544972
Fluid models and solutions for large-scale IP networks
In this paper we present a scalable model of a network of Active Queue Management (AQM) routers serving a large population of TCP flows. We present efficient solution techniques that allow one to obtain the transient behavior of the average queue lengths, packet loss probabilities, and average end-to-end latencies. We model different versions of TCP as well as different versions of RED, the most popular AQM scheme currently in use. Comparisons between our models and <tt>ns</tt> simulation show our models to be quite accurate while at the same time requiring substantially less time to solve, especially when workloads and bandwidths are high.
15563828
Self-similarity and long range dependence on the internet: a second look at the evidence, origins and implications
In this paper we critically reexamine some of the long standing beliefs regarding self-similarity and long range dependence (LRD) on the Internet. Power law tails have been conjectured to be a cause of LRD. In this paper, we reexamine the claims regarding heavy tails. We first examine the generative models for the heavy tail phenomena, both in terms of the fragility of some proposed mechanisms to modeling perturbations as well as the weak statistical evidence for the mechanisms. Next, we take a look at some of the implications of LRD in key performance aspects of Internet algorithms. Finally, we present an alternative model explaining the LRD phenomena of Internet traffic. We argue that the multiple time-scale nature of the generation of traffic and transport protocols make the observation of LRD inevitable.
1409797
On the TCP-Friendliness of VoIP Traffic
Recently, some concerns have been raised regarding whether the rapid growth of the UDP traffic due to the Skype-like applications will throttle down regular TCP users because UDP flows are unresponsive to network congestion, i.e., UDP senders do not reduce their sending rates when congestion occurs in the network. However, most UDP based real time applications, such as Voiceover-IP (VoIP), drop out completely if the user perceived quality becomes unacceptable as the network congestion increases. This user back-off mechanism effectively reduces UDP traffic rate at the aggregated level when the network is congested. In this paper, we investigate the fairness issue between VoIP flows and TCP flows under different network environment. Our study explicitly takes into account both transport layer congestion control mechanism built-into TCP protocol stack and the self-adaptiveness of VoIP users. Various models are developed to characterize VoIP user back-off behaviors in response to call quality degradation resulted from packet loss and delay inside the network. The comparison between VoIP user back-off and TCP rate adaption shows that not only VoIP flows consume much less bandwidth than TCP flows, but also VoIP traffic is indeed very responsive to congestion when the network is overloaded.
13098192
On the impact of concurrent downloads
Concurrent downloads accelerate information access speed for individual Web users. The speed-up comes from multiple connections launched for one subject download, which leads to unfairness at user level. We study the impact of concurrent downloads on the network. Particularly, we investigate the fairness between users who employ different downloading concurrency. We also discuss how concurrent downloads affect the transient behavior of the network.
8905363
Improving Query Response Delivery Quality in Peer-to-Peer Systems
Unstructured peer-to-peer (P2P) system is the prevalent model in today's P2P systems. In such systems, a response is sent along the same path that carried the incoming query message. To guarantee the anonymity of the requestor, no requestor information is included in the response message, and each node in the query's incoming path only knows its direct neighbors who sent the query request to it. This mechanism introduces response loss when any one node or connection in the path fails, which is a common occurrence in the P2P system due to its dynamic feature. In this paper, we address the response loss problem and show that peers' oscillation can cause up to a 35 percent response loss in an unstructured P2P system. We also present three techniques to alleviate this problem: the redundant response delivery (RRD) scheme as a proactive approach, the adaptive response delivery (ARD) scheme as a reactive approach, and the extended adaptive response delivery scheme to render ARD to function in an unstructured P2P system with limited or no flooding-based search mechanism. We have evaluated our techniques in a large-scale network simulation. With limited traffic overhead, all three techniques reduce response loss rate by more than 65 percent and are fully distributed. We have designed our techniques to be simple to develop and implement in existing P2P systems
18963770
A quasi-Monte Carlo method for computing areas of point-sampled surfaces
A novel and efficient quasi-Monte Carlo method for computing the area of a point-sampled surface with associated surface normal for each point is presented. Our method operates directly on the point cloud without any surface reconstruction procedure. Using the Cauchy-Crofton formula, the area of the point-sampled surface is calculated by counting the number of intersection points between the point cloud and a set of uniformly distributed lines generated with low-discrepancy sequences. Based on a clustering technique, we also propose an effective algorithm for computing the intersection points of a line with the point-sampled surface. By testing on a number of point-based models, experiments suggest that our method is more robust and more efficient than those conventional approaches based on surface reconstruction.
14257785
Comparison of probabilistic combination methods for protein secondary structure prediction
MOTIVATION
Protein secondary structure prediction is an important step towards understanding how proteins fold in three dimensions. Recent analysis by information theory indicates that the correlation between neighboring secondary structures are much stronger than that of neighboring amino acids. In this article, we focus on the combination problem for sequences, i.e. combining the scores or assignments from single or multiple prediction systems under the constraint of a whole sequence, as a target for improvement in protein secondary structure prediction.


RESULTS
We apply several graphical chain models to solve the combination problem and show that they are consistently more effective than the traditional window-based methods. In particular, conditional random fields (CRFs) moderately improve the predictions for helices and, more importantly, for beta sheets, which are the major bottleneck for protein secondary structure prediction.
1265218
Solving Regular Tree Grammar Based Constraints
This paper describes the precise specification, design, analysis, implementation, and measurements of an efficient algorithm for solving regular tree grammar based constraints. The particular constraints are for dead-code elimination on recursive data, but the method used for the algorithm design and complexity analysis is general and applies to other program analysis problems as well. The method is centered around Paige's finite differencing, i.e., computing expensive set expressions incrementally, and allows the algorithm to be derived and analyzed formally and implemented easily. We propose higher-level transformations that make the derived algorithm concise and allow its complexity to be analyzed accurately. Although a rough analysis shows that the worst-case time complexity is cubic in program size, an accurate analysis shows that it is linear in the number of live program points and in other parameters, including mainly the arity of data constructors and the number of selector applications into whose arguments the value constructed at a program point might flow. These parameters explain the performance of the analysis in practice. Our implementation also runs two to ten times as fast as a previous implementation of an informally designed algorithm.
15475945
Performance prediction of component-based applications
One of the major problems in building large-scale enterprise systems is anticipating the performance of the eventual solution before it has been built. The fundamental software engineering problem becomes more difficult when the systems are built on component technology. This paper investigates the feasibility of providing a practical solution to this problem. An empirical approach is proposed to determine the performance characteristics of component-based applications by benchmarking and profiling. Based on observation, a model is constructed to act as a performance predictor for a class of applications based on the specific component technology. The performance model derived from empirical measures is necessary to make the problem tractable and the results relevant. A case study applies the performance model to an application prototype implemented by two component infrastructures: CORBA and J2EE.