6965590
An information-theoretic approach to network monitoring and measurement
Network engineers and operators are faced with a number of challenges that arise in the context of network monitoring and measurement. These include: i) how much information is included in measurement traces and by how much can we compress those traces?, ii) how much information is captured by different monitoring paradigms and tools ranging from full packet header captures to flow-level captures (such as with NetFlow) to packet and byte counts (such as with SNMP)? and iii) how much joint information is included in traces collected at different points and can we take advantage of this joint information? In this paper we develop a network model and an information theoretic framework within which to address these questions. We use the model and the framework to first determine the benefits of compressing traces captured at a single monitoring point, and we outline approaches to achieve those benefits. We next consider the benefits of joint coding, or equivalently of joint compression of traces captured a different monitoring points. Finally, we examine the difference in information content when measurements are made at either the flow level or the packet/byte count level. In all of these cases, the effect of temporal and spatial correlation on the answers to the above questions is examined. Both our model and its predictions are validated against measurements taken from a large operational network.
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.
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.
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.
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.
40408404
Learning and Evolution by Minimization of Mutual Information
Based on negative correlation learning [1] and evolutionary learning, evolutionary ensembles with negative correlation learning (EENCL) was proposed for learning and designing of neural network ensembles [2]. The idea of EENCL is to regard the population of neural networks as an ensemble, and the evolutionary process as the design of neural network ensembles. EENCL used a fitness sharing based on the covering set. Such fitness sharing did not make accurate measurement on the similarity in the population. In this paper, a fitness sharing scheme based on mutual information is introduced in EENCL to evolve a diverse and cooperative population. The effectiveness of such evolutionary learning approach was tested on two real-world problems. This paper has also analyzed negative correlation learning in terms of mutual information on a regression task in the different noise conditions.
7031742
The effect of pruning and compression on graphical representations of the output of a speech recognizer
Large vocabulary continuous speech recognition can benefit from an efficient data structure for representing a large number of acoustic hypotheses compactly. Word graphs or lattices have been chosen as such an efficient interface between acoustic recognition engines and subsequent language processing modules. This paper first investigates the effect of pruning during acoustic decoding on the quality of word lattices and shows that by combining different pruning options (at the model level and word level), we can obtain word lattices with comparable accuracy to the original lattices and a manageable size. In order to use the word lattices as the input for a post-processing language module, they should preserve the target hypotheses and their scores while being as small as possible. In this paper, we introduce a word graph compression algorithm that significantly reduces the number of words in the graphical representation without eliminating utterance hypotheses or distorting their acoustic scores. We compare this word graph compression algorithm with several other lattice size-reducing approaches and demonstrate the relative strength of the new word graph compression algorithm for decreasing the number of words in the representation. Experiments are conducted across corpora and vocabulary sizes to determine the consistency of the pruning and compression results.
5064803
Body Localization in Still Images Using Hierarchical Models and Hybrid Search
We present a 3-level hierarchical model for localizing human bodies in still images from arbitrary viewpoints. We first fit a simple tree-structured model defined on a small landmark set along the body contours by Dynamic Programming (DP). The output is a series of proposal maps that encode the probabilities of partial body configurations. Next, we fit a mixture of view-dependent models by Sequential Monte Carlo (SMC), which handles self-occlusion, anthropometric constraints, and large viewpoint changes. DP and SMC are designed to search in opposite directions such that the DP proposals are utilized effectively to initialize and guide the SMC inference. This hybrid strategy of combining deterministic and stochastic search ensures both the robustness and efficiency of DP, and the accuracy of SMC. Finally, we fit an expanded mixture model with increased landmark density through local optimization. The model hierarchy is trained on a large number of gait images. Extensive tests on cluttered images with varying poses including walking, dancing and various types of sports activities demonstrate the feasibility of the proposed approach.
6591453
Opportunistic fair scheduling over multiple wireless channels
Emerging spread spectrum high-speed data networks utilize multiple channels via orthogonal codes or frequency-hopping patterns such that multiple users can transmit concurrently. In this paper, we develop a framework for opportunistic scheduling over multiple wireless channels. With a realistic channel model, any subset of users can be selected for data transmission at any time, albeit with different throughputs and system resource requirements. We first transform selection of the best users and rates from a complex general optimization problem into a decoupled and tractable formulation: a multiuser scheduling problem that maximizes total system throughput and a control-update problem that ensures long-term deterministic or probabilistic fairness constraints. We then design and evaluate practical schedulers that approximate these objectives.
887136
Value Functions for RL-Based Behavior Transfer: A Comparative Study
Temporal difference (TD) learning methods (Sutton & Barto 1998) have become popular reinforcement learning techniques in recent years. TD methods, relying on function approximators to generalize learning to novel situations, have had some experimental successes and have been shown to exhibit some desirable properties in theory, but have often been found slow in practice. This paper presents methods for further generalizing across tasks, thereby speeding up learning, via a novel form of behavior transfer. We compare learning on a complex task with three function approximators, a CMAC, a neural network, and an RBF, and demonstrate that behavior transfer works well with all three. Using behavior transfer, agents are able to learn one task and then markedly reduce the time it takes to learn a more complex task. Our algorithms are fully implemented and tested in the RoboCup-soccer keepaway domain.