Exogenous-Loss Awareness in Queue Management: Toward Global Fairness

For a given TCP flow, exogenous losses are those occurring on links other than the flow's bottleneck link. Exogenous losses are typically viewed as introducing undesirable "noise" into TCP's feedback control loop, leading to inefficient network utilization and potentially severe global unfairness. This has prompted much research on mechanisms for hiding such losses from end-points. In this paper, we show through analysis and simulations that low levels of exogenous losses are surprisingly beneficial in that they improve stability and convergence, without sacrificing efficiency. Based on this, we argue that exogenous loss awareness should be taken into account in any AQM design that aims to achieve global fairness. To that end, we propose an exogenous-loss aware Queue Management (XQM) that actively accounts for and leverages exogenous losses. We use an equation based approach to derive the quiescent loss rate for a connection based on the connection's profile and its global fair share. In contrast to other queue management techniques, XQM ensures that a connection sees its quiescent loss rate, not only by complementing already existing exogenous losses, but also by actively hiding exogenous losses, if necessary, to achieve global fairness. We establish the advantages of exogenous-loss awareness using extensive simulations in which, we contrast the performance of XQM to that of a host of traditional exogenous-loss unaware AQM techniques.

dPAM: A Distributed Prefetching Protocol for Scalable Asynchronous Multicast in P2P Systems

We leverage the buffering capabilities of end-systems to achieve scalable, asynchronous delivery of streams in a peer-to-peer environment. Unlike existing cache-and-relay schemes, we propose a distributed prefetching protocol where peers prefetch and store portions of the streaming media ahead of their playout time, thus not only turning themselves to possible sources for other peers but their prefetched data can allow them to overcome the departure of their source-peer. This stands in sharp contrast to existing cache-and-relay schemes where the departure of the source-peer forces its peer children to go the original server, thus disrupting their service and increasing server and network load. Through mathematical analysis and simulations, we show the effectiveness of maintaining such asynchronous multicasts from several source-peers to other children peers, and the efficacy of prefetching in the face of peer departures. We confirm the scalability of our dPAM protocol as it is shown to significantly reduce server load.

Blocking Java Applets at the Firewall

This paper explores the problem of protecting a site on the Internet against hostile external Java applets while allowing trusted internal applets to run. With careful implementation, a site can be made resistant to current Java security weaknesses as well as those yet to be discovered. In addition, we describe a new attack on certain sophisticated firewalls that is most effectively realized as a Java applet.

How does TCP generate Pseudo-self-similarity?

Long-range dependence has been observed in many recent Internet traffic measurements. In addition, some recent studies have shown that under certain network conditions, TCP itself can produce traffic that exhibits dependence over limited timescales, even in the absence of higher-level variability. In this paper, we use a simple Markovian model to argue that when the loss rate is relatively high, TCP's adaptive congestion control mechanism indeed generates traffic with OFF periods exhibiting power-law shape over several timescales and thus introduces pseudo-long-range dependence into the overall traffic. Moreover, we observe that more variable initial retransmission timeout values for different packets introduces more variable packet inter-arrival times, which increases the burstiness of the overall traffic. We can thus explain why a single TCP connection can produce a time-series that can be misidentified as self-similar using standard tests.

Scalable Peer-to-Peer Indexing with Constant State

We present a distributed indexing scheme for peer to peer networks. Past work on distributed indexing traded off fast search times with non-constant degree topologies or network-unfriendly behavior such as flooding. In contrast, the scheme we present optimizes all three of these performance measures. That is, we provide logarithmic round searches while maintaining connections to a fixed number of peers and avoiding network flooding. In comparison to the well known scheme Chord, we provide competitive constant factors. Finally, we observe that arbitrary linear speedups are possible and discuss both a general brute force approach and specific economical optimizations.

Angels: In-Network Support for Minimum Distribution Time in P2P Overlays

This thesis proposes the use of in-network caches (which we call Angels) to reduce the Minimum Distribution Time (MDT) of a file from a seeder – a node that possesses the file – to a set of leechers – nodes who are interested in downloading the file. An Angel is not a leecher in the sense that it is not interested in receiving the entire file, but rather it is interested in minimizing the MDT to all leechers, and as such uses its storage and up/down-link capacity to cache and forward parts of the file to other peers. We extend the analytical results by Kumar and Ross (Kumar and Ross, 2006) to account for the presence of angels by deriving a new lower bound for the MDT. We show that this newly derived lower bound is tight by proposing a distribution strategy under assumptions of a fluid model. We present a GroupTree heuristic that addresses the impracticalities of the fluid model. We evaluate our designs through simulations that show that our GroupTree heuristic outperforms other heuristics, that it scales well with the increase of the number of leechers, and that it closely approaches the optimal theoretical bounds.

Fine-Grained Layered Multicast

Traditional approaches to receiver-driven layered multicast have advocated the benefits of cumulative layering, which can enable coarse-grained congestion control that complies with TCP-friendliness equations over large time scales. In this paper, we quantify the costs and benefits of using non-cumulative layering and present a new, scalable multicast congestion control scheme which provides a fine-grained approximation to the behavior of TCP additive increase/multiplicative decrease (AIMD). In contrast to the conventional wisdom, we demonstrate that fine-grained rate adjustment can be achieved with only modest increases in the number of layers and aggregate bandwidth consumption, while using only a small constant number of control messages to perform either additive increase or multiplicative decrease.

Mistreatment in Distributed Caching Groups: Causes and Implications

Although cooperation generally increases the amount of resources available to a community of nodes, thus improving individual and collective performance, it also allows for the appearance of potential mistreatment problems through the exposition of one node’s resources to others. We study such concerns by considering a group of independent, rational, self-aware nodes that cooperate using on-line caching algorithms, where the exposed resource is the storage of each node. Motivated by content networking applications – including web caching, CDNs, and P2P – this paper extends our previous work on the off-line version of the problem, which was limited to object replication and was conducted under a game-theoretic framework. We identify and investigate two causes of mistreatment: (1) cache state interactions (due to the cooperative servicing of requests) and (2) the adoption of a common scheme for cache replacement/redirection/admission policies. Using analytic models, numerical solutions of these models, as well as simulation experiments, we show that online cooperation schemes using caching are fairly robust to mistreatment caused by state interactions. When this becomes possible, the interaction through the exchange of miss-streams has to be very intense, making it feasible for the mistreated nodes to detect and react to the exploitation. This robustness ceases to exist when nodes fetch and store objects in response to remote requests, i.e., when they operate as Level-2 caches (or proxies) for other nodes. Regarding mistreatment due to a common scheme, we show that this can easily take place when the “outlier” characteristics of some of the nodes get overlooked. This finding underscores the importance of allowing cooperative caching nodes the flexibility of choosing from a diverse set of schemes to fit the peculiarities of individual nodes. To that end, we outline an emulation-based framework for the development of mistreatment-resilient distributed selfish caching schemes.

Object Detection at the Optimal Scale with Hidden State Shape Models

Hidden State Shape Models (HSSMs) [2], a variant of Hidden Markov Models (HMMs) [9], were proposed to detect shape classes of variable structure in cluttered images. In this paper, we formulate a probabilistic framework for HSSMs which provides two major improvements in comparison to the previous method [2]. First, while the method in [2] required the scale of the object to be passed as an input, the method proposed here estimates the scale of the object automatically. This is achieved by introducing a new term for the observation probability that is based on a object-clutter feature model. Second, a segmental HMM [6, 8] is applied to model the "duration probability" of each HMM state, which is learned from the shape statistics in a training set and helps obtain meaningful registration results. Using a segmental HMM provides a principled way to model dependencies between the scales of different parts of the object. In object localization experiments on a dataset of real hand images, the proposed method significantly outperforms the method of [2], reducing the incorrect localization rate from 40% to 15%. The improvement in accuracy becomes more significant if we consider that the method proposed here is scale-independent, whereas the method of [2] takes as input the scale of the object we want to localize.

Neural Dynamics Underlying Impaired Autonomic and Conditioned Responses Following Amygdala and Orbitofrontal Lesions

A neural model is presented that explains how outcome-specific learning modulates affect, decision-making and Pavlovian conditioned approach responses. The model addresses how brain regions responsible for affective learning and habit learning interact, and answers a central question: What are the relative contributions of the amygdala and orbitofrontal cortex to emotion and behavior? In the model, the amygdala calculates outcome value while the orbitofrontal cortex influences attention and conditioned responding by assigning value information to stimuli. Model simulations replicate autonomic, electrophysiological, and behavioral data associated with three tasks commonly used to assay these phenomena: Food consumption, Pavlovian conditioning, and visual discrimination. Interactions of the basal ganglia and amygdala with sensory and orbitofrontal cortices enable the model to replicate the complex pattern of spared and impaired behavioral and emotional capacities seen following lesions of the amygdala and orbitofrontal cortex.

Neural Pattern Recognition on Multichannel Input Representation

This article presents a new neural pattern recognition architecture on multichannel data representation. The architecture emploies generalized ART modules as building blocks to construct a supervised learning system generating recognition codes on channels dynamically selected in context using serial and parallel match trackings led by inter-ART vigilance signals.

Cortical Dynamics of Feature Binding and Reset: Control of Visual Persistence

An analysis of the reset of visual cortical circuits responsible for the binding or segmentation of visual features into coherent visual forms yields a model that explains properties of visual persistence. The reset mechanisms prevent massive smearing or visual percepts in response to rapidly moving images. The model simulates relationships among psychophysical data showing inverse relations of persistence to flash luminance and duration, greaterr persistence of illusory contours than real contours, a U-shaped temporal function for persistence of illusory contours, a reduction of persistence: due to adaptation with a stimulus of like orientation, an increase or persistence due to adaptation with a stimulus of perpendicular orientation, and an increase of persistence with spatial separation of a masking stimulus. The model suggests that a combination of habituative, opponent, and endstopping mechanisms prevent smearing and limit persistence. Earlier work with the model has analyzed data about boundary formation, texture segregation, shape-from-shading, and figure-ground separation. Thus, several types of data support each model mechanism and new predictions are made.