On the Efficiency and Fairness of Transmission Control Loops: A Case for Exogenous Losses

We postulate that exogenous losses-which are typically regarded as introducing undesirable "noise" that needs to be filtered out or hidden from end points-can be surprisingly beneficial. In this paper we evaluate the effects of exogenous losses on transmission control loops, focusing primarily on efficiency and convergence to fairness properties. By analytically capturing the effects of exogenous losses, we are able to characterize the transient behavior of TCP. Our numerical results suggest that "noise" resulting from exogenous losses should not be filtered out blindly, and that a careful examination of the parameter space leads to better strategies regarding the treatment of exogenous losses inside the network. Specifically, we show that while low levels of exogenous losses do help connections converge to their fair share, higher levels of losses lead to inefficient network utilization. We draw the line between these two cases by determining whether or not it is advantageous to hide, or more interestingly introduce, exogenous losses. Our proposed approach is based on classifying the effects of exogenous losses into long-term and short-term effects. Such classification informs the extent to which we control exogenous losses, so as to operate in an efficient and fair region. We validate our results through simulations.

Rationing Antiretroviral Therapy for HIV/AIDS in Africa: Efficiency, Equity, and Reality

Background: Rationing of access to antiretroviral therapy already exists in sub-Saharan Africa and will intensify as national treatment programs develop. The number of people who are medically eligible for therapy will far exceed the human, infrastructural, and financial resources available, making rationing of public treatment services inevitable. Methods: We identified 15 criteria by which antiretroviral therapy could be rationed in African countries and analyzed the resulting rationing systems across 5 domains: clinical effectiveness, implementation feasibility, cost, economic efficiency, and social equity. Findings: Rationing can be explicit or implicit. Access to treatment can be explicitly targeted to priority subpopulations such as mothers of newborns, skilled workers, students, or poor people. Explicit conditions can also be set that cause differential access, such as residence in a designated geographic area, co-payment, access to testing, or a demonstrated commitment to adhere to therapy. Implicit rationing on the basis of first-come, first-served or queuing will arise when no explicit system is enforced; implicit systems almost always allow a high degree of queue-jumping by the elite. There is a direct tradeoff between economic efficiency and social equity. Interpretation: Rationing is inevitable in most countries for some period of time. Without deliberate social policy decisions, implicit rationing systems that are neither efficient nor equitable will prevail. Governments that make deliberate choices, and then explain and defend those choices to their constituencies, are more likely to achieve a socially desirable outcome from the large investments now being made than are those that allow queuing and queue-jumping to dominate.

BoostMap: A Method for Efficient Approximate Similarity Rankings

This paper introduces BoostMap, a method that can significantly reduce retrieval time in image and video database systems that employ computationally expensive distance measures, metric or non-metric. Database and query objects are embedded into a Euclidean space, in which similarities can be rapidly measured using a weighted Manhattan distance. Embedding construction is formulated as a machine learning task, where AdaBoost is used to combine many simple, 1D embeddings into a multidimensional embedding that preserves a significant amount of the proximity structure in the original space. Performance is evaluated in a hand pose estimation system, and a dynamic gesture recognition system, where the proposed method is used to retrieve approximate nearest neighbors under expensive image and video similarity measures. In both systems, BoostMap significantly increases efficiency, with minimal losses in accuracy. Moreover, the experiments indicate that BoostMap compares favorably with existing embedding methods that have been employed in computer vision and database applications, i.e., FastMap and Bourgain embeddings.

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.

A Bayesian Approach for TCP to Distinguish Congestion from Wireless Losses

(This Technical Report revises TR-BUCS-2003-011) The Transmission Control Protocol (TCP) has been the protocol of choice for many Internet applications requiring reliable connections. The design of TCP has been challenged by the extension of connections over wireless links. In this paper, we investigate a Bayesian approach to infer at the source host the reason of a packet loss, whether congestion or wireless transmission error. Our approach is "mostly" end-to-end since it requires only one long-term average quantity (namely, long-term average packet loss probability over the wireless segment) that may be best obtained with help from the network (e.g. wireless access agent).Specifically, we use Maximum Likelihood Ratio tests to evaluate TCP as a classifier of the type of packet loss. We study the effectiveness of short-term classification of packet errors (congestion vs. wireless), given stationary prior error probabilities and distributions of packet delays conditioned on the type of packet loss (measured over a larger time scale). Using our Bayesian-based approach and extensive simulations, we demonstrate that congestion-induced losses and losses due to wireless transmission errors produce sufficiently different statistics upon which an efficient online error classifier can be built. We introduce a simple queueing model to underline the conditional delay distributions arising from different kinds of packet losses over a heterogeneous wired/wireless path. We show how Hidden Markov Models (HMMs) can be used by a TCP connection to infer efficiently conditional delay distributions. We demonstrate how estimation accuracy is influenced by different proportions of congestion versus wireless losses and penalties on incorrect classification.

Robust Identification of Shared Losses Using End-to-End Unicast Probes

ERRATA: We present corrections to Fact 3 and (as a consequence) to Lemma 1 of BUCS Technical Report BUCS-TR-2000-013 (also published in IEEE INCP'2000)[1]. These corrections result in slight changes to the formulae used for the identifications of shared losses, which we quantify.

A Spectrum of TCP-Friendly Window-Based Congestion Control Algorithms

The increased diversity of Internet application requirements has spurred recent interests in transport protocols with flexible transmission controls. In window-based congestion control schemes, increase rules determine how to probe available bandwidth, whereas decrease rules determine how to back off when losses due to congestion are detected. The parameterization of these control rules is done so as to ensure that the resulting protocol is TCP-friendly in terms of the relationship between throughput and loss rate. In this paper, we define a new spectrum of window-based congestion control algorithms that are TCP-friendly as well as TCP-compatible under RED. Contrary to previous memory-less controls, our algorithms utilize history information in their control rules. Our proposed algorithms have two salient features: (1) They enable a wider region of TCP-friendliness, and thus more flexibility in trading off among smoothness, aggressiveness, and responsiveness; and (2) they ensure a faster convergence to fairness under a wide range of system conditions. We demonstrate analytically and through extensive ns simulations the steady-state and transient behaviors of several instances of this new spectrum of algorithms. In particular, SIMD is one instance in which the congestion window is increased super-linearly with time since the detection of the last loss. Compared to recently proposed TCP-friendly AIMD and binomial algorithms, we demonstrate the superiority of SIMD in: (1) adapting to sudden increases in available bandwidth, while maintaining competitive smoothness and responsiveness; and (2) rapidly converging to fairness and efficiency.

A Two-Tiered On-Line Server-Side Bandwidth Reservation Framework for the Real-Time Delivery of Multiple Video Streams

The advent of virtualization and cloud computing technologies necessitates the development of effective mechanisms for the estimation and reservation of resources needed by content providers to deliver large numbers of video-on-demand (VOD) streams through the cloud. Unfortunately, capacity planning for the QoS-constrained delivery of a large number of VOD streams is inherently difficult as VBR encoding schemes exhibit significant bandwidth variability. In this paper, we present a novel resource management scheme to make such allocation decisions using a mixture of per-stream reservations and an aggregate reservation, shared across all streams to accommodate peak demands. The shared reservation provides capacity slack that enables statistical multiplexing of peak rates, while assuring analytically bounded frame-drop probabilities, which can be adjusted by trading off buffer space (and consequently delay) and bandwidth. Our two-tiered bandwidth allocation scheme enables the delivery of any set of streams with less bandwidth (or equivalently with higher link utilization) than state-of-the-art deterministic smoothing approaches. The algorithm underlying our proposed frame-work uses three per-stream parameters and is linear in the number of servers, making it particularly well suited for use in an on-line setting. We present results from extensive trace-driven simulations, which confirm the efficiency of our scheme especially for small buffer sizes and delay bounds, and which underscore the significant realizable bandwidth savings, typically yielding losses that are an order of magnitude or more below our analytically derived bounds.

Robust Identification of Shared Losses Using End-to-End Unicast Probes

Current Internet transport protocols make end-to-end measurements and maintain per-connection state to regulate the use of shared network resources. When two or more such connections share a common endpoint, there is an opportunity to correlate the end-to-end measurements made by these protocols to better diagnose and control the use of shared resources. We develop packet probing techniques to determine whether a pair of connections experience shared congestion. Correct, efficient diagnoses could enable new techniques for aggregate congestion control, QoS admission control, connection scheduling and mirror site selection. Our extensive simulation results demonstrate that the conditional (Bayesian) probing approach we employ provides superior accuracy, converges faster, and tolerates a wider range of network conditions than recently proposed memoryless (Markovian) probing approaches for addressing this opportunity.

Efficient Nearest Neighbor Classification Using a Cascade of Approximate Similarity Measures

Nearest neighbor classification using shape context can yield highly accurate results in a number of recognition problems. Unfortunately, the approach can be too slow for practical applications, and thus approximation strategies are needed to make shape context practical. This paper proposes a method for efficient and accurate nearest neighbor classification in non-Euclidean spaces, such as the space induced by the shape context measure. First, a method is introduced for constructing a Euclidean embedding that is optimized for nearest neighbor classification accuracy. Using that embedding, multiple approximations of the underlying non-Euclidean similarity measure are obtained, at different levels of accuracy and efficiency. The approximations are automatically combined to form a cascade classifier, which applies the slower approximations only to the hardest cases. Unlike typical cascade-of-classifiers approaches, that are applied to binary classification problems, our method constructs a cascade for a multiclass problem. Experiments with a standard shape data set indicate that a two-to-three order of magnitude speed up is gained over the standard shape context classifier, with minimal losses in classification accuracy.