24 resultados para 13627-011

em Boston University Digital Common


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We consider the problems of typability[1] and type checking[2] in the Girard/Reynolds second-order polymorphic typed λ-calculus, for which we use the short name "System F" and which we use in the "Curry style" where types are assigned to pure λ -terms. These problems have been considered and proven to be decidable or undecidable for various restrictions and extensions of System F and other related systems, and lower-bound complexity results for System F have been achieved, but they have remained "embarrassing open problems"[3] for System F itself. We first prove that type checking in System F is undecidable by a reduction from semi-unification. We then prove typability in System F is undecidable by a reduction from type checking. Since the reverse reduction is already known, this implies the two problems are equivalent. The second reduction uses a novel method of constructing λ-terms such that in all type derivations, specific bound variables must always be assigned a specific type. Using this technique, we can require that specific subterms must be typable using a specific, fixed type assignment in order for the entire term to be typable at all. Any desired type assignment may be simulated. We develop this method, which we call "constants for free", for both the λK and λI calculi.

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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. We ask a fundamental question: What is the basic predictive power of TCP of network state, including wireless error conditions? The goal is to improve or readily exploit this predictive power to enable TCP (or variants) to perform well in generalized network settings. To that end, we use Maximum Likelihood Ratio tests to evaluate TCP as a detector/estimator. We quantify how well network state can be estimated, given network response such as distributions of packet delays or TCP throughput that are conditioned on the type of packet loss. Using our model-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 detector can be built; distributions of network loads can provide effective means for estimating packet loss type; and packet delay is a better signal of network state than short-term throughput. We demonstrate how estimation accuracy is influenced by different proportions of congestion versus wireless losses and penalties on incorrect estimation.

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(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.

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Accurate knowledge of traffic demands in a communication network enables or enhances a variety of traffic engineering and network management tasks of paramount importance for operational networks. Directly measuring a complete set of these demands is prohibitively expensive because of the huge amounts of data that must be collected and the performance impact that such measurements would impose on the regular behavior of the network. As a consequence, we must rely on statistical techniques to produce estimates of actual traffic demands from partial information. The performance of such techniques is however limited due to their reliance on limited information and the high amount of computations they incur, which limits their convergence behavior. In this paper we study a two-step approach for inferring network traffic demands. First we elaborate and evaluate a modeling approach for generating good starting points to be fed to iterative statistical inference techniques. We call these starting points informed priors since they are obtained using actual network information such as packet traces and SNMP link counts. Second we provide a very fast variant of the EM algorithm which extends its computation range, increasing its accuracy and decreasing its dependence on the quality of the starting point. Finally, we evaluate and compare alternative mechanisms for generating starting points and the convergence characteristics of our EM algorithm against a recently proposed Weighted Least Squares approach.

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As the World Wide Web (Web) is increasingly adopted as the infrastructure for large-scale distributed information systems, issues of performance modeling become ever more critical. In particular, locality of reference is an important property in the performance modeling of distributed information systems. In the case of the Web, understanding the nature of reference locality will help improve the design of middleware, such as caching, prefetching, and document dissemination systems. For example, good measurements of reference locality would allow us to generate synthetic reference streams with accurate performance characteristics, would allow us to compare empirically measured streams to explain differences, and would allow us to predict expected performance for system design and capacity planning. In this paper we propose models for both temporal and spatial locality of reference in streams of requests arriving at Web servers. We show that simple models based only on document popularity (likelihood of reference) are insufficient for capturing either temporal or spatial locality. Instead, we rely on an equivalent, but numerical, representation of a reference stream: a stack distance trace. We show that temporal locality can be characterized by the marginal distribution of the stack distance trace, and we propose models for typical distributions and compare their cache performance to our traces. We also show that spatial locality in a reference stream can be characterized using the notion of self-similarity. Self-similarity describes long-range correlations in the dataset, which is a property that previous researchers have found hard to incorporate into synthetic reference strings. We show that stack distance strings appear to be strongly self-similar, and we provide measurements of the degree of self-similarity in our traces. Finally, we discuss methods for generating synthetic Web traces that exhibit the properties of temporal and spatial locality that we measured in our data.

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Reliability and availability have long been considered twin system properties that could be enhanced by distribution. Paradoxically, the traditional definitions of these properties do not recognize the positive impact of recovery as distinct from simple repair and restart on reliability, nor the negative effect of recovery, and of internetworking of clients and servers, on availability. As a result of employing the standard definitions, reliability would tend to be underestimated, and availability overestimated. We offer revised definitions of these two critical metrics, which we call service reliability and service availability, that improve the match between their formal expression, and intuitive meaning. A fortuitous advantage of our approach is that the product of our two metrics yields a highly meaningful figure of merit for the overall dependability of a system. But techniques that enhance system dependability exact a performance cost, so we conclude with a cohesive definition of performability that rewards the system for performance that is delivered to its client applications, after discounting the following consequences of failure: service denial and interruption, lost work, and recovery cost.

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The congestion control mechanisms of TCP make it vulnerable in an environment where flows with different congestion-sensitivity compete for scarce resources. With the increasing amount of unresponsive UDP traffic in today's Internet, new mechanisms are needed to enforce fairness in the core of the network. We propose a scalable Diffserv-like architecture, where flows with different characteristics are classified into separate service queues at the routers. Such class-based isolation provides protection so that flows with different characteristics do not negatively impact one another. In this study, we examine different aspects of UDP and TCP interaction and possible gains from segregating UDP and TCP into different classes. We also investigate the utility of further segregating TCP flows into two classes, which are class of short and class of long flows. Results are obtained analytically for both Tail-drop and Random Early Drop (RED) routers. Class-based isolation have the following salient features: (1) better fairness, (2) improved predictability for all kinds of flows, (3) lower transmission delay for delay-sensitive flows, and (4) better control over Quality of Service (QoS) of a particular traffic type.

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An automated system for detection of head movements is described. The goal is to label relevant head gestures in video of American Sign Language (ASL) communication. In the system, a 3D head tracker recovers head rotation and translation parameters from monocular video. Relevant head gestures are then detected by analyzing the length and frequency of the motion signal's peaks and valleys. Each parameter is analyzed independently, due to the fact that a number of relevant head movements in ASL are associated with major changes around one rotational axis. No explicit training of the system is necessary. Currently, the system can detect "head shakes." In experimental evaluation, classification performance is compared against ground-truth labels obtained from ASL linguists. Initial results are promising, as the system matches the linguists' labels in a significant number of cases.

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A secure sketch (defined by Dodis et al.) is an algorithm that on an input w produces an output s such that w can be reconstructed given its noisy version w' and s. Security is defined in terms of two parameters m and m˜ : if w comes from a distribution of entropy m, then a secure sketch guarantees that the distribution of w conditioned on s has entropy m˜ , where λ = m−m˜ is called the entropy loss. In this note we show that the entropy loss of any secure sketch (or, more generally, any randomized algorithm) on any distribution is no more than it is on the uniform distribution.

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snBench is a platform on which novice users compose and deploy distributed Sense and Respond programs for simultaneous execution on a shared, distributed infrastructure. It is a natural imperative that we have the ability to (1) verify the safety/correctness of newly submitted tasks and (2) derive the resource requirements for these tasks such that correct allocation may occur. To achieve these goals we have established a multi-dimensional sized type system for our functional-style Domain Specific Language (DSL) called Sensor Task Execution Plan (STEP). In such a type system data types are annotated with a vector of size attributes (e.g., upper and lower size bounds). Tracking multiple size aspects proves essential in a system in which Images are manipulated as a first class data type, as image manipulation functions may have specific minimum and/or maximum resolution restrictions on the input they can correctly process. Through static analysis of STEP instances we not only verify basic type safety and establish upper computational resource bounds (i.e., time and space), but we also derive and solve data and resource sizing constraints (e.g., Image resolution, camera capabilities) from the implicit constraints embedded in program instances. In fact, the static methods presented here have benefit beyond their application to Image data, and may be extended to other data types that require tracking multiple dimensions (e.g., image "quality", video frame-rate or aspect ratio, audio sampling rate). In this paper we present the syntax and semantics of our functional language, our type system that builds costs and resource/data constraints, and (through both formalism and specific details of our implementation) provide concrete examples of how the constraints and sizing information are used in practice.

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The initial phase in a content distribution (file sharing) scenario is a delicate phase due to the lack of global knowledge and the dynamics of the overlay. An unwise distribution of the pieces in this phase can cause delays in reaching steady state, thus increasing file download times. We devise a scheduling algorithm at the seed (source peer with full content), based on a proportional fair approach, and we implement it on a real file sharing client [1]. In dynamic overlays, our solution improves up to 25% the average downloading time of a standard protocol ala BitTorrent.

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Quality of Service (QoS) guarantees are required by an increasing number of applications to ensure a minimal level of fidelity in the delivery of application data units through the network. Application-level QoS does not necessarily follow from any transport-level QoS guarantees regarding the delivery of the individual cells (e.g. ATM cells) which comprise the application's data units. The distinction between application-level and transport-level QoS guarantees is due primarily to the fragmentation that occurs when transmitting large application data units (e.g. IP packets, or video frames) using much smaller network cells, whereby the partial delivery of a data unit is useless; and, bandwidth spent to partially transmit the data unit is wasted. The data units transmitted by an application may vary in size while being constant in rate, which results in a variable bit rate (VBR) data flow. That data flow requires QoS guarantees. Statistical multiplexing is inadequate, because no guarantees can be made and no firewall property exists between different data flows. In this paper, we present a novel resource management paradigm for the maintenance of application-level QoS for VBR flows. Our paradigm is based on Statistical Rate Monotonic Scheduling (SRMS), in which (1) each application generates its variable-size data units at a fixed rate, (2) the partial delivery of data units is of no value to the application, and (3) the QoS guarantee extended to the application is the probability that an arbitrary data unit will be successfully transmitted through the network to/from the application.

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In a recent paper (Changes in Web Client Access Patterns: Characteristics and Caching Implications by Barford, Bestavros, Bradley, and Crovella) we performed a variety of analyses upon user traces collected in the Boston University Computer Science department in 1995 and 1998. A sanitized version of the 1995 trace has been publicly available for some time; the 1998 trace has now been sanitized, and is available from: http://www.cs.bu.edu/techreports/1999-011-usertrace-98.gz ftp://ftp.cs.bu.edu/techreports/1999-011-usertrace-98.gz This memo discusses the format of this public version of the log, and includes additional discussion of how the data was collected, how the log was sanitized, what this log is and is not useful for, and areas of potential future research interest.

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The relative importance of long-term popularity and short-term temporal correlation of references for Web cache replacement policies has not been studied thoroughly. This is partially due to the lack of accurate characterization of temporal locality that enables the identification of the relative strengths of these two sources of temporal locality in a reference stream. In [21], we have proposed such a metric and have shown that Web reference streams differ significantly in the prevalence of these two sources of temporal locality. These finding underscore the importance of a Web caching strategy that can adapt in a dynamic fashion to the prevalence of these two sources of temporal locality. In this paper, we propose a novel cache replacement algorithm, GreedyDual*, which is a generalization of GreedyDual-Size. GreedyDual* uses the metrics proposed in [21] to adjust the relative worth of long-term popularity versus short-term temporal correlation of references. Our trace-driven simulation experiments show the superior performance of GreedyDual* when compared to other Web cache replacement policies proposed in the literature.

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The data streaming model provides an attractive framework for one-pass summarization of massive data sets at a single observation point. However, in an environment where multiple data streams arrive at a set of distributed observation points, sketches must be computed remotely and then must be aggregated through a hierarchy before queries may be conducted. As a result, many sketch-based methods for the single stream case do not apply directly, as either the error introduced becomes large, or because the methods assume that the streams are non-overlapping. These limitations hinder the application of these techniques to practical problems in network traffic monitoring and aggregation in sensor networks. To address this, we develop a general framework for evaluating and enabling robust computation of duplicate-sensitive aggregate functions (e.g., SUM and QUANTILE), over data produced by distributed sources. We instantiate our approach by augmenting the Count-Min and Quantile-Digest sketches to apply in this distributed setting, and analyze their performance. We conclude with experimental evaluation to validate our analysis.