An Omniscient Scheduling Oracle for Systems with Harmonic Periods

Most real-time scheduling problems are known to be NP-complete. To enable accurate comparison between the schedules of heuristic algorithms and the optimal schedule, we introduce an omniscient oracle. This oracle provides schedules for periodic task sets with harmonic periods and variable resource requirements. Three different job value functions are described and implemented. Each corresponds to a different system goal. The oracle is used to examine the performance of different on-line schedulers under varying loads, including overload. We have compared the oracle against Rate Monotonic Scheduling, Statistical Rate Monotonic Scheduling, and Slack Stealing Job Admission Control Scheduling. Consistently, the oracle provides an upper bound on performance for the metric under consideration.

Temporal Locality in Web Request Streams: Sources, Characteristics, and Caching Implications

Temporal locality of reference in Web request streams emerges from two distinct phenomena: the popularity of Web objects and the {\em temporal correlation} of requests. Capturing these two elements of temporal locality is important because it enables cache replacement policies to adjust how they capitalize on temporal locality based on the relative prevalence of these phenomena. In this paper, we show that temporal locality metrics proposed in the literature are unable to delineate between these two sources of temporal locality. In particular, we show that the commonly-used distribution of reference interarrival times is predominantly determined by the power law governing the popularity of documents in a request stream. To capture (and more importantly quantify) both sources of temporal locality in a request stream, we propose a new and robust metric that enables accurate delineation between locality due to popularity and that due to temporal correlation. Using this metric, we characterize the locality of reference in a number of representative proxy cache traces. Our findings show that there are measurable differences between the degrees (and sources) of temporal locality across these traces, and that these differences are effectively captured using our proposed metric. We illustrate the significance of our findings by summarizing the performance of a novel Web cache replacement policy---called GreedyDual*---which exploits both long-term popularity and short-term temporal correlation in an adaptive fashion. Our trace-driven simulation experiments (which are detailed in an accompanying Technical Report) show the superior performance of GreedyDual* when compared to other Web cache replacement policies.

Utility-Based Decision-Making in Wireless Sensor Networks

We consider challenges associated with application domains in which a large number of distributed, networked sensors must perform a sensing task repeatedly over time. For the tasks we consider, there are three significant challenges to address. First, nodes have resource constraints imposed by their finite power supply, which motivates computations that are energy-conserving. Second, for the applications we describe, the utility derived from a sensing task may vary depending on the placement and size of the set of nodes who participate, which often involves complex objective functions for nodes to target. Finally, nodes must attempt to realize these global objectives with only local information. We present a model for such applications, in which we define appropriate global objectives based on utility functions and specify a cost model for energy consumption. Then, for an important class of utility functions, we present distributed algorithms which attempt to maximize the utility derived from the sensor network over its lifetime. The algorithms and experimental results we present enable nodes to adaptively change their roles over time and use dynamic reconfiguration of routes to load balance energy consumption in the network.

Typed Abstraction of Complex Network Compositions

The heterogeneity and open nature of network systems make analysis of compositions of components quite challenging, making the design and implementation of robust network services largely inaccessible to the average programmer. We propose the development of a novel type system and practical type spaces which reflect simplified representations of the results and conclusions which can be derived from complex compositional theories in more accessible ways, essentially allowing the system architect or programmer to be exposed only to the inputs and output of compositional analysis without having to be familiar with the ins and outs of its internals. Toward this end we present the TRAFFIC (Typed Representation and Analysis of Flows For Interoperability Checks) framework, a simple flow-composition and typing language with corresponding type system. We then discuss and demonstrate the expressive power of a type space for TRAFFIC derived from the network calculus, allowing us to reason about and infer such properties as data arrival, transit, and loss rates in large composite network applications.

Safe Compositional Specification of Networking Systems: TRAFFIC The Language and Its Type Checking

This paper formally defines the operational semantic for TRAFFIC, a specification language for flow composition applications proposed in BUCS-TR-2005-014, and presents a type system based on desired safety assurance. We provide proofs on reduction (weak-confluence, strong-normalization and unique normal form), on soundness and completeness of type system with respect to reduction, and on equivalence classes of flow specifications. Finally, we provide a pseudo-code listing of a syntax-directed type checking algorithm implementing rules of the type system capable of inferring the type of a closed flow specification.

Type Systems for a Network Specification Language With Multiple-Choice Let

When analysing the behavior of complex networked systems, it is often the case that some components within that network are only known to the extent that they belong to one of a set of possible "implementations" – e.g., versions of a specific protocol, class of schedulers, etc. In this report we augment the specification language considered in BUCSTR-2004-021, BUCS-TR-2005-014, BUCS-TR-2005-015, and BUCS-TR-2005-033, to include a non-deterministic multiple-choice let-binding, which allows us to consider compositions of networking subsystems that allow for looser component specifications.

Extending snBench to Support a Graphical Programming Interface for a Sensor Network Tasking Language (STEP)

The purpose of this project is the creation of a graphical "programming" interface for a sensor network tasking language called STEP. The graphical interface allows the user to specify a program execution graphically from an extensible pallet of functionalities and save the results as a properly formatted STEP file. Moreover, the software is able to load a file in STEP format and convert it into the corresponding graphical representation. During both phases a type-checker is running on the background to ensure that both the graphical representation and the STEP file are syntactically correct. This project has been motivated by the Sensorium project at Boston University. In this technical report we present the basic features of the software, the process that has been followed during the design and implementation. Finally, we describe the approach used to test and validate our software.

Anisotropic Interpolation on Graphs: The Combinatorial Dirichlet Problem

The combinatorial Dirichlet problem is formulated, and an algorithm for solving it is presented. This provides an effective method for interpolating missing data on weighted graphs of arbitrary connectivity. Image processing examples are shown, and the relation to anistropic diffusion is discussed.

Fixational Instability and Natural Image Statistics: Implications for Early Visual Representations

Under natural viewing conditions small movements of the eye, head, and body prevent the maintenance of a steady direction of gaze. It is known that stimuli tend to fade when they a restabilized on the retina for several seconds. However; it is unclear whether the physiological motion of the retinal image serves a visual purpose during the brief periods of natural visual fixation. This study examines the impact of fixational instability on the statistics of the visua1 input to the retina and on the structure of neural activity in the early visual system. We show that fixational instability introduces a component in the retinal input signals that in the presence of natural images, lacks spatial correlations. This component strongly influences neural activity in a model of the LGN. It decorrelates cell responses even if the contrast sensitivity functions of simulated cells arc not perfectly tuned to counterbalance the power-law spectrum of natural images. A decorrelation of neural activity at the early stages of the visual system has been proposed to be beneficial for discarding statistical redundancies in the input signals. The results of this study suggest that fixational instability might contribute to establishing efficient representations of natural stimuli.

Neural Models of Motion Integration, Segmentation, and Probablistic Decision-Making

When brain mechanism carry out motion integration and segmentation processes that compute unambiguous global motion percepts from ambiguous local motion signals? Consider, for example, a deer running at variable speeds behind forest cover. The forest cover is an occluder that creates apertures through which fragments of the deer's motion signals are intermittently experienced. The brain coherently groups these fragments into a trackable percept of the deer in its trajectory. Form and motion processes are needed to accomplish this using feedforward and feedback interactions both within and across cortical processing streams. All the cortical areas V1, V2, MT, and MST are involved in these interactions. Figure-ground processes in the form stream through V2, such as the seperation of occluding boundaries of the forest cover from the boundaries of the deer, select the motion signals which determine global object motion percepts in the motion stream through MT. Sparse, but unambiguous, feauture tracking signals are amplified before they propogate across position and are intergrated with far more numerous ambiguous motion signals. Figure-ground and integration processes together determine the global percept. A neural model predicts the processing stages that embody these form and motion interactions. Model concepts and data are summarized about motion grouping across apertures in response to a wide variety of displays, and probabilistic decision making in parietal cortex in response to random dot displays.

Fuzzy ARTMAP, Slow Learning and Probability Estimation

A nonparametric probability estimation procedure using the fuzzy ARTMAP neural network is here described. Because the procedure does not make a priori assumptions about underlying probability distributions, it yields accurate estimates on a wide variety of prediction tasks. Fuzzy ARTMAP is used to perform probability estimation in two different modes. In a 'slow-learning' mode, input-output associations change slowly, with the strength of each association computing a conditional probability estimate. In 'max-nodes' mode, a fixed number of categories are coded during an initial fast learning interval, and weights are then tuned by slow learning. Simulations illustrate system performance on tasks in which various numbers of clusters in the set of input vectors mapped to a given class.

Working Memory Networks for Learning Temporal Order, with Application to 3-D Visual Object Recognition

Working memory neural networks are characterized which encode the invariant temporal order of sequential events. Inputs to the networks, called Sustained Temporal Order REcurrent (STORE) models, may be presented at widely differing speeds, durations, and interstimulus intervals. The STORE temporal order code is designed to enable all emergent groupings of sequential events to be stably learned and remembered in real time, even as new events perturb the system. Such a competence is needed in neural architectures which self-organize learned codes for variable-rate speech perception, sensory-motor planning, or 3-D visual object recognition. Using such a working memory, a self-organizing architecture for invariant 3-D visual object recognition is described. The new model is based on the model of Seibert and Waxman (1990a), which builds a 3-D representation of an object from a temporally ordered sequence of its 2-D aspect graphs. The new model, called an ARTSTORE model, consists of the following cascade of processing modules: Invariant Preprocessor --> ART 2 --> STORE Model --> ART 2 --> Outstar Network.

Genetically Generated Neural Networks I: Representational Effects

This paper studies several applications of genetic algorithms (GAs) within the neural networks field. After generating a robust GA engine, the system was used to generate neural network circuit architectures. This was accomplished by using the GA to determine the weights in a fully interconnected network. The importance of the internal genetic representation was shown by testing different approaches. The effects in speed of optimization of varying the constraints imposed upon the desired network were also studied. It was observed that relatively loose constraints provided results comparable to a fully constrained system. The type of neural network circuits generated were recurrent competitive fields as described by Grossberg (1982).