Opportunities and obligations for physical computing systems

The recent confluence of embedded and real-time systems with wireless, sensor, and networking technologies is creating a nascent infrastructure for a technical, economic, and social revolution. Based on the seamless integration of computing with the physical world via sensors and actuators, this revolution will accrue many benefits. Potentially, its impact could be similar to that of the current Internet. We believe developers must focus on the physical, real-time, and embedded aspects of pervasive computing. We refer to this domain as physical computing systems. For pervasive computing to achieve its promise, developers must create not only high-level system software and application solutions, but also low-level embedded systems solutions. To better understand physical computing\u27s advantages, we consider three application areas: assisted living, emergency response systems for natural or man-made disasters, and protecting critical infrastructures at the national level

The knowledge-based software assistant

Where the Knowledge Based Software Assistant (KBSA) is now, four years after the initial report, is discussed. Also described is what the Rome Air Development Center expects at the end of the first contract iteration. What the second and third contract iterations will look like are characterized

Curracurrong: a stream processing system for distributed environments

Advances in technology have given rise to applications that are deployed on wireless sensor networks (WSNs), the cloud, and the Internet of things. There are many emerging applications, some of which include sensor-based monitoring, web traffic processing, and network monitoring. These applications collect large amount of data as an unbounded sequence of events and process them to generate a new sequences of events. Such applications need an adequate programming model that can process large amount of data with minimal latency; for this purpose, stream programming, among other paradigms, is ideal. However, stream programming needs to be adapted to meet the challenges inherent in running it in distributed environments. These challenges include the need for modern domain specific language (DSL), the placement of computations in the network to minimise energy costs, and timeliness in real-time applications. To overcome these challenges we developed a stream programming model that achieves easy-to-use programming interface, energy-efficient actor placement, and timeliness. This thesis presents Curracurrong, a stream data processing system for distributed environments. In Curracurrong, a query is represented as a stream graph of stream operators and communication channels. Curracurrong provides an extensible stream operator library and adapts to a wide range of applications. It uses an energy-efficient placement algorithm that optimises communication and computation. We extend the placement problem to support dynamically changing networks, and develop a dynamic program with polynomially bounded runtime to solve the placement problem. In many stream-based applications, real-time data processing is essential. We propose an approach that measures time delays in stream query processing; this model measures the total computational time from input to output of a query, i.e., end-to-end delay

Curracurrong: a stream processing system for distributed environments

Advances in technology have given rise to applications that are deployed on wireless sensor networks (WSNs), the cloud, and the Internet of things. There are many emerging applications, some of which include sensor-based monitoring, web traffic processing, and network monitoring. These applications collect large amount of data as an unbounded sequence of events and process them to generate a new sequences of events. Such applications need an adequate programming model that can process large amount of data with minimal latency; for this purpose, stream programming, among other paradigms, is ideal. However, stream programming needs to be adapted to meet the challenges inherent in running it in distributed environments. These challenges include the need for modern domain specific language (DSL), the placement of computations in the network to minimise energy costs, and timeliness in real-time applications. To overcome these challenges we developed a stream programming model that achieves easy-to-use programming interface, energy-efficient actor placement, and timeliness. This thesis presents Curracurrong, a stream data processing system for distributed environments. In Curracurrong, a query is represented as a stream graph of stream operators and communication channels. Curracurrong provides an extensible stream operator library and adapts to a wide range of applications. It uses an energy-efficient placement algorithm that optimises communication and computation. We extend the placement problem to support dynamically changing networks, and develop a dynamic program with polynomially bounded runtime to solve the placement problem. In many stream-based applications, real-time data processing is essential. We propose an approach that measures time delays in stream query processing; this model measures the total computational time from input to output of a query, i.e., end-to-end delay

Large scale numerical software development using functional languages

PhD ThesisFunctional programming languages such as Haskell allow numerical algorithms to be expressed in a concise, machine-independent manner that closely reflects the underlying mathematical notation in which the algorithm is described. Unfortunately the price paid for this level of abstraction is usually a considerable increase in execution time and space usage. This thesis presents a three-part study of the use of modern purely-functional languages to develop numerical software. In Part I the appropriateness and usefulness of language features such as polymorphism. pattern matching, type-class overloading and non-strict semantics are discussed together with the limitations they impose. Quantitative statistics concerning the manner in which these features are used in practice are also presented. In Part II the information gathered from Part I is used to design and implement FSC. all experimental functional language tailored to numerical computing, motivated as much by pragmatic as theoretical issues. This language is then used to develop numerical software and its suitability assessed via benchmarking it against C/C++ and Haskell under various metrics. In Part III the work is summarised and assessed.EPSRC

A survey of sequential and parallel implementation techniques for functional programming languages

This paper surveys sequential and parallel implementation techniques for functional programming languages, as well as optimizations that can improve their performance. Sequential implementations have evolved from simple interpreters to sophisticated super-combinator-based compilers, while most parallel implementations have explored a broad range of techniques. We analyze the purpose and function of each implementation technique and discuss the current state-of-the-art in functional language implementation

Design of testbed and emulation tools

The research summarized was concerned with the design of testbed and emulation tools suitable to assist in projecting, with reasonable accuracy, the expected performance of highly concurrent computing systems on large, complete applications. Such testbed and emulation tools are intended for the eventual use of those exploring new concurrent system architectures and organizations, either as users or as designers of such systems. While a range of alternatives was considered, a software based set of hierarchical tools was chosen to provide maximum flexibility, to ease in moving to new computers as technology improves and to take advantage of the inherent reliability and availability of commercially available computing systems

Repetitive Model Refactoring for Design Space Exploration of Intensive Signal Processing Applications

The efficient design of computation intensive multidimensional signal processing application requires to deal with three kinds of constraints: those implied by the data dependencies, the non functional requirements (real-time, power consumption) and the availability of resources of the execution platform. We propose here a strategy to use a refactoring tool dedicated to this kind of applications to help explore the design space. This strategy is illustrated on an industrial radar application modeled using the Modeling and Analysis of Real-time and Embedded systems (MARTE) UML profile. It allows to find good trade-offs in the usage of storage and computation resources and in the parallelism (both task and data parallelism) exploitation