ContextErlang: A language for distributed context-aware self-adaptive applications

Self-adaptive software modifies its behavior at run time to satisfy changing requirements in a dynamic environment. Context-oriented programming (COP) has been recently proposed as a specialized programming paradigm for context-aware and adaptive systems. COP mostly focuses on run time adaptation of the application’s behavior by supporting modular descriptions of behavioral variations. However, self-adaptive applications must satisfy additional requirements, such as distribution and concurrency, support for unforeseen changes and enforcement of correct behavior in the presence of dynamic change. Addressing these issues at the language level requires a holistic design that covers all aspects and takes into account the possibly cumbersome interaction of those features, for example concurrency and dynamic change. We present ContextErlang, a COP programming language in which adaptive abstractions are seamlessly integrated with distribution and concurrency. We define ContextErlang’s formal semantics, validated through an executable prototype, and we show how it supports formal proofs that the language design ensures satisfaction of certain safety requirements. We provide empirical evidence that ContextErlang is an effective solution through case studies and a performance assessment. We also show how the same design principles that lead to the development of ContextErlang can be followed to systematically design contextual extensions of other languages. A concrete example is presented concerning ContextScala

Online machine learning approach to multicore data structures

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 175-180).As multicores become prevalent, the complexity of programming is skyrocketing. One major difficulty is eciently orchestrating collaboration among threads through shared data structures. Unfortunately, choosing and hand-tuning data structure algorithms to get good performance across a variety of machines and inputs is a herculean task to add to the fundamental difficulty of getting a parallel program correct. To help mitigate these complexities, this work develops a new class of parallel data structures called Smart Data Structures that leverage online machine learning to adapt themselves automatically. We prototype and evaluate an open source library of Smart Data Structures for common parallel programming needs and demonstrate signicant improvements over the best existing algorithms under a variety of conditions. Our results indicate that learning is a promising technique for balancing and adapting to complex, time-varying tradeoffs and achieving the best performance available.by Jonathan M. Eastep.Ph.D

CoAP Infrastructure for IoT

The Internet of Things (IoT) can be seen as a large-scale network of billions of smart devices. Often IoT devices exchange data in small but numerous messages, which requires IoT services to be more scalable and reliable than ever. Traditional protocols that are known in the Web world does not fit well in the constrained environment that these devices operate in. Therefore many lightweight protocols specialized for the IoT have been studied, among which the Constrained Application Protocol (CoAP) stands out for its well-known REST paradigm and easy integration with existing Web. On the other hand, new paradigms such as Fog Computing emerges, attempting to avoid the centralized bottleneck in IoT services by moving computations to the edge of the network. Since a node of the Fog essentially belongs to relatively constrained environment, CoAP fits in well. Among the many attempts of building scalable and reliable systems, Erlang as a typical concurrency-oriented programming (COP) language has been battle tested in the telecom industry, which has similar requirements as the IoT. In order to explore the possibility of applying Erlang and COP in general to the IoT, this thesis presents an Erlang based CoAP server/client prototype ecoap with a flexible concurrency model that can scale up to an unconstrained environment like the Cloud and scale down to a constrained environment like an embedded platform. The flexibility of the presented server renders the same architecture applicable from Fog to Cloud. To evaluate its performance, the proposed server is compared with the mainstream CoAP implementation on an Amazon Web Service (AWS) Cloud instance and a Raspberry Pi 3, representing the unconstrained and constrained environment respectively. The ecoap server achieves comparable throughput, lower latency, and in general scales better than the other implementation in the Cloud and on the Raspberry Pi. The thesis yields positive results and demonstrates the value of the philosophy of Erlang in the IoT space

New hardware support transactional memory and parallel debugging in multicore processors

This thesis contributes to the area of hardware support for parallel programming by introducing new hardware elements in multicore processors, with the aim of improving the performance and optimize new tools, abstractions and applications related with parallel programming, such as transactional memory and data race detectors. Specifically, we configure a hardware transactional memory system with signatures as part of the hardware support, and we develop a new hardware filter for reducing the signature size. We also develop the first hardware asymmetric data race detector (which is also able to tolerate them), based also in hardware signatures. Finally, we propose a new module of hardware signatures that solves some of the problems that we found in the previous tools related with the lack of flexibility in hardware signatures

Dataflow development of medium-grained parallel software

PhD ThesisIn the 1980s, multiple-processor computers (multiprocessors) based on conven- tional processing elements emerged as a popular solution to the continuing demand for ever-greater computing power. These machines offer a general-purpose parallel processing platform on which the size of program units which can be efficiently executed in parallel - the "grain size" - is smaller than that offered by distributed computing environments, though greater than that of some more specialised architectures. However, programming to exploit this medium-grained parallelism remains difficult. Concurrent execution is inherently complex, yet there is a lack of programming tools to support parallel programming activities such as program design, implementation, debugging, performance tuning and so on. In helping to manage complexity in sequential programming, visual tools have often been used to great effect, which suggests one approach towards the goal of making parallel programming less difficult. This thesis examines the possibilities which the dataflow paradigm has to offer as the basis for a set of visual parallel programming tools, and presents a dataflow notation designed as a framework for medium-grained parallel programming. The implementation of this notation as a programming language is discussed, and its suitability for the medium-grained level is examinedScience and Engineering Research Council of Great Britain EC ERASMUS schem

Modeling and Mapping of Optimized Schedules for Embedded Signal Processing Systems

The demand for Digital Signal Processing (DSP) in embedded systems has been increasing rapidly due to the proliferation of multimedia- and communication-intensive devices such as pervasive tablets and smart phones. Efficient implementation of embedded DSP systems requires integration of diverse hardware and software components, as well as dynamic workload distribution across heterogeneous computational resources. The former implies increased complexity of application modeling and analysis, but also brings enhanced potential for achieving improved energy consumption, cost or performance. The latter results from the increased use of dynamic behavior in embedded DSP applications. Furthermore, parallel programming is highly relevant in many embedded DSP areas due to the development and use of Multiprocessor System-On-Chip (MPSoC) technology. The need for efficient cooperation among different devices supporting diverse parallel embedded computations motivates high-level modeling that expresses dynamic signal processing behaviors and supports efficient task scheduling and hardware mapping. Starting with dynamic modeling, this thesis develops a systematic design methodology that supports functional simulation and hardware mapping of dynamic reconfiguration based on Parameterized Synchronous Dataflow (PSDF) graphs. By building on the DIF (Dataflow Interchange Format), which is a design language and associated software package for developing and experimenting with dataflow-based design techniques for signal processing systems, we have developed a novel tool for functional simulation of PSDF specifications. This simulation tool allows designers to model applications in PSDF and simulate their functionality, including use of the dynamic parameter reconfiguration capabilities offered by PSDF. With the help of this simulation tool, our design methodology helps to map PSDF specifications into efficient implementations on field programmable gate arrays (FPGAs). Furthermore, valid schedules can be derived from the PSDF models at runtime to adapt hardware configurations based on changing data characteristics or operational requirements. Under certain conditions, efficient quasi-static schedules can be applied to reduce overhead and enhance predictability in the scheduling process. Motivated by the fact that scheduling is critical to performance and to efficient use of dynamic reconfiguration, we have focused on a methodology for schedule design, which complements the emphasis on automated schedule construction in the existing literature on dataflow-based design and implementation. In particular, we have proposed a dataflow-based schedule design framework called the dataflow schedule graph (DSG), which provides a graphical framework for schedule construction based on dataflow semantics, and can also be used as an intermediate representation target for automated schedule generation. Our approach to applying the DSG in this thesis emphasizes schedule construction as a design process rather than an outcome of the synthesis process. Our approach employs dataflow graphs for representing both application models and schedules that are derived from them. By providing a dataflow-integrated framework for unambiguously representing, analyzing, manipulating, and interchanging schedules, the DSG facilitates effective codesign of dataflow-based application models and schedules for execution of these models. As multicore processors are deployed in an increasing variety of embedded image processing systems, effective utilization of resources such as multiprocessor systemon-chip (MPSoC) devices, and effective handling of implementation concerns such as memory management and I/O become critical to developing efficient embedded implementations. However, the diversity and complexity of applications and architectures in embedded image processing systems make the mapping of applications onto MPSoCs difficult. We help to address this challenge through a structured design methodology that is built upon the DSG modeling framework. We refer to this methodology as the DEIPS methodology (DSG-based design and implementation of Embedded Image Processing Systems). The DEIPS methodology provides a unified framework for joint consideration of DSG structures and the application graphs from which they are derived, which allows designers to integrate considerations of parallelization and resource constraints together with the application modeling process. We demonstrate the DEIPS methodology through cases studies on practical embedded image processing systems

AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research

The fusion of machine learning and biomedical research offers novel ways to understand, diagnose, and treat various health conditions. However, the complexities of biomedical data, coupled with the intricate process of developing and deploying machine learning solutions, often pose significant challenges to researchers in these fields. Our pivotal achievement in this research is the introduction of the Automatic Semantic Machine Learning Microservice Framework (AIMS). AIMS addresses these challenges by automating various stages of the machine learning pipeline, with a particular emphasis on the ontology of machine learning services tailored for the biomedical domain. This ontology encompasses everything from task representation, service modeling, and knowledge acquisition to knowledge reasoning and the establishment of a self-supervised learning policy. Our framework has been crafted to prioritize model interpretability, integrate domain knowledge effortlessly, and handle biomedical data with efficiency. Additionally, AIMS boasts a distinctive feature: it leverages self-supervised knowledge learning through reinforcement learning techniques, paired with an ontology-based policy recording schema. This enables it to autonomously generate, fine-tune, and continually adapt to machine learning models, especially when faced with new tasks and data. Our work has two standout contributions of demonstrating that machine learning processes in the biomedical domain can be automated, while integrating a rich domain knowledge base and providing a way for machines to have a self-learning ability, ensuring they handle new tasks effectively. To showcase AIMS in action, we've highlighted its prowess in three case studies from biomedical tasks. These examples emphasize how our framework can simplify research routines, uplift the caliber of scientific exploration, and set the stage for notable advances

Functional programming languages in computing clouds: practical and theoretical explorations

Cloud platforms must integrate three pillars: messaging, coordination of workers and data. This research investigates whether functional programming languages have any special merit when it comes to the implementation of cloud computing platforms. This thesis presents the lightweight message queue CMQ and the DSL CWMWL for the coordination of workers that we use as artefact to proof or disproof the special merit of functional programming languages in computing clouds. We have detailed the design and implementation with the broad aim to match the notions and the requirements of computing clouds. Our approach to evaluate these aims is based on evaluation criteria that are based on a series of comprehensive rationales and specifics that allow the FPL Haskell to be thoroughly analysed. We find that Haskell is excellent for use cases that do not require the distribution of the application across the boundaries of (physical or virtual) systems, but not appropriate as a whole for the development of distributed cloud based workloads that require communication with the far side and coordination of decoupled workloads. However, Haskell may be able to qualify as a suitable vehicle in the future with future developments of formal mechanisms that embrace non-determinism in the underlying distributed environments leading to applications that are anti-fragile rather than applications that insist on strict determinism that can only be guaranteed on the local system or via slow blocking communication mechanisms

Functional programming languages in computing clouds: practical and theoretical explorations

Cloud platforms must integrate three pillars: messaging, coordination of workers and data. This research investigates whether functional programming languages have any special merit when it comes to the implementation of cloud computing platforms. This thesis presents the lightweight message queue CMQ and the DSL CWMWL for the coordination of workers that we use as artefact to proof or disproof the special merit of functional programming languages in computing clouds. We have detailed the design and implementation with the broad aim to match the notions and the requirements of computing clouds. Our approach to evaluate these aims is based on evaluation criteria that are based on a series of comprehensive rationales and specifics that allow the FPL Haskell to be thoroughly analysed. We find that Haskell is excellent for use cases that do not require the distribution of the application across the boundaries of (physical or virtual) systems, but not appropriate as a whole for the development of distributed cloud based workloads that require communication with the far side and coordination of decoupled workloads. However, Haskell may be able to qualify as a suitable vehicle in the future with future developments of formal mechanisms that embrace non-determinism in the underlying distributed environments leading to applications that are anti-fragile rather than applications that insist on strict determinism that can only be guaranteed on the local system or via slow blocking communication mechanisms