72 research outputs found

    Nested Pure Operation-Based CRDTs

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    Modern distributed applications increasingly replicate data to guarantee high availability and optimal user experience. Conflict-free Replicated Data Types (CRDTs) are a family of data types specially designed for highly available systems that guarantee some form of eventual consistency. Designing CRDTs is very difficult because it requires devising designs that guarantee convergence in the presence of conflicting operations. Even though design patterns and structured frameworks have emerged to aid developers with this problem, they mostly focus on statically structured data; nesting and dynamically changing the structure of a CRDT remains to be an open issue. This paper explores support for nested CRDTs in a structured and systematic way. To this end, we define an approach for building nested CRDTs based on the work of pure operation-based CRDTs, resulting in nested pure operation-based CRDTs. We add constructs to control the nesting of CRDTs into a pure operation-based CRDT framework and show how several well-known CRDT designs can be defined in our framework. We provide an implementation of nested pure operation-based CRDTs as an extension to the Flec, an existing TypeScript-based framework for pure operation-based CRDTs. We validate our approach, 1) by implementing a portfolio of nested data structures, 2) by implementing and verifying our approach in the VeriFx language, and 3) by implementing a real-world application scenario and comparing its network usage against an implementation in the closest related work, Automerge. We show that the framework is general enough to nest well-known CRDT designs like maps and lists, and its performance in terms of network traffic is comparable to the state of the art

    Improving interoperability in distributed multi-tier software stacks

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    Distributed multi-tier software stacks organise and deploy software components as a hierarchy of interacting tiers. The components are typically heterogeneous, i.e. each component may be written in a different language and may interoperate using a variety of protocols. Tiered software is modular but leads to a range of interoperability challenges including the following. (1) Interoperating components in multiple languages and paradigms increases developer cognitive load since they must simultaneously reason in multiple languages and paradigms. (2) There must be correct interoperation of components, e.g. adherence to the API or communication protocols between components. (3) Interoperation between different components can lead to diverse modes of failure as each component can fail in unique ways. Many of these challenges are the result of contributing factors like tight coupling or polyglot programmming. This thesis investigates techniques to improve heterogeneous interoperability in distributed multi-tier software stacks. Some common approaches include microservices and tierless languages. Microservices are perceived to offer better reliability than components in multi-tier software stacks through the loose coupling of services. The reliability of microservices is investigated by combining the established properties of dependence and state with reliability. This defines a new three-dimensional space: the Microservices Dependency State Reliability (MDSR) classification with six classes. The feasibility of statically identifying MDSR classes is demonstrated with a prototype analyser that identifies all six classes in Flask microservices web applications. The reliability implications of the different MDSR classes are evaluated by running three case study applications (Hipster-Shop, JPyL & WordPress) against a fault injector. Key results are as follows. (1) All applications fail catastrophically if a critical microservice fails. (2) Applications survive the failure of individual minor microservice(s). (3) The failure of any chain of microservices in JPyL & Hipster is catastrophic. (4) Individual microservices do not necessarily have minor reliability implications. In a tierless language, the compiler generates the code for each component and ensures their correct interoperation. They are mainly used to implement web stacks. However, their use in implementing IoT stacks is less common. This investigation compares interoperation in tiered and tierless IoT stacks through the systematic evaluation of four implementations of the prototype UoG smart campus IoT system: two tierless and two Python-based tiered. Key results of the study are as follows. (1) Tierless languages have the potential to significantly reduce the development effort for IoT systems, requiring 70% less code than the tiered implementations. (2) Tierless languages have the potential to significantly improve the reliability of IoT systems. (3) The first comparison of a tierless codebase for resource-rich sensor nodes and one for resourceconstrained sensor nodes shows that they have very similar functional structure and code sizes - within 7%. Tier elimination is a technique that removes a tier/component by integrating two tiers. Specifically, this thesis investigates the implications of eliminating the Apache web server in a 4-tier web stack: Jupyter Notebook, Apache, Python, Linux (JAPyL) and replacing it with PHP libraries in the frontend webpage to get the 3-tier (JPL). The study reveals the following. (1) The JPL 3-tier web stack requires that the developer uses fewer programming languages and paradigms than JAPyL, i.e two compared with four languages and two compared with three paradigms. (2) JPL requires 42% less code than JAPyL. (3) In JPL, some of the functionalities can be automated due to the decreased abstraction levels at the upper layers of the stack. (4) However, the latency in JPL is two to three times greater than that of JAPyL. So while tier elimination reduces developer effort and semantic friction the tradeoffs are high performance overhead & resource consumption and increasing code complexity

    ComPOS - a Domain-Specific Language for Composing Internet-of-Things Systems

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    Internet-of-Things (IoT) systems consist of spatially distributed interacting devices. In contrast to desktop applications, IoT systems are always running and need to deal with unresponsive devices and weak connectivity. In this thesis, we propose techniques for simplifying the development of such systems. The work addresses IoT systems organised as reusable services connected by compositions. We propose to program such compositions using stateful reactions that mediate messages. To this end, we have designed a domain-specific language (DSL), called ComPOS. To help systems operate partly in cases of weak connectivity, we propose that ComPOS aborts older reactions when newer messages arrive. We evaluate our DSL in home-automation and e-health scenarios. Understanding IoT systems can be hard, and different analyses can help explain how they work. To support analysis, we propose a conceptual runtime model based on relational reference attribute grammars. We demonstrate the approach by formulating and implementing a Device Dependency Analysis (DDA). The DDA finds sets of devices needed for given parts of the system to work. The ComPOS editor supports live programming to allow development while the system is running. We propose a methodology for live ComPOS programming which divides the development into three, iteratively applied, phases: finding services (explore), composing services (assemble), and abstracting compositions as new services (expose). When developing a DSL, it takes substantial effort to specify the syntax and semantics, to build tools like editors, and to integrate with the environment (in this case the underlying middleware). To reduce the effort needed to experiment with ComPOS, we have created a tool called Jatte. Jatte is a generic projectional editor that developers can tune using attribute grammars. We used Jatte to implement the ComPOS editor

    Engineering Self-Adaptive Collective Processes for Cyber-Physical Ecosystems

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    The pervasiveness of computing and networking is creating significant opportunities for building valuable socio-technical systems. However, the scale, density, heterogeneity, interdependence, and QoS constraints of many target systems pose severe operational and engineering challenges. Beyond individual smart devices, cyber-physical collectives can provide services or solve complex problems by leveraging a “system effect” while coordinating and adapting to context or environment change. Understanding and building systems exhibiting collective intelligence and autonomic capabilities represent a prominent research goal, partly covered, e.g., by the field of collective adaptive systems. Therefore, drawing inspiration from and building on the long-time research activity on coordination, multi-agent systems, autonomic/self-* systems, spatial computing, and especially on the recent aggregate computing paradigm, this thesis investigates concepts, methods, and tools for the engineering of possibly large-scale, heterogeneous ensembles of situated components that should be able to operate, adapt and self-organise in a decentralised fashion. The primary contribution of this thesis consists of four main parts. First, we define and implement an aggregate programming language (ScaFi), internal to the mainstream Scala programming language, for describing collective adaptive behaviour, based on field calculi. Second, we conceive of a “dynamic collective computation” abstraction, also called aggregate process, formalised by an extension to the field calculus, and implemented in ScaFi. Third, we characterise and provide a proof-of-concept implementation of a middleware for aggregate computing that enables the development of aggregate systems according to multiple architectural styles. Fourth, we apply and evaluate aggregate computing techniques to edge computing scenarios, and characterise a design pattern, called Self-organising Coordination Regions (SCR), that supports adjustable, decentralised decision-making and activity in dynamic environments.Con lo sviluppo di informatica e intelligenza artificiale, la diffusione pervasiva di device computazionali e la crescente interconnessione tra elementi fisici e digitali, emergono innumerevoli opportunità per la costruzione di sistemi socio-tecnici di nuova generazione. Tuttavia, l'ingegneria di tali sistemi presenta notevoli sfide, data la loro complessità—si pensi ai livelli, scale, eterogeneità, e interdipendenze coinvolti. Oltre a dispositivi smart individuali, collettivi cyber-fisici possono fornire servizi o risolvere problemi complessi con un “effetto sistema” che emerge dalla coordinazione e l'adattamento di componenti fra loro, l'ambiente e il contesto. Comprendere e costruire sistemi in grado di esibire intelligenza collettiva e capacità autonomiche è un importante problema di ricerca studiato, ad esempio, nel campo dei sistemi collettivi adattativi. Perciò, traendo ispirazione e partendo dall'attività di ricerca su coordinazione, sistemi multiagente e self-*, modelli di computazione spazio-temporali e, specialmente, sul recente paradigma di programmazione aggregata, questa tesi tratta concetti, metodi, e strumenti per l'ingegneria di ensemble di elementi situati eterogenei che devono essere in grado di lavorare, adattarsi, e auto-organizzarsi in modo decentralizzato. Il contributo di questa tesi consiste in quattro parti principali. In primo luogo, viene definito e implementato un linguaggio di programmazione aggregata (ScaFi), interno al linguaggio Scala, per descrivere comportamenti collettivi e adattativi secondo l'approccio dei campi computazionali. In secondo luogo, si propone e caratterizza l'astrazione di processo aggregato per rappresentare computazioni collettive dinamiche concorrenti, formalizzata come estensione al field calculus e implementata in ScaFi. Inoltre, si analizza e implementa un prototipo di middleware per sistemi aggregati, in grado di supportare più stili architetturali. Infine, si applicano e valutano tecniche di programmazione aggregata in scenari di edge computing, e si propone un pattern, Self-Organising Coordination Regions, per supportare, in modo decentralizzato, attività decisionali e di regolazione in ambienti dinamici

    Action-Oriented Programming Model: Collective Executions and Interactions in the Fog

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    Today’s dominant design for the Internet of Things (IoT) is a Cloud-based system, where devices transfer their data to a back-end and in return receive instructions on how to act. This view is challenged when delays caused by communication with the back-end become an obstacle for the IoT applications with e.g., stringent timing constraints. In contrast, Fog Computing approaches, where devices communicate and orchestrate their operations collectively and closer to the origin of data, lack adequate tools for programming secure interactions between humans and their proximate devices at the network edge. This paper fills the gap by applying Action-Oriented Programming (AcOP) model for this task. While originally the AcOP model was proposed for Cloud-based infrastructures, presently it is re-designed around the notion of coalescence and disintegration, which enable the devices to collectively and autonomously execute their operations in the Fog by serving humans in a peer-to-peer fashion. The Cloud’s role has been minimized—it is being leveraged as a development and deployment platform.Peer reviewe

    A Study of Concurrency Bugs and Advanced Development Support for Actor-based Programs

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    The actor model is an attractive foundation for developing concurrent applications because actors are isolated concurrent entities that communicate through asynchronous messages and do not share state. Thereby, they avoid concurrency bugs such as data races, but are not immune to concurrency bugs in general. This study taxonomizes concurrency bugs in actor-based programs reported in literature. Furthermore, it analyzes the bugs to identify the patterns causing them as well as their observable behavior. Based on this taxonomy, we further analyze the literature and find that current approaches to static analysis and testing focus on communication deadlocks and message protocol violations. However, they do not provide solutions to identify livelocks and behavioral deadlocks. The insights obtained in this study can be used to improve debugging support for actor-based programs with new debugging techniques to identify the root cause of complex concurrency bugs.Comment: - Submitted for review - Removed section 6 "Research Roadmap for Debuggers", its content was summarized in the Future Work section - Added references for section 1, section 3, section 4.3 and section 5.1 - Updated citation

    Actor based behavioural simulation as an aid for organisational decision making

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    Decision-making is a critical activity for most of the modern organizations to stay competitive in rapidly changing business environment. Effective organisational decision-making requires deep understanding of various organisational aspects such as its goals, structure, business-as-usual operational processes, environment where it operates, and inherent characteristics of the change drivers that may impact the organisation. The size of a modern organisation, its socio-technical characteristics, inherent uncertainty, volatile operating environment, and prohibitively high cost of the incorrect decisions make decision-making a challenging endeavor. While the enterprise modelling and simulation technologies have evolved into a mature discipline for understanding a range of engineering, defense and control systems, their application in organisational decision-making is considerably low. Current organisational decision-making approaches that are prevalent in practice are largely qualitative. Moreover, they mostly rely on human experts who are often aided with the primitive technologies such as spreadsheets and visual diagrams. This thesis argues that the existing modelling and simulation technologies are neither suitable to represent organisation and decision artifacts in a comprehensive and machine-interpretable form nor do they comprehensively address the analysis needs. An approach that advances the modelling abstraction and analysis machinery for organisational decision-making is proposed. In particular, this thesis proposes a domain specific language to represent relevant aspects of an organisation for decision-making, establishes the relevance of a bottom-up simulation technique as a means for analysis, and introduces a method to utilise the proposed modelling abstraction, analysis technique, and analysis machinery in an effective and convenient manner

    Efficient and Deterministic Record & Replay for Actor Languages

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    With the ubiquity of parallel commodity hardware, developers turn to high-level concurrency models such as the actor model to lower the complexity of concurrent software. However, debugging concurrent software is hard, especially for concurrency models with a limited set of supporting tools. Such tools often deal only with the underlying threads and locks, which obscures the view on e.g. actors and messages and thereby introduces additional complexity. To improve on this situation, we present a low-overhead record & replay approach for actor languages. It allows one to debug concurrency issues deterministically based on a previously recorded trace. Our evaluation shows that the average run-time overhead for tracing on benchmarks from the Savina suite is 10% (min. 0%, max. 20%). For Acme-Air, a modern web application, we see a maximum increase of 1% in latency for HTTP requests and about 1.4 MB/s of trace data. These results are a first step towards deterministic replay debugging of actor systems in production
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