Time-fluid field-based coordination

Emerging application scenarios, such as cyber-physical systems (CPSs), the Internet of Things (IoT), and edge computing, call for coordination approaches addressing openness, self-adaptation, heterogeneity, and deployment agnosticism. Field-based coordination is one such approach, promoting the idea of programming system coordination declaratively from a global perspective, in terms of functional manipulation and evolution in \u201cspace and time\u201d of distributed data structures, called fields. More specifically, regarding time, in field-based coordination it is assumed that local activities in each device, called computational rounds, are regulated by a fixed clock, typically, a fair and unsynchronized distributed scheduler. In this work, we challenge this assumption, and propose an alternative approach where the round execution scheduling is naturally programmed along with the usual coordination specification, namely, in terms of a field of causal relations dictating what is the notion of causality (why and when a round has to be locally scheduled) and how it should change across time and space. This abstraction over the traditional view on global time allows us to express what we call \u201ctime-fluid\u201d coordination, where causality can be finely tuned to select the event triggers to react to, up to to achieve improved balance between performance (system reactivity) and cost (usage of computational resources). We propose an implementation in the aggregate computing framework, and evaluate via simulation on a case study

Time-fluid field-based coordination

Emerging application scenarios, such as cyber-physical systems (CPSs), the Internet of Things (IoT), and edge computing, call for coordination approaches addressing openness, self-adaptation, heterogeneity, and deployment agnosticism. Field-based coordination is one such approach, promoting the idea of programming system coordination declaratively from a global perspective, in terms of functional manipulation and evolution in “space and time” of distributed data structures, called fields. More specifically, regarding time, in field-based coordination it is assumed that local activities in each device, called computational rounds, are regulated by a fixed clock, typically, a fair and unsynchronized distributed scheduler. In this work, we challenge this assumption, and propose an alternative approach where the round execution scheduling is naturally programmed along with the usual coordination specification, namely, in terms of a field of causal relations dictating what is the notion of causality (why and when a round has to be locally scheduled) and how it should change across time and space. This abstraction over the traditional view on global time allows us to express what we call “time-fluid” coordination, where causality can be finely tuned to select the event triggers to react to, up to to achieve improved balance between performance (system reactivity) and cost (usage of computational resources). We propose an implementation in the aggregate computing framework, and evaluate via simulation on a case study

Space-Fluid Adaptive Sampling: A Field-Based, Self-organising Approach

A recurrent task in coordinated systems is managing (estimating, predicting, or controlling) signals that vary in space, such as distributed sensed data or computation outcomes. Especially in large-scale settings, the problem can be addressed through decentralised and situated computing systems: nodes can locally sense, process, and act upon signals, and coordinate with neighbours to implement collective strategies. Accordingly, in this work we devise distributed coordination strategies for the estimation of a spatial phenomenon through collaborative adaptive sampling. Our design is based on the idea of dynamically partitioning space into regions that compete and grow/shrink to provide accurate aggregate sampling. Such regions hence define a sort of virtualised space that is “fluid”, since its structure adapts in response to pressure forces exerted by the underlying phenomenon. We provide an adaptive sampling algorithm in the field-based coordination framework. Finally, we verify by simulation that the proposed algorithm effectively carries out a spatially adaptive sampling

A Methodology and Simulation-Based Toolchain for Estimating Deployment Performance of Smart Collective Services at the Edge

Research trends are pushing artificial intelligence (AI) across the Internet of Things (IoT)-edge-fog-cloud continuum to enable effective data analytics, decision making, as well as the efficient use of resources for QoS targets. Approaches for collective adaptive systems (CASs) engineering, such as aggregate computing, provide declarative programming models and tools for dealing with the uncertainty and the complexity that may arise from scale, heterogeneity, and dynamicity. Crucially, aggregate computing architecture allows for 'pulverization': applications can be decomposed into many deployable micromodules that can be spread across the ICT infrastructure, thus allowing multiple potential deployment configurations for the same application logic. This article studies the deployment architecture of aggregate-based edge services and its implications in terms of performance and cost. The goal is to provide methodological guidelines and a model-based toolchain for the generation and simulation-based evaluation of potential deployments. First, we address this subject methodologically by proposing an approach based on deployment code generators and a simulation phase whose obtained solutions are assessed with respect to their performance and costs. We then tailor this approach to aggregate computing applications deployed onto an IoT-edge-fog-cloud infrastructure, and we develop a corresponding toolchain based on Protelis and EdgeCloudSim. Finally, we evaluate the approach and tools through a case study of edge multimedia streaming, where the edge ecosystem exhibits intelligence by self-organizing into clusters to promote load balancing in large-scale dynamic settings

Resilient distributed collection through information speed thresholds

Part 6: Large-Scale Decentralised SystemsInternational audienceOne of the key coordination problems in physically-deployed distributed systems, such as mobile robots, wireless sensor networks, and IoT systems in general, is to provide notions of “distributed sensing” achieved by the strict, continuous cooperation and interaction among individual devices. An archetypal operation of distributed sensing is data summarisation over a region of space, by which several higher-level problems can be addressed: counting items, measuring space, averaging environmental values, and so on. A typical coordination strategy to perform data summarisation in a peer-to-peer scenario, where devices can communicate only with a neighbourhood, is to progressively accumulate information towards one or more collector devices, though this typically exhibits problems of reactivity and fragility, especially in scenarios featuring high mobility. In this paper, we propose coordination strategies for data summarisation involving both idempotent and arithmetic aggregation operators, with the idea of controlling the minimum information propagation speed, so as to improve the reactivity to input changes. Given suitable assumptions on the network model, and under the restriction of no data loss, these algorithms achieve optimal reactivity. By empirical evaluation via simulation, accounting for various sources of volatility, and comparing to other existing implementations of data summarisation algorithms, we show that our algorithms are able to retain adequate accuracy even in high-variability scenarios where all other algorithms are significantly diverging from correct estimations

ScaFi-Web: A Web-Based Application for Field-Based Coordination Programming

Field-based coordination is a model for expressing the coordination logic of large-scale adaptive systems, composing functional blocks from a global perspective. As for any coordination model, a proper toolchain must be developed to support its adoption across all development phases. Under this point of view, the ScaFi toolkit provides a coordination language (field calculus) as a DSL internal in the Scala language, a library of reusable building blocks, and an infrastructure for simulation of distributed deployments. In this work, we enrich such a toolchain by introducing ScaFi-Web, a web-based application allowing in-browser editing, execution, and visualisation of ScaFi programs. ScaFi-Web facilitates access to the ScaFi coordination technology by flattening the learning curve and simplifying configuration and requirements, thus promoting agile prototyping of field-based coordination specifications. In turn, this opens the door to easier demonstrations and experimentation, and also constitutes a stepping stone towards monitoring and control of simulated/deployed systems

A Simulation Framework for Pervasive Services Ecosystems

This paper grounds on the SAPERE project (Self-Aware PERvasive Service Ecosystems), which aims at proposing a multi-agent framework for pervasive computing, based on the idea of making each agent (service, device, human) manifest its existence in the ecosystem by a Live Semantic Annotation (LSA), and of coordinating agent activities by a small and fixed set of so-called eco-laws--sort of chemical-like reactions over patterns of LSAs. System dynamics in SAPERE is complex because of opennes and due to the self-* requirements imposed by the pervasive computing setting: a simulation framework is hence needed for what-if analysis prior to deployment. In this paper we present a prototype simulator we are developing, tested on a crowd steering scenario. Due to the role of chemical-like dynamics, this is based on a variation of an existing SSA (Stochastic Simulation Algorithm), suitable tailored to the specific features of SAPERE, including dynamicity of network topology, pattern-based application of eco-laws, and temporal triggers

A Chemical Inspired Simulation Framework for Pervasive Services Ecosystems

This paper grounds on the SAPERE project (Self-Aware PERvasive Service Ecosystems), which aims at proposing a multi-agent framework for pervasive computing, based on the idea of making each agent (service, device, human) manifest its existence in the ecosystem by a Live Semantic Annotation (LSA), and of coordinating agent activities by a small and fixed set of so-called eco-laws - sort of chemical-like reactions over patterns of LSAs. System dynamics in SAPERE is complex because of opennes and due to the self-* requirements imposed by the pervasive computing setting: a simulation framework is hence needed for what-if analysis prior to deployment. In this paper we present a prototype simulator we are developing. Due to the role of chemical-like dynamics, this is based on a variation of an existing SSA (Stochastic Simulation Algorithm), suitable tailored to the specific features of SAPERE, including dynamicity of network topology, pattern-based application of eco-laws, and temporal triggers. The simulator is tested on a crowd steering scenario where the navigation of groups is guided towards the preferential destination by showing the direction on public or private screens