A calculus for attribute-based communication

The notion of attribute-based communication seems promising to model and analyse systems with huge numbers of interacting components that dynamically adjust and combine their behaviour to achieve specific goals. A basic process calculus, named AbC, is introduced that has as primitive construct exactly attribute-based communication and its impact on the above mentioned kind of systems is considered. An AbC system consists of a set of parallel components each of which is equipped with a set of attributes. Communication takes place in a broadcast fashion and communication links among components are dynamically established by taking into account interdependences determined by predicates over attributes. First, the syntax and the reduction semantics of AbC are presented, then its expressiveness and effectiveness is demonstrated by modelling two scenarios from the realm of TV streaming channels. An example of how well-established process calculi could be encoded into AbC is given by considering the translation into AbC of a prototypical π-calculus process

Case studies for a new IoT programming paradigm: Fluidware

A number of scientific and technological advancements enabled turning the Internet of Things vision into reality. However, there is still a bottleneck in designing and developing IoT applications and services: each device has to be programmed individually, and services are deployed to specific devices. The Fluidware approach advocates that to truly scale and raise the level of abstraction a novel perspective is needed, focussing on device ensembles and dynamic allocation of resources. In this paper, we motivate the need for such a paradigm shift through three case studies emphasising a mismatch between state of art solutions and desired properties to achieve

CARMA Eclipse plug-in: A tool supporting design and analysis of Collective Adaptive Systems

Collective Adaptive Systems (CAS) are heterogeneous populations of autonomous task-oriented agents that cooperate on common goals forming a collective system. This class of systems is typically composed of a huge number of interacting agents that dynamically adjust and combine their behaviour to achieve specific goals. Existing tools and languages are typically not able to describe the complex interactions that underpin such systems, which operate in a highly dynamic environment. For this reason, recently, new formalisms have been proposed to model CAS. One such is Carma, a process specification language that is equipped with linguistic constructs specifically developed for modelling and programming systems that can operate in open-ended and unpredictable environments. In this paper we present the Carma Eclipse plug-in, a toolset integrated in Eclipse, developed to support the design and analysis of CAS

The AbU Language: IoT Distributed Programming Made Easy

Event-driven programming based on Event-Condition-Action (ECA) rules allows users to define complex automation routines in a simple, declarative way; for this reason, in recent years ECA rules have been adopted by the majority of companies in the Internet of Things (IoT) industry as a promising paradigm for implementing ubiquitous and pervasive systems. Unfortunately, programming simplicity comes to a price: most implementations of ECA rules are bound to a strongly centralized communication infrastructure, that poses serious limitations on the application scenarios for the IoT, due to scalability, security and availability issues. To mitigate these issues, recent works introduced abstractions for communication and coordination of ensembles of IoT devices in a decentralized setting, effectively moving the computation towards the edge of the network without sacrificing the programming simplicity prerogative of ECA rules. In particular, Attribute-based memory Updates is a communication model transparently enhancing ECA rules-based systems with an interaction mechanism where communication is similar to broadcast but actual receivers are selected on the spot, by means of predicates (i.e., properties) over devices attributes. In this paper, we introduce AbU-dsl, a Domain Specific Language for the IoT that compiles on top of an implementation of Attribute-based memory Updates. In this way, AbU-dsl provides a practical development interface, based on ECA rules, to effectively program IoT devices in a fully decentralized setting, by exploiting a full-fledged attribute-based interaction model. Thus, programmers can specify interactions between devices in a declarative way, abstracting from details such as devices identity, number, or even their existence, without the need for a central controlling service

A distributed API for coordinating AbC programs

Collective adaptive systems exhibit a particular notion of interaction where environmental conditions largely influence interactions. Previously, we proposed a calculus, named AbC, to model and reason about CAS. The calculus proved to be effective by naturally modelling essential CAS features. However, the question on the tradeoff between its expressiveness and its efficiency, when implemented to program CAS applications, is to be answered. In this article, we propose an efficient and distributed coordination infrastructure for AbC. We prove its correctness, and we evaluate its performance. The main novelty of our approach is that AbC components are infrastructure agnostic. Thus the code of a component does not specify how messages are routed in the infrastructure but rather what properties a target component must satisfy. We also developed a Go API, named GoAt, and an Eclipse plugin to program in a high-level syntax which can be automatically used to generate matching Go code. We showcase our development through a non-trivial case study

Fluid approximation of broadcasting systems

Nature-inspired paradigms have been proposed to design and forecast behaviour of open distributed systems, such as sensor networks and the internet of things. In these paradigms system behaviour emerges from (complex) interactions among a large number of agents. Modelling these interactions in terms of classical point-to-point communication is often not practical. This is due to the large scale and the open nature of the systems, which means that partners for point-to-point communication may not be available at any given time. Nevertheless the need for efficient formal verification of qualitative and quantitative properties of these systems is of utmost importance, especially given their proposed pervasive and transparent nature. CARMA is a recently proposed formal modelling language for open distributed systems, which is equipped with a broadcast communication in order to meet the communication challenges of such systems. The inclusion of quantitative information about the timing and probability of actions gives rise to models suitable for analysing questions such as the probability that information will achieve total coverage within a system, or the expected market share that might be gained by competing service providers relying on viral advertising. The ability to express models is not the only challenge, because the scale of the systems we are interested in often defies discrete state-based analysis techniques such as stochastic simulation. This is the problem that we address in this paper as we consider how to provide an efficient fluid approximation, supporting efficient and accurate quantitative analysis of large scale systems, for a language that incorporates broadcast communication

On Expressiveness and Behavioural Theory of Attribute-based Communication

Attribute-based communication is an interesting alternative to broadcast and binary communication when providing abstract models for the so called Collective Adaptive Systems which consist of a large number of interacting components that dynamically adjust and combine their behavior to achieve specifc goals. A basic process calculus, named AbC, is introduced whose primary primitive for interaction is attribute-based communication. An AbC system consists of a set of parallel components each of which is equipped with a set of attributes. Communication takes place in an implicit multicast fashion, and interactions among components are dynamically established by taking into account\connections" as determined by predicates over the attributes exposed by components. First, the syntax and the semantics of AbC are presented, then expressiveness and effectiveness of the calculus are demonstrated both in terms of the ability to model scenarios featuring collaboration, reconfiguration, and adaptation and of the possibility of encoding a process calculus for broadcasting channel-based communication and other communication paradigms. Behavioral equivalences for AbC are introduced for establishing formal relationships between different descriptions of the same system

Fluid approximation of broadcasting systems

Nature-inspired paradigms have been proposed to design and forecast behaviour of open distributed systems, such as sensor networks and the internet of things. In these paradigms system behaviour emerges from (complex) interactions among a large number of agents. Modelling these interactions in terms of classical point-to-point communication is often not practical. This is due to the large scale and the open nature of the systems, which means that partners for point-to-point communication may not be available at any given time. Nevertheless the need for efficient formal verification of qualitative and quantitative properties of these systems is of utmost importance, especially given their proposed pervasive and transparent nature. CARMA is a recently proposed formal modelling language for open distributed systems, which is equipped with a broadcast communication in order to meet the communication challenges of such systems. The inclusion of quantitative information about the timing and probability of actions gives rise to models suitable for analysing questions such as the probability that information will achieve total coverage within a system, or the expected market share that might be gained by competing service providers relying on viral advertising. The ability to express models is not the only challenge, because the scale of the systems we are interested in often defies discrete state-based analysis techniques such as stochastic simulation. This is the problem that we address in this paper as we consider how to provide an efficient fluid approximation, supporting efficient and accurate quantitative analysis of large scale systems, for a language that incorporates broadcast communication

Programming the Interactions of Collective Adaptive Systems by Relying on Attribute-based Communication

Collective adaptive systems are new emerging computational systems consisting of a large number of interacting components and featuring complex behaviour. These systems are usually distributed, heterogeneous, decentralised and interdependent, and are operating in dynamic and possibly unpredictable environments. Finding ways to understand and design these systems and, most of all, to model the interactions of their components, is a difficult but important endeavour. In this article we propose a language-based approach for programming the interactions of collective-adaptive systems by relying on attribute-based communication; a paradigm that permits a group of partners to communicate by considering their run-time properties and capabilities. We introduce AbC, a foundational calculus for attribute-based communication and show how its linguistic primitives can be used to program a complex and sophisticated variant of the well-known problem of Stable Allocation in Content Delivery Networks. Also other interesting case studies, from the realm of collective-adaptive systems, are considered. We also illustrate the expressive power of attribute-based communication by showing the natural encoding of other existing communication paradigms into AbC