Coordination Models and Languages

Abstract

A new class of models, formalisms and mechanisms has recently evolved for describing concurrent and distributed computations based on the concept of "coordination". The purpose of a coordination model and associated language is to provide a means of integrating a number of possibly heterogeneous components together, by interfacing with each component in such a way that the collective set forms a single application that can execute on and take advantage of parallel and distributed systems. In this chapter we initially define and present in sufficient detail the fundamental concepts of what constitutes a coordination model or language. We then go on to classify these models and languages as either "data-driven" or "control-driven" (also called "process-" or "task-oriented"). Next, the main existing coordination models and languages are described in sufficient detail to let the reader appreciate their features and put them into perspective with respect to each other. The chapter ends with a discussion comparing the various models and some conclusions.

Tool Supported Analysis of IoT

“…The language We review IoT-LySa [9,8], a specification language recently proposed for designing IoT systems. IoT-LySa is based on a process calculus and it has a specific focus on data collecting, aggregation, and communication.…”

“…This filtering input enables us to explicitly implement external choices, through the switch primitive. It was not originally included in [9,8] although the construct can be encoded there.…”

“…More precisely, the first branch is executed when the node receives a message with guard temp (line 7). The node checks if the temperature x is below or above a given threshold, instructs the actuator, and sends an acknowledgement accordingly (lines [8][9][10][11][12][13][14]. The second case is when the node receives the message with guard quit (line 17): the process sends an acknowledgement and terminates.…”

“…The control flow analysis A control flow analysis (CFA) that safely approximates the behaviour of IoT-LySa systems has been given in [9]. The analysis tracks how sensor values are used inside their local nodes, how they are aggregated and propagated to other nodes, and which messages are exchanged among nodes.…”

“…This may prevent us from statically determining safe approximants of aggregated data, because we cannot establish how many applications of aggregation functions are necessary by only inspecting the code. The analysis of [10] resorts therefore to tree grammars for keeping finite the abstract values and for improving the precision of the analysis in [9], where instead trees are cut at a fixed depth.…”