ScaFi: A Scala DSL and Toolkit for Aggregate Programming

Supported by current socio-scientific trends, programming the global behaviour of whole computational collectives makes for great opportunities, but also significant challenges. Recently, aggregate computing has emerged as a prominent paradigm for so-called collective adaptive systems programming. To shorten the gap between such research endeavours and mainstream software development and engineering, we present ScaFi, a Scala toolkit providing an internal domain-specific language, libraries, a simulation environment, and runtime support for practical aggregate computing systems development

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

Self-stabilising Priority-Based Multi-Leader Election and Network Partitioning

A common task in situated distributed systems is the self-organising election of leaders. These leaders can be devices or software agents appointed, for instance, to coordinate the activities of other agents or processes. In this work, we focus on the multi-leader election problem in networks of asynchronous message-passing devices, which are a common model in self-organisation approaches like aggregate computing. Specifically, we introduce a novel algorithm for space- and priority-based leader election and compare it with the state of the art. We call the algorithm Bounded Election since it leverages bounding (i.e. minimisation or maximisation) of candidacy messages to drop or promote candidate leaders and ensure stabilisation. The proposed algorithm is formally proven to be self-stabilising, allows for leader prioritisation, and performs on-the-fly network partitioning (namely, as a side effect of the leader election process, the areas regulated by the leaders are also established). Also, we experimentally compare its performance together with the state of the art of leader election in aggregate computing in a variety of synthetic scenarios, showing benefits in terms of convergence time and resilience

A Collective Adaptive Approach to Decentralised k-Coverage in Multi-robot Systems

We focus on the online multi-object k-coverage problem (OMOkC), where mobile robots are required to sense a mobile target from k diverse points of view, coordinating themselves in a scalable and possibly decentralised way. There is active research on OMOkC, particularly in the design of decentralised algorithms for solving it. We propose a new take on the issue: Rather than classically developing new algorithms, we apply a macro-level paradigm, called aggregate computing, specifically designed to directly program the global behaviour of a whole ensemble of devices at once. To understand the potential of the application of aggregate computing to OMOkC, we extend the Alchemist simulator (supporting aggregate computing natively) with a novel toolchain component supporting the simulation of mobile robots. This way, we build a software engineering toolchain comprising language and simulation tooling for addressing OMOkC. Finally, we exercise our approach and related toolchain by introducing new algorithms for OMOkC; we show that they can be expressed concisely, reuse existing software components and perform better than the current state-of-the-art in terms of coverage over time and number of objects covered overall

Addressing Collective Computations Efficiency: Towards a Platform-level Reinforcement Learning Approach

Aggregate Computing is a macro-level approach for programming collective intelligence and self-organisation in distributed systems. In this paradigm, system behaviour unfolds as a combination of a system-wide program, functionally manipulating distributed data structures called computational fields, and a distributed protocol where devices work at asynchronous rounds comprising sense-compute-interact steps. Interestingly, there exists a large amount of flexibility in how aggregate systems could actually execute while preserving the desired functionality. The ideal place for making choices about execution is the aggregate computing platform (or middleware), which can be engineered with the goal of promoting efficiency and other non-functional goals. In this work, we explore the possibility of applying Reinforcement Learning at the platform level in order to optimise aspects of a collective computation while achieving coherent functional goals. This idea is substantiated through synthetic experiments of data propagation and collection, where we show how Q-Learning could reduce the power consumption of aggregate computations

A General Approach to Derive Uncontrolled Reversible Semantics

Reversible computing is a paradigm where programs can execute backward as well as in the usual forward direction. Reversible computing is attracting interest due to its applications in areas as different as biochemical modelling, simulation, robotics and debugging, among others. In concurrent systems the main notion of reversible computing is called causal-consistent reversibility, and it allows one to undo an action if and only if its consequences, if any, have already been undone. This paper presents a general and automatic technique to define a causal-consistent reversible extension for given forward models. We support models defined using a reduction semantics in a specific format and consider a causality relation based on resources consumed and produced. The considered format is general enough to fit many formalisms studied in the literature on causal-consistent reversibility, notably Higher-Order ?-calculus and Core Erlang, an intermediate language in the Erlang compilation. Reversible extensions of these models in the literature are ad hoc, while we build them using the same general technique. This also allows us to show in a uniform way that a number of relevant properties, causal-consistency in particular, hold in the reversible extensions we build. Our technique also allows us to go beyond the reversible models in the literature: we cover a larger fragment of Core Erlang, including remote error handling based on links, which has never been considered in the reversibility literature

A general approach to derive uncontrolled reversible semantics

Reversible computing is a paradigm where programs can execute backward as well as in the usual forward direction. Reversible computing is attracting interest due to its applications in areas as different as biochemical modelling, simulation, robotics and debugging, among others. In concurrent systems the main notion of reversible computing is called causal-consistent reversibility, and it allows one to undo an action if and only if its consequences, if any, have already been undone. This paper presents a general and automatic technique to define a causal-consistent reversible extension for given forward models. We support models defined using a reduction semantics in a specific format and consider a causality relation based on resources consumed and produced. The considered format is general enough to fit many formalisms studied in the literature on causal-consistent reversibility, notably Higher-Order π-calculus and Core Erlang, an intermediate language in the Erlang compilation. Reversible extensions of these models in the literature are ad hoc, while we build them using the same general technique. This also allows us to show in a uniform way that a number of relevant properties, causal-consistency in particular, hold in the reversible extensions we build. Our technique also allows us to go beyond the reversible models in the literature: we cover a larger fragment of Core Erlang, including remote error handling based on links, which has never been considered in the reversibility literature

Towards Races in Linear Logic

Part 1: Computational ModelsInternational audienceProcess calculi based in logic, such as $\pi$DILL and CP, provide a foundation for deadlock-free concurrent programming, but exclude non-determinism and races. HCP is a reformulation of CP which addresses a fundamental shortcoming: the fundamental operator for parallel composition from the $\pi$-calculus does not correspond to any rule of linear logic, and therefore not to any term construct in CP. We introduce HCP${-} _{\text {ND}}$, which extends HCP with a novel account of non-determinism. Our approach draws on bounded linear logic to provide a strongly-typed account of standard process calculus expressions of non-determinism. We show that our extension is expressive enough to capture many uses of non-determinism in untyped calculi, such as non-deterministic choice, while preserving HCP ’s meta-theoretic properties, including deadlock freedom