Abstract
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.
Highlights
Emerging application scenarios, such as the Internet of Things (IoT), cyberphysical systems (CPSs), and edge computing, call for software design approaches addressing openness, heterogeneity, self-adaptation, and deployment agnosticism [19]
The proposed model enriches the coordination abstraction of field-based coordination with the possibility to explicitly and possibly reactively program the scheduling of the coordination actions; second, it enables a functional description of causality and observability, since manipulation of the interaction frequency among single components of the coordinated system reflects in changes in how causal events are perceived, and actions are taken in response to event triggers; third, the most immediate practical implication of a time-fluid coordination when compared to a traditional time-driven approach is improved efficiency, intended as improved responsiveness with the same resource cost
Such adaptive sampling approaches challenge the underlying notion of time, but they tend to focus on the temporal dimension only
Summary
Emerging application scenarios, such as the Internet of Things (IoT), cyberphysical systems (CPSs), and edge computing, call for software design approaches addressing openness, heterogeneity, self-adaptation, and deployment agnosticism [19]. Similar considerations apply for example to the area of landslide monitoring [28], where long intervals of immobility are interspersed by sudden slope movements: sensors sampling rate can and should be low most of the time, but it needs to get promptly increased on slope changes This generally suggests a key unexpressed potential for field-based computation: the general ability to provide improved balance between performance (system reactivity) and cost (usage of computational resources). The proposed model enriches the coordination abstraction of field-based coordination with the possibility to explicitly and possibly reactively program the scheduling of the coordination actions; second, it enables a functional description of causality and observability, since manipulation of the interaction frequency among single components of the coordinated system reflects in changes in how causal events are perceived, and actions are taken in response to event triggers; third, the most immediate practical implication of a time-fluid coordination when compared to a traditional time-driven approach is improved efficiency, intended as improved responsiveness with the same resource cost. The remainder of this work is as follows: Sect. 2 frames this work with respect to the existing literature on topic; Sect. 3 introduces the proposed time-fluid model and discusses its implications; Sect. 4 presents a prototype implementation in the framework of aggregate computing, showing examples and evaluating the potential practical implications via simulation Sect. 5 discusses future directions and concludes the work
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