Towards Real-Time Aggregate Computing

Developing large-scale collective adaptive systems for safety-critical applications requires an extensive effort, involving the interplay of distributed programming techniques and mathematical proofs of real-time guarantees. This effort could be significantly reduced by allowing the system developer to rely on libraries of predefined algorithms. By exploiting such algorithms, distributed behaviour and (hard) real-time guarantees for the final application could be automatically inferred, effectively shifting the verification burden from the system designer to the algorithm developer. Following earlier work on real-time guarantees for aggregate computing algorithms, we argue that aggregate computing could provide a convenient framework towards this aim. As a first step, we give a detailed description of different kinds of models that abstract aggregate programs as mathematical functions. Then, building on such models, we start investigating the problem of how real-time behavior constraints could be specified in a compositional way. Finally, we conclude by singling out a number of potential building block library algorithms that could constitute such a real-time aggregate computing library, with the potential of providing a valuable asset for supporting the rigorous engineering of safety-critical large-scale collective adaptive systems.