Thunderstriking constraints with JUPITER

Abstract

We present JUPITER, a tool for analysing multiconstrained systems. JUPITER was built to explore three basic ideas. First, how to use controller synthesis so as to find the exact conditions under which a particular constraint will be satisfied. Second, how to successively refine the models used for the controller synthesis so as to obtain a series of more easily understandable and more robust controllers. Last but not least, how to structure & explain the synthesised controllers and provide hints to designers for further optimisations through the use of Machine Learning techniques.Thus, JUPITER can help in the design and analysis of multi-constraint systems through the automatic synthesis of control logic for certain of the constraints and the aid it provides to designers for discovering further optimisations.The controllers it synthesises can be easily implemented on top of a standard real-time OS [7,6].

Better abstractions for reusable components & architectures

“…Some of the protocol strategies employed by components will be trivial ones, when a protocol has no choices, like the server of the request-response connector of Figure 2. In other cases these Figure 3: Two versions of the Dining Philosopher strategies can be quite complicated, e.g., in real-time systems [16,13]. There a connector describes the protocol used by components to request resources with specific deadlines from the platform and synchronise with each other.…”

“…Component strategies themselves may be structured [9], e.g., as a stack, in order to specify generic strategies first and then specialised ones. Thus, a designer can define a strategy to guard against deadlocks, another one for deadlines and so on, as in [16,13]. This will make the strategies easier to understand/optimise/validate and also make the system easier to analyse and more robust.…”