Rigorous implementation of real-time systems – from theory to application

Abstract

The correct and efficient implementation of general real-time applications remains very much an open problem. A key issue is meeting timing constraints whose satisfaction depends on features of the execution platform, in particular its speed. Existing rigorous implementation techniques are applicable to specific classes of systems, for example, with periodic tasks or time-deterministic systems.We present a general model-based implementation method for real-time systems based on the use of two models: -An abstract model representing the behaviour of real-time software as a timed automaton, which describes user-defined platform-independent timing constraints. Its transitions are timeless and correspond to the execution of statements of the real-time software. -A physical model representing the behaviour of the real-time software running on a given platform. It is obtained by assigning execution times to the transitions of the abstract model.A necessary condition for implementability is time-safety, that is, any (timed) execution sequence of the physical model is also an execution sequence of the abstract model. Time-safety simply means that the platform is fast enough to meet the timing requirements. As execution times of actions are not known exactly, time-safety is checked for the worst-case execution times of actions by making an assumption of time-robustness: time-safety is preserved when the speed of the execution platform increases. We show that, as a rule, physical models are not time-robust, and that time-determinism is a sufficient condition for time-robustness. For a given piece of real-time software and an execution platform corresponding to a time-robust model, we define an execution engine that coordinates the execution of the application software so that it meets its timing constraints. Furthermore, in the case of non-robustness, the execution engine can detect violations of time-safety and stop execution. We have implemented the execution engine for BIP programs with real-time constraints and validated the implementation method for two case studies. The experimental results for a module of a robotic application show that the CPU utilisation and the size of the model are reduced compared with existing implementations. The experimental results for an adaptive video encoder also show that a lack of time-robustness may seriously degrade the performance for increasing platform execution speed.Rigorous implementation of real-time systems -from theory to application 883

Early validation of system requirements and design through correctness-by-construction

“…For the enforcement of the property, an Action flow architecture was instantiated using the two components as operands of type B. Table 8 shows the mapping of their ports for actions a [1] and a [2] to port type parameters. Figure 11 presents the result of applying the architecture, which adds the coordinator and two connectors shown with dashed lines.…”

“…HK-04-P3.1: globally, obs(s001) precedes beg(a001) States s001 and s002 belong to the same state-set, hence, they can be enforced through a single Mode management architecture in which k = 2. The style parameters shown in Table 9 associate state s001 with mode [1] and state s002 with mode [2]. The m [1]b and m [2]b port types are mapped to the beg(M) of each pattern, namely the beg(a001) (evaluated as HK PL read.mem write req) and beg(a002).…”

Performance evaluation of stochastic real-time systems with the SBIP framework

“…We recall that BIP has been introduced as a component-based framework where systems are obtained by composition of untimed atomic components with multi-party interactions, and coordinated using dynamic priorities. RT-BIP (Abdellatif et al, 2013) extended BIP with real-time features and has (dense) real-time semantics based on timed automata concepts (Alur and Dill, 1994). S-BIP (Nouri et al, 2015) extended BIP with stochastic features and has (discrete) stochastic semantics based on Markov chains.…”

Performance evaluation of stochastic real-time systems with the SBIP framework

“…We recall that BIP has been introduced as a component-based framework where systems are obtained by composition of untimed atomic components with multi-party interactions, and coordinated using dynamic priorities. RT-BIP (Abdellatif et al, 2013) extended BIP with real-time features and has (dense) real-time semantics based on timed automata concepts (Alur and Dill, 1994). S-BIP (Nouri et al, 2015) extended BIP with stochastic features and has (discrete) stochastic semantics based on Markov chains.…”