Modern computing systems are based on multi-processor systems, i.e. multiple cores on the same chip. Hard real-time systems are required to perform particular tasks within certain amount of time; failure to do so characterises an unaccepted behavior. Hard real-time systems are found in safety-critical applications, e.g. airbag control software, flight control software, etc. In safety-critical applications, failure to meet the real-time constraints can have catastrophic effects. The safe and, at the same time, efficient deployment of applications, with hard real-time constraints, on multi-processors is a challenging task. Scheduling methods and Models of Computation, that provide safe deployments, require a realistic estimation of the Worst-Case Execution Time (WCET) of tasks. The simultaneous access of shared resources by parallel tasks, causes interference delays due to hardware arbitration. Interference delays can be accounted for, with the pessimistic assumption that all possible interference can happen. The resulting schedules would be exceedingly conservative, thus the benefits of multi-processor would be significantly negated. Producing less pessimistic schedules is challenging due to the inter-dependency between WCET estimation and deployment optimisation. Accurate estimation of interference delays -and thus estimation of task WCET- depends on the way an application is deployed; deployment is an optimisation problem that depends on the estimation of task WCET. Another efficiency gap, which is of consequence in several systems (e.g. airbag control), stems from the fact that rarely tasks execute with their WCET. Safe runtime adaptation based on the Actual Execution Times, can yield additional improvements in terms of latency (more responsive systems). To achieve efficiency and retain adaptability, we propose that interference analysis should be coupled with the deployment process. The proposed interference analysis method estimates the possible amount of interference, based on an architecture and an application model. As more information is provided, such as scheduling, memory mapping, etc, the per-task interference estimation becomes more accurate. Thus, the method computes interference-sensitive WCET estimations (isWCET). Based on the isWCET method, we propose a method to break the inter-dependency between WCET estimation and deployment optimisation. Initially, the isWCETs are over-approximated, by assuming worst-case interference, and a safe deployment is derived. Subsequently, the proposed method computes accurate isWCETs by spatio-temporal exclusion, i.e. excluding interferences from non-overlapping tasks that share resources (space). Based on accurate isWCETs, the deployment solution is improved to provide better latency guarantees. We also propose a distributed runtime adaptation technique, that aims to improve run-time latency. Using isWCET estimations restricts the possible adaptations, as an adaptation might increase the interference and violate the safety guarantees. The proposed technique introduces statically scheduling dependencies between tasks that prevent additional interference. At runtime, a self-timed scheduling policy that respects these dependencies, is applied, proven to be safe, and with minimal overhead. Experimental evaluation on Kalray MPPA-256 shows that our methods improve isWCET up to 36%, guaranteed latency up to 46%, runtime performance up to 42%, with a consolidated performance gain of 50%.
EPFL2017
