The York Research Database

Mixed-criticality scheduling theory (MCSh) was developed to allow for more resource-efficient implementation of systems comprising different components that need to have their correctness validated at different levels of assurance. MCSh is primarily concerned with the pre-runtime verification of such systems; hence many mixed-criticality scheduling algorithms tend to exhibit poor survivability characteristics during run-time. (e.g., MCSh allows for less-important (“lo-criticality”) workloads to be completely discarded in the event that run-time behavior is not compliant with the assumptions under which the correctness of the lo-criticality workload should be verified.) We propose extensions to MCSh to make it cognizant of survivability considerations, by defining quantitative metrics for the robustness and resilience of mixed-criticality scheduling algorithms. Such metrics allow us to make quantitative assertions regarding the survivability characteristics of mixed-criticality scheduling algorithms, and to compare different algorithms from the perspective of their survivability.