Future real time applications are often closely integrated with the surrounding IT infrastructure and often they have to interact with external services that have CORBA interfaces. In such a scenario, static run time prediction for scheduling and static guarantees of timing assertions is impossible since many nonpredictable interaction elements influence execution times. To include CORBA objects in the real time system, the system must be aware of their timing behavior. Monitoring run time behavior is essential for future real time applications that exhibit dynamic behavior since it can be used to dynamically generate estimates of execution times and remaining execution times. However, the accuracy of the generated estimates is crucial for the success of such an approach. Achieving sufficient accuracy is difficult if the variance of the monitored execution times is high. One main source of high variances in execution times are data dependencies in the program's code. We present an object oriented, adaptable approach to monitoring execution times in object oriented systems which reduces the influence of data dependencies. It includes monitoring and mathematical analysis concepts to continuously predict remaining execution times in order to detect executions that will exceed their timing estimate. Furthermore, it allows us to dynamically adjust the monitoring granularity and the depth of the mathematical analysis on a per object base, in order to achieve higher accuracy if necessary, thus trading monitoring overhead against accuracy.
展开▼
