Realtime scheduler

As an approach to my thesis subject, we chose to focus on a particular widely used system component : the scheduler. The initial context of application is CamilleRT, an real time exo-kernel for embedded devices, and the problem is the following : given a set of periodic tasks and a scheduler, how can we ensure the good behavior of the scheduler? (an example of a bad behavior would be that the scheduler gives all the CPU time to a particular task). This problem was studied in collaboration with Damien Deville

Analysis of the problem

Since all the tasks are periodic and form a stable set (we consider that bringing in a new task breaks the warranty, and thus requires a new proof of good behavior), we can test the scheduler against a period of the set (the hyperperiod of all the tasks), and then decide wether the scheduling plan is acceptable or not (that is, is every deadline met?). From there, the problem is reduced to a determinism property : if the behavior is identical on each hyperperiod and the local behavior is acceptable, then the whole scheduling plan is acceptable. On the contrary, if the schedule() function is able to cheat (for example, detecting wether it is being tested or not) we have no warranty that the plan is correct.

If the schedule() function is a pure one (in the mathematical sense), the determinism is trivial with the same entries, we obtain the same result. But to write efficient scheduling functions, it is often necessary to keep an internal state, such as the task progression, for the computation. Such an internal state can be trivially used for cheating.

Proposed solution

I proposed [dhr-ijca-05] an algorithm that enables us to detect the potentiality of cheating. The idea is to let the algorithm cheat, but to make this cheating useless. A solution for this is to periodically reset every internal state. Doing this, we can be sure that the algorithm will always behave the same.

The principle of the algorithm is to follow the references (objects) that appear in a method and infer a signature of the method. That signature will say us for example that the second argument has been linked into the return value, or to this object. The algorithm is incremental, with some restrictions due to the dynamic binding of methods.

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