Deadline Assignment in Distributed Hard Real-Time Systems with Relaxed Locality Constraints
Paper i proceeding, 1997
In a real-time system, tasks are constrained by global
end-to-end deadlines. In order to cater for high task
schedulability, these deadlines must be distributed over
component subtasks in an intelligent way. Existing methods
for automatic distribution of end-to-end deadlines are all
based on the assumption that task assignments are entirely
known beforehand. This assumption is not necessarily valid
for large real-time systems. Furthermore, most task assignment
strategies require information on deadlines in order to make
good assignments, thus forming a circular dependency
between deadline distribution and task assignment.
We present a heuristic approach that performs deadline distribution
prior to task assignment.
The deadline distribution problem is presented in the context of large
distributed hard real-time systems with relaxed locality
constraints, where schedulability analysis must be performed
off-line, and only a subset of the tasks are constrained by
predetermined assignments to specific processors. Using
experimental results we identify drawbacks of
previously-proposed techniques, and then show that our solution
provides significantly better performance for a large
variety of system configurations.