On the acceptance tests of aperiodic real-time tasks for FPGAs

Partially runtime-reconfigurable devices allow tasks to be placed and removed dynamically at runtime. For real-time systems, tasks have to complete their work and also to meet their deadlines. It is important to decide at arrival time whether the real-time task could meet its deadline or not. Acceptance tests are concerned with determining whether the incoming task can meet its deadline or not. The utilization bound-based acceptance tests (UBTs) -that accept new tasks till certain utilization limit- were proposed to handle single processor with aperiodic tasks which do not apply to the reconfigurable environment. The rejection ratio was used as a measure for performance of acceptance tests when all the rejected tasks have the same failure cost. However, when acceptance tests are used, the tasks that are not accepted to run are diverted to other system resources and not actually rejected. In this paper, we modify the utilization bound acceptance test to cope with the reconfigurable platform. Although this test requires simple calculations, it may reject tasks that could have been accepted if they wait in the system. Then, we present an exact acceptance test (EAT) for real-time non-preemptive tasks. This test decides exactly whether the incoming task can meet its deadline or not, at its arrival time. The test depends on a look- ahead placement (LAP) strategy. Finally, we propose a new factor, acceptance ratio of workload (AROW), to deal with systems that deploy acceptance tests. The AROW is suitable to measure the performance of acceptance tests as it takes into account sizes and computation times of accepted and diverted tasks. The increase in this ratio means an increase in the work done by the accepted tasks and vice versa. We compare the LAP strategy to the UBT and show its performance regarding the diversion ratio and the AROW measure. Our results show that the LAP strategy outperforms the UBT technique by over 80% using the AROW measure and also enhances the diversion ratio by around 40%.