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Title: Techniques for scheduling time-triggered resource-constrained embedded systems
Author: Gendy, Ayman Khalifa Ghaly
ISNI:       0000 0004 2686 9951
Awarding Body: University of Leicester
Current Institution: University of Leicester
Date of Award: 2009
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It is often argued that time-triggered (TT) architectures are the most suitable basis for safety-related applications as their use tends to result in highly-predictable system behaviour. This predictability is increased when TT architectures are coupled with the use of co-operative (or "non pre-emptive") task sets. Despite many attractive properties, such "time-triggered co-operative" (TTC) and related "time-triggered hybrid" (TTH) architectures rarely receive much attention in the research literature. One important reason for this is that these designs are seen to be "fragile": that is, small changes to the task set may require revisions to the whole schedule. Such revisions are seen as challenging and time consuming. To tackle this problem two novel algorithms (TTSA1 and TTSA2), which help to automate the process of scheduler selection and configuration, are introduced. While searching for a workable schedule, both the algorithms try to ensure that all task constraints are met, a co-operative scheduler is used whenever possible and the power consumption is kept as low as possible. The effectiveness of these algorithms is tested by means of empirical trials. Both TTSA1 and TTSA2, like most of scheduling algorithms introduced in the literature, rely on knowledge of task worst-case execution time (WCET). Unfortunately, determining the WCET of tasks is rarely straightforward. Even in situations where accurate WCET estimates are available at design time, variations in task execution time, between its best-case execution time (BCET) and its WCET, may still affect the system predictability and/or violate task constraints. In an effort to address this problem, a set of code-balancing techniques is introduced. Using an empirical study it is demonstrated that these techniques help in reducing the variations in task execution time, and hence increase the system predictability. These goals are achieved with a reduced power-consumption overhead, compared to alternative solutions.
Supervisor: Pont, M. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available