Varying task-set parameters

In this first series of experiments, we vary task-set parameters, i.e. the range of the task period and the number of tasks. For each of these experiments, we use the weighted schedulability ratio as metric.

11.3.1 Period range

In the first experiment in this series, we vary the range of the task periods in steps of increasing orders of magnitude. Figure11(middle) shows the weighted schedulability ratio for a varying period range and implicit deadlines, using the composite approach when CRPD is taken into account. Since we generate computation times depending on the task periods, a larger range of the periods results in a larger computation time for some tasks. The performance of FPNS quickly drops, because computation times of tasks with a large period may exceed the periods (and the implicit deadlines) of other tasks in the system. For the same reason, however, we may be unable to assign a pre-emption threshold to tasks with a large period and long computation time other than its regular priority. The performance of FPPS with CRPD therefore approaches the performance of FPTS with CRPD. At the other extreme, when the range of task periods is small, then FPTS with CRPD provides performance close to that of FPTS without CRPD. This is because with a small range of periods and deadlines, the OTA algorithm can set pre-emption thresholds such that most tasks cannot pre-empt each other, thus greatly reducing CRPD. Overall, FPTS provides consistently high performance irrespective of the range of task periods. The performance benefits of the task-layout search remain stable.

Figure11 (top and bottom) also shows the results for constrained and arbitrary deadlines (respectively). The graphs clearly illustrate that the weighted schedulability ratio increases from constrained to arbitrary deadlines for all algorithms. The graphs also illustrate that the performance loss for FPTS due to CRPD gradually decreases from constrained to arbitrary deadlines, whereas the performance loss for FPPS due to CRPD remains roughly the same. As before, we attribute this relative strength of FPTS to its ability toincreasethe worst-case response time of higher priority tasks allowing adecreaseof response times of lower priority tasks. This strength becomes amplified for increasing deadlines.

Figure12shows the results for the various approaches when CRPD is taken into account. Similar to the earlier experiments, the UCB-Union Multiset approach and the composite approach have overlapping lines in the graphs.

11.3.2 Number of tasks

In the second experiment we vary the number of tasks from 2 to 20 in steps of 2. Figure13(middle) shows the results for implicit deadlines. An increasing number of tasks leads to an improved performance of FPTS with CRPD relative to FPPS with CRPD. There are two reasons for this: (i) as the cache utilization remains constant, the ECBs per task decrease and (ii) by increasing the number of tasks, the individual task utilizations and execution times decrease, thus decreasing the potential blocking

Fig. 11 Weighted schedulability ratio for varying period range and constrained (top), implicit (middle) and arbitrary (bottom) deadlines. The composite approach is used when CRPD is taken into account

times. This gives the OTA algorithm more freedom to set pre-emption thresholds such that most tasks cannot pre-empt each other, again greatly reducing CRPD. For a low number of tasks, the task-layout search algorithm has only a minor impact on the performance of FPPS and FPTS. The number of task layouts is limited, and

Fig. 12 Weighted schedulability ratio for varying period range, constrained (top), implicit (middle) and arbitrary (bottom) deadlines. The initial layout is used for the various CRPD approaches

thus also the potential gain. The difference between the initial and the improved layout becomes noticeable at a task-set size of 6, and has its peak at 10 and 12 tasks. Although the task-layout search remains effective in case of large task sets, the performance benefits drop slightly. The larger the task set, the more potential task permutations

Fig. 13 Weighted schedulability ratio for varying number of tasks and constrained (top), implicit (middle) and arbitrary (bottom) deadlines. The composite approach is used when CRPD is taken into account

exist. Consequently, the search algorithm is only able to explore a smaller fraction of the complete search-space making it less likely to find an optimal or near-optimal task layout.

Fig. 14 Weighted schedulability ratio for varying number of tasks, constrained (top), implicit (middle) and arbitrary (bottom) deadlines. The initial layout is used for the various CRPD approaches

Figure13 (top and bottom) also shows the results for constrained and arbitrary deadlines (respectively). The performance of FPTS with CRPD converges to FPTS withoutCRPD for an increasing number of tasks. For arbitrary deadlines, the per- formance of FPTS with CRPD and FPTS without CRPD are almost the same. For

Fig. 15 Weighted schedulability ratio for varying block reload time, constrained (top), implicit (middle) and arbitrary (bottom) deadlines, and the composite approach for CRPD. Thevertical black lineindicates a change in the scale of thex-axis

FPPS, however, a relative performance improvement of FPPS with CRPD compared to FPPS without CRPD is not noticeable from constrained deadlines towards arbitrary deadlines.

Fig. 16 Weighted schedulability ratio for varying block reload time, the initial memory layout, constrained (top), implicit (middle) and arbitrary (bottom) deadlines, and various approaches for CRPD. Thevertical black lineindicates a change in the scale of thex-axis

Figure 14 shows the results for the various CRPD approaches for constrained, implicit, and arbitrary deadlines. Similar to the earlier experiments, the UCB-Union Multiset approach and the composite approach have overlapping lines in the graphs.

For an increasing number of tasks, the performance of the UCB-Only Multiset approach degrades faster than that of the ECB-Only approach. The rationale for this behavior is that as the number of tasks gets larger, so the affected sets tend to become bigger and hence the change that the number of UCBs of the tasks affected by a task

τj is larger than the ECBs of taskτjincreases.