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we PO ee Comparing Figure 3 with Figure 4 a) shows a close relationship between the approximation of the real-time calculus and the test given by Devi. The real-time calculus approximation is a bit worse than the test given by Devi because of the limited number of curves in_ the approximation of the real-time calculus. As shown in the last section the test by Devi is equal to SuperPos(1) of the superposition approach. The main difference between the test by Devi and the real-time calculus is the specification of bursts which can be only expressed by the real-time calculus. By using event streams it is easy to analyse burst by the superposition test [1]. However, this leads to a higher complexity then the test by Devi because of each element of the burst has to be handled as a seperate element of the event stream.

Figure 3 we PO ee Comparing Figure 3 with Figure 4 a) shows a close relationship between the approximation of the real-time calculus and the test given by Devi. The real-time calculus approximation is a bit worse than the test given by Devi because of the limited number of curves in_ the approximation of the real-time calculus. As shown in the last section the test by Devi is equal to SuperPos(1) of the superposition approach. The main difference between the test by Devi and the real-time calculus is the specification of bursts which can be only expressed by the real-time calculus. By using event streams it is easy to analyse burst by the superposition test [1]. However, this leads to a higher complexity then the test by Devi because of each element of the burst has to be handled as a seperate element of the event stream.

Related Figures (6)

WU Figure 1: Results for different values for SuperPos(x) A higher level of the superpostion test leads to a higher acceptance rate but also to a longer execution time of the test. Figure 1 shows measured acceptance rates using different SuperPos(x). The superposition test is a sufficient test with a selectable error. It can be regarded as a series of
WU Figure 1: Results for different values for SuperPos(x) A higher level of the superpostion test leads to a higher acceptance rate but also to a longer execution time of the test. Figure 1 shows measured acceptance rates using different SuperPos(x). The superposition test is a sufficient test with a selectable error. It can be regarded as a series of
Figure 2: Approximation by Devi for two tasks sufficient tests, which gets better with declining errot
Figure 2: Approximation by Devi for two tasks sufficient tests, which gets better with declining errot
This test has to be preformed only for the first job of each task which has a deadline equal to the deadline of the task. Now consider the test of Devi (Definition 1). It can be expressed as: (V(1 s&s |I|) This test also has to be performed for the deadline of each task. Setting 7 = D, in the above equation results in SuperPos(1). This proves Lemma 2. The relationship between the test by Devi and the superposition approach allows to include the extensions of the test by Devi
This test has to be preformed only for the first job of each task which has a deadline equal to the deadline of the task. Now consider the test of Devi (Definition 1). It can be expressed as: (V(1 s&s |I|) This test also has to be performed for the deadline of each task. Setting 7 = D, in the above equation results in SuperPos(1). This proves Lemma 2. The relationship between the test by Devi and the superposition approach allows to include the extensions of the test by Devi
Figure 3: Approximation by Devi for a single task described in [9] into the super position approach. The extensions concern practical relevant issues like switching time, priority ceiling protocol, self-suspension and limits for the number of priorities.
Figure 3: Approximation by Devi for a single task described in [9] into the super position approach. The extensions concern practical relevant issues like switching time, priority ceiling protocol, self-suspension and limits for the number of priorities.
The prove of this lemma is visualized in Figure 6 and can also be followed using Lemma 4. Therefore if the test fails at I,ex1, the previous test guarantees all intervals which are smaller than J,,,,,, even if they are approximated.
The prove of this lemma is visualized in Figure 6 and can also be followed using Lemma 4. Therefore if the test fails at I,ex1, the previous test guarantees all intervals which are smaller than J,,,,,, even if they are approximated.
Related topics:
Computer ScienceReal Time SystemsScheduling TheoryApproximate AlgorithmProcessor Scheduling
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