A scheduling analysis framework for predicting the weakly hard real-time systems
For real-time systems, hard real-time and soft real-time systems are based on “miss restriction” and “miss tolerance”, respectively. However, a weakly hard real-time system integrates both these requirements. The problem with these systems is the limitation of the scheduling analysis method which on...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/35890/5/HabibahIsmailMFSKSM2013.pdf http://eprints.utm.my/id/eprint/35890/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:70027?site_name=Restricted Repository |
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| Summary: | For real-time systems, hard real-time and soft real-time systems are based on “miss restriction” and “miss tolerance”, respectively. However, a weakly hard real-time system integrates both these requirements. The problem with these systems is the limitation of the scheduling analysis method which only uses the traditional scheduling approach. Besides that, the current framework has problems with the complexity and predictability of the systems. This study proposed a scheduling analysis framework based on the suitability of scheduling algorithms, weakly hard real-time modelling and the combination of the deterministic and probabilistic schedulability analyses for predicting the weakly hard real-time tasks. Initially, the best fitting specification of a weakly hard real-time system was integrated into the proposed framework and tested in the Modeling and Analysis of Real-Time Embedded systems (MARTE) profile. The profile was enhanced because the current MARTE timing constraint restricted to the hard and soft real time timing requirement, thus some modifications were made to model the weakly hard real-time requirements. For complex systems, rather than only using scheduling algorithms to schedule the tasks, the algorithms were used with Unified Modeling Language (UML) modelling. Sequence diagram complexity factor metrics were used to measure the behavioural complexity. The proposed combination approach was applied on case studies and then evaluated with reference to the existing approaches. The results of the evaluations showed that the proposed framework is more predictable compared to the other frameworks and has addressed the problem posed in this research. In conclusion, the proposed scheduling analysis framework provides a less complex design through the behavioural complexity measurements, as well as increases the predictability of the systems. |
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