Jie Zou
ABSTRACT
In an autonomous system, understanding functional causality is crucial when designing a shared resource scheduling strategy. Without this, the system would be unlikely to achieve a holistic functional quality of service (QoS) and may not meet safety goals. This work proposes a novel graceful degradation strategy in a mixed-criticality context. Instead of discarding computational load at the application level, a qualitative and quantitative definition of importance order is used to assist in realizing finer-grained task-level degradation and preserve more LO-criticality tasks. The causality analysis-based degradation bridges the gap where functional dependencies are not considered in the scheduling design and, thus, leads to the system being able to continue to run with relatively high QoS during the degradation process.
- 2010. AUTOSAR specification of operating system. http://www.autosar.org/, AUTOSAR.Google Scholar
- 2011. ISO 26262 Road Vehicles – Functional Safety. International Organization for Standardization, Geneva, CH, Standard.Google Scholar
- Ankur Ankan and Abinash Panda. 2015. pgmpy: Probabilistic graphical models using python. In Proceedings of the 14th Python in Science Conference (SCIPY 2015). Citeseer.Google ScholarCross Ref
- Neil C Audsley. 1991. Optimal priority assignment and feasibility of static priority tasks with arbitrary start times. Citeseer.Google Scholar
- Sanjoy K Baruah, Alan Burns, and Robert I Davis. 2011. Response-time analysis for mixed criticality systems. In 2011 IEEE 32nd Real-Time Systems Symposium. IEEE, 34–43.Google ScholarDigital Library
- Alan Burns and Robert Davis. 2013. Mixed criticality systems-a review. Department of Computer Science, University of York, Tech. Rep (2013), 1–69.Google Scholar
- Alan Burns, Robert I Davis, Sanjoy Baruah, and Iain Bate. 2018. Robust mixed-criticality systems. IEEE Trans. Comput. 67, 10 (2018), 1478–1491.Google ScholarCross Ref
- Paul Emberson and Iain Bate. 2008. Extending a task allocation algorithm for graceful degradation of real-time distributed embedded systems. In 2008 Real-Time Systems Symposium. IEEE, 270–279.Google ScholarDigital Library
- Paul Emberson, Roger Stafford, and Robert I Davis. 2010. Techniques for the synthesis of multiprocessor tasksets. In proceedings 1st International Workshop on Analysis Tools and Methodologies for Embedded and Real-time Systems (WATERS 2010). 6–11.Google Scholar
- Tom Fleming and Alan Burns. 2014. Incorporating the notion of importance into mixed criticality systems. In Proc. 2nd Workshop on Mixed Criticality Systems (WMC), RTSS. 33–38.Google Scholar
- Xiaozhe Gu and Arvind Easwaran. 2016. Dynamic budget management with service guarantees for mixed-criticality systems. In 2016 IEEE Real-Time Systems Symposium (RTSS). IEEE, 47–56.Google ScholarCross Ref
- Stefan Holzknecht, Erwin Biebl, and Hans-Ulrich Michel. 2009. Graceful degradation for driver assistance systems. In Advanced Microsystems for Automotive Applications 2009. Springer, 255–265.Google ScholarCross Ref
- Tasuku Ishigooka, Satoshi Otsuka, Kazuyoshi Serizawa, Ryo Tsuchiya, and Fumio Narisawa. 2019. Graceful degradation design process for autonomous driving system. In International Conference on Computer Safety, Reliability, and Security. Springer, 19–34.Google ScholarDigital Library
- Zhe Jiang, Shuai Zhao, Ran Wei, Dawei Yang, Richard Paterson, Nan Guan, Yan Zhuang, and Neil Audsly. 2021. Bridging the Pragmatic Gaps for Mixed-Criticality Systems in the Automotive Industry. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (2021).Google Scholar
- Uffe B Kjærulff and Anders L Madsen. 2005. Probabilistic networks-an introduction to bayesian networks and influence diagrams. Aalborg University (2005), 10–31.Google Scholar
- John Alexander McDermid, Yan Jia, and Ibrahim Habli. 2019. Towards a framework for safety assurance of autonomous systems. In Artificial Intelligence Safety 2019. CEUR Workshop Proceedings, 1–7.Google Scholar
- Jan Reich, Marc Wellstein, Ioannis Sorokos, Fabian Oboril, and Kay-Ulrich Scholl. 2021. Towards a Software Component to Perform Situation-Aware Dynamic Risk Assessment for Autonomous Vehicles. In European Dependable Computing Conference. Springer, 3–11.Google ScholarCross Ref
- Paolo Trucco, Enrico Cagno, Fabrizio Ruggeri, and Ottavio Grande. 2008. A Bayesian Belief Network modelling of organisational factors in risk analysis: A case study in maritime transportation. Reliability Engineering & System Safety 93, 6 (2008), 845–856.Google ScholarCross Ref
- Steve Vestal. 2007. Preemptive scheduling of multi-criticality systems with varying degrees of execution time assurance. In 28th IEEE International Real-Time Systems Symposium (RTSS 2007). IEEE, 239–243.Google ScholarCross Ref
- Shuai Zhao, Xiaotian Dai, Iain Bate, Alan Burns, and Wanli Chang. 2020. DAG scheduling and analysis on multiprocessor systems: Exploitation of parallelism and dependency. In 2020 IEEE Real-Time Systems Symposium (RTSS). IEEE, 128–140.Google ScholarCross Ref
Recommendations
Context-Aware Graceful Degradation for Mixed-Criticality Scheduling in Autonomous Systems
Autonomous systems are of high complexity and often regarded as mixed-criticality systems (MCSs) in which functions are allocated criticality levels according to risk assessment based on safety standards. Typically, tasks have different real-time ...
Declarative Scheduling for Optimally Graceful QoS Degradation
ICMCS '96: Proceedings of the 1996 International Conference on Multimedia Computing and SystemsThis paper introduces a mechanism for optimally graceful QoS degradation in distributed real-time multimedia services, Declarative Scheduling. We assume that for each medium of a multimedia service there are a set of implementations, both with differing ...
Declarative Scheduling for Optimally Graceful QoS Degradation
ICMCS '96: Proceedings of the 1996 International Conference on Multimedia Computing and SystemsThis paper introduces a mechanism for optimally graceful QoS degradation in distributed real-time multimedia services, Declarative Scheduling. We assume that for each medium of a multimedia service there are a set of implementations, both with differing ...
Comments