Graceful Degradation with Condition-and Inference-Awareness for Mixed-Criticality Scheduling in Autonomous Systems

Jie Zou; Xiaotian Dai; John Alexander McDermid

doi:10.1145/3576914.3587511

Graceful Degradation with Condition-and Inference-Awareness for Mixed-Criticality Scheduling in Autonomous Systems

Jie Zou, Xiaotian Dai, John Alexander McDermid

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Proceedings of Cyber-Physical Systems and Internet of Things Week 2023
Publisher	Association for Computing Machinery, Inc
Pages	215–220
ISBN (Print)	979-8-4007-0049-1
DOIs	https://doi.org/10.1145/3576914.3587511
Publication status	Published - 9 May 2023

Access to Document

10.1145/3576914.3587511

Cite this

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title = "Graceful Degradation with Condition-and Inference-Awareness for Mixed-Criticality Scheduling in Autonomous Systems",

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.",

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Graceful Degradation with Condition-and Inference-Awareness for Mixed-Criticality Scheduling in Autonomous Systems. / Zou, Jie ; Dai, Xiaotian ; McDermid, John Alexander.
Proceedings of Cyber-Physical Systems and Internet of Things Week 2023. Association for Computing Machinery, Inc, 2023. p. 215–220.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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