Application-Aware Scheduling of Networked Applications over the Low-Power Wireless Bus

Wardega, Kacper; Li, Wenchao

doi:10.23919/date48585.2020.9116206

Cited by 2 publications

(1 citation statement)

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“…Compared with hard deadlines, weakly-hard constraints can more accurately capture the timing requirements of those system functions that tolerate deadline misses, and significantly improve system feasibility and flexibility [20,16]. Compared with soft deadlines, where any deadline miss is allowed, weakly-hard constraints could still provide deterministic guarantees on system safety, stability, performance, and other properties under formal analysis [17,29].…”

Section: Introductionmentioning

confidence: 99%

SAW: A Tool for Safety Analysis of Weakly-Hard Systems

Huang¹,

Chang

Lin

et al. 2020

Computer Aided Verification

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We introduce SAW, a tool for safety analysis of weakly-hard systems, in which traditional hard timing constraints are relaxed to allow bounded deadline misses for improving design flexibility and runtime resiliency. Safety verification is a key issue for weakly-hard systems, as it ensures system safety under allowed deadline misses. Previous works are either for linear systems only, or limited to a certain type of nonlinear systems (e.g., systems that satisfy exponential stability and Lipschitz continuity of the system dynamics). In this work, we propose a new technique for infinite-time safety verification of general nonlinear weakly-hard systems. Our approach first discretizes the safe state set into grids and constructs a directed graph, where nodes represent the grids and edges represent the reachability relation. Based on graph theory and dynamic programming, our approach can effectively find the safe initial set (consisting of a set of grids), from which the system can be proven safe under given weakly-hard constraints. Experimental results demonstrate the effectiveness of our approach, when compared with the state-of-the-art. An open source implementation of our tool is available at https://github.com/551100kk/SAW . The virtual machine where the tool is ready to run can be found at https://www.csie.ntu.edu.tw/~r08922054/SAW.ova .

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