Watwaos: A Framework for Worst-Case-Aware Tailoring and Whole-System Analysis of Energy-Constrained Real-Time Systems
Tobias Häberlein, Eva Dengler, Phillip Raffeck, Peter Wägemann
Abstract
Emerging embedded systems have to increasingly meet energy constraints besides their timing requirements. While frequency-scaling techniques are well explored, existing operating systems for embedded real-time systems have shortcomings in comprehensively exploiting energy-saving features present in modern system-on-chip (SoC) platforms. Existing systems lack operating-system abstractions to exploit the tradeoff between computing performance and energy efficiency. Consequently, whole-system analysis techniques are not applicable to yield optimal configurations tailored to the applications' requirements. Finally, the complexity of modern energy-saving hardware features creates huge search spaces for optimal configurations. In this paper, we present WATWAOS, a framework for worst-case-aware tailoring and whole-system analysis of energyconstrained real-time systems. WatwaOS acts as both an analysis/tailoring framework and a (generated) real-time operating system. The approach exploits knowledge acquired during wholesystem analysis and applies worst-case-aware tailoring of the system for its runtime. WatwaOS has an awareness of the application's requirements (i.e., deadlines, peripheral devices) and the underlying SoC's energy-saving features. To achieve the tailoring, WATWAOS introduces a concept of hierarchical abstractions, which offer fine-grained power-management decisions. These abstractions are designed to enable merging of their states without loss of accuracy. Static analysis based on these abstractions yields worst-case-optimal (i.e., provably energy minimal) solutions with regard to given deadlines. To tackle the enormous search space of our bilevel problem, WatwaOS employs several concepts to exploit advanced features of mathematical optimizing tools. The evaluations of WATWAOS validate our claim of finding worst-case-optimal solutions within acceptable analysis times.
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