A reconfigurable framework for compositional schedulability and power analysis of hierarchical scheduling systems with frequency scaling

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• 作者：Abdeldjalil Boudjadar^a ; ^{abdeldjalil.boudjadar@liu.se" class="auth_mail" title="E-mail the corresponding author} ; Alexandre David^b ; ^{adavid@cs.aau.dk" class="auth_mail" title="E-mail the corresponding author} ; Jin Hyun Kim^c ; ^{jin-hyun.kim@inria.fr" class="auth_mail" title="E-mail the corresponding author} ; Kim G. Larsen^b ; ^{kgl@cs.aau.dk" class="auth_mail" title="E-mail the corresponding author} ; Marius Mikučionis^b ; ^{marius@cs.aau.dk" class="auth_mail" title="E-mail the corresponding author} ; Ulrik Nyman^b ; ^{ulrik@cs.aau.dk" class="auth_mail" title="E-mail the corresponding author} ; Arne Skou^b ; ^{ask@cs.aau.dk" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Hierarchical scheduling systems ; Schedulability analysis ; Power consumption ; Voltage/frequency scaling ; Uppaal
• 刊名：Science of Computer Programming
• 出版年：2015
• 出版时间：1 December 2015
• 年：2015
• 卷：113
• 期：part_P3
• 页码：236-260
• 全文大小：1586 K

文摘

This paper presents a compositional framework for the modeling and analysis of hierarchical scheduling systems. We consider both schedulability and energy consumption of individual components, while analyzing a single core setting with a voltage frequency scaling CPU. According to the CPU frequency scaling, each task has a set of different execution times. Thus, the energy consumption of the whole system varies from one execution to another.

We analyze each component individually by checking the feasibility of its workload against both the CPU availability and energy consumption constraints of such a component. Our periodic task model considers both static and dynamic priorities together with preemptive and non-preemptive behaviors. The models are realized using different forms of Hybrid Automata, all of which are analyzed using variants of Uppaal. The CPU frequencies, task behavior and scheduling policies used in each component are some of the reconfigurable parameters of the system. Finally, we demonstrate the applicability and scalability of our framework by analyzing the schedulability and power consumption of an avionics system.