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Dynamic and static task allocation for hard real-time video stream decoding on NoCs

Mendis, Hashan R, Audsley, N. C. ORCID: 0000-0003-3739-6590 & Indrusiak, L. S. (2017). Dynamic and static task allocation for hard real-time video stream decoding on NoCs. Leibniz Transactions on Embedded Systems, 4, doi: 10.4230/LITES-v004-i002-a001


Hard real-time (HRT) video systems require admission control decisions that rely on two factors. Firstly, schedulability analysis of the data-dependent, communicating tasks within the application need to be carried out in order to guarantee timing and predictability. Secondly, the allocation of the tasks to multi-core processing elements would generate different results in the schedulability analysis. Due to the conservative nature of the state-of-the-art schedulability analysis of tasks and message lows, and the unpredictability in the application, the system resources are often under-utilised. In this paper we propose two blocking-aware dynamic task allocation techniques that exploit application and platform characteristics, in order to increase the number of simultaneous, fully schedulable, video streams handled by the system. A novel, worst-case response time aware, search-based, static hard real-time task mapper is introduced to act as an upper-baseline to the proposed techniques. Further evaluations are carried out against existing heuristic-based dynamic mappers. Improvements to the admission rates and the system utilisation under a range of different workloads and platform sizes are explored.

Publication Type: Article
Additional Information: This work is licensed under a Creative Commons Attribution 3.0 Germany License (CC BY 3.0 DE). The article has been published in Leibniz Transactions on Embedded Systems, doi
Publisher Keywords: Real-time multimedia; Task mapping; Network-on-chip
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Science & Technology
Text - Published Version
Available under License Creative Commons: Attribution 3.0.

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