A task remapping technique for reliable multi-core embedded systems
- Authors
- Lee, Chanhee; Kim, Hokeun; Park, Hae-woo; Kim, Sungchan.; Oh, Hyunok; Ha, Soonhoi
- Issue Date
- Oct-2010
- Keywords
- Multi-core embedded systems; Reliability; Static task mapping
- Citation
- Embedded Systems Week 2010 - Proceedings of the 8th IEEE/ACM/IFIP International Conference on Compilers, Architecture and Synthesis for Embedded Systems, CODES+ISSS'2010, pp.307 - 316
- Indexed
- SCOPUS
- Journal Title
- Embedded Systems Week 2010 - Proceedings of the 8th IEEE/ACM/IFIP International Conference on Compilers, Architecture and Synthesis for Embedded Systems, CODES+ISSS'2010
- Start Page
- 307
- End Page
- 316
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/173569
- DOI
- 10.1145/1878961.1879014
- ISSN
- 0000-0000
- Abstract
- With the continuous scaling of semiconductor technology, the life-time of circuit is decreasing so that processor failure becomes an important issue in MPSoC design. A software solution to tolerate run-time processor failure is to migrate tasks from the failed processors to the live processors when failure occurs. Previous works on run-time task migration usually aim to minimize the migration overhead with or without a given latency constraint. For streaming applications, however, it is more important to minimize the throughput degradation than the migration overhead or the latency. Hence, we propose a task remapping technique to minimize the throughput degradation assuming that the migration overhead can be amortized safely. The target multi-core system assumed in this paper consists of processor pools and each pool consists of homogeneous processors. The proposed technique is based on an intensive compile-time analysis for all possible failure scenarios. It involves the following steps; 1) Determine the static mapping of tasks onto the live processors, aiming to minimize the throughput degradation: 2) Find an optimal processor-to-processor mapping to minimize the task migration overhead: and 3) Store the resultant task remapping information that includes task mapping and processor-to-processor mapping results. Since the task remapping information is pre-computed at compile-time for all possible failure scenarios, it should be efficiently represented and stored. At run-time, we simply remap the tasks following the compile-time decision. We examine the scalability of the proposed technique on both space and run-time overhead for compile-time analysis varying the number of failed processors. Through intensive experiments, we show that the proposed technique outperforms the previous works with respect to application throughput.
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