ABSTRACT
Heterogeneous multicore processors have become popular computing engines for modern embedded real-time systems recently. However, there is rather limited research on the scheduling of real-time tasks running on heterogeneous multicore systems with shared resources. Note that, different partitionings of tasks upon heterogeneous cores can affect the synchronization overheads of tasks (and thus the system schedulability). Focusing on the partitioned-EDF scheduling and resource access protocol MSRP (Multiprocessor Stack Resource Policy), this paper proposes an effective synchronization aware task partitioning algorithm for heterogeneous multicores (SATPA-HM). Several resource-oriented heuristics are exploited to tighten the bound on the synchronization costs of tasks through dynamic task prioritization and to find an appropriate core for each task that can minimize the system utilization increment. The simulation results show that our proposed SA-TPA-HM scheme can achieve higher acceptance ratio (e.g., 60% more), when compared to the existing schemes designed for homogeneous multicores.
- T. P. Baker. 1991. Stack-based scheduling for realtime processes. Real-Time System 3, 1 (1991), 67--99. Google ScholarDigital Library
- A. Block, H. Leontyev, Björn B Brandenburg, and J.-H. Anderson. 2007. A Flexible Real-Time Locking Protocol for Multiprocessors. In Int'l Conference on Embedded and Real-Time Computing Systems and Applications. 47--56. Google ScholarDigital Library
- A. Burns and A. J. Wellings. 2013. A Schedulability Compatible Multiprocessor Resource Sharing Protocol - MrsP. In Euromicro Conference on Real-Time Systems. 282--291. Google ScholarDigital Library
- N. Chitlur, G. Srinivasa, S. Hahn, P K. Gupta, D. Reddy, D. Koufaty, P. Brett, A. Prabhakaran, L. Zhao, and N. Ijih. 2012. QuickIA: Exploring heterogeneous architectures on real prototypes. In International Symposium on High-Performance Computer Architecture. 1--8. Google ScholarDigital Library
- P. Gai, M. Di Natale, G. Lipari, A. Ferrari, C. Gabellini, and P. Marceca. 2003. A comparison of MPCP and MSRP when sharing resources in the Janus multiple-processor on a chip platform. In Real-Time and Embedded Technology and Applications Symposium. 189--198. Google ScholarDigital Library
- J.-J. Han, X. Wu, D. Zhu, H. Jin, L. T. Yang, and J.-L. Gaudiot. 2012. Synchronization-aware energy management for VFI-based multicore real-time systems. IEEE Trans. on Computers 61, 12 (2012), 1682--1696. Google ScholarDigital Library
- J.-J. Han, D. Zhu, X. Wu, L. T. Yang, and H. Jin. 2014. Multiprocessor Real-Time Systems with Shared Resources: Utilization Bound and Mapping. IEEE Trans. Parallel Distrib. Syst. 25, 11 (2014), 2981--2991.Google ScholarCross Ref
- M. Happe, E. Bbers, and M. Platzner. 2013. A self-adaptive heterogeneous multi-core architecture for embedded real-time video object tracking. J. of Real-Time Image Processing 8, 1 (2013), 95--110. Google ScholarDigital Library
- L. S. Indrusiak, J. Harbin, and A. Burns. 2015. Average and worst-case latency improvements in mixed-criticality wormhole networks-on-chip. In the Euromicro Conference on Real-Time Systems. 47--56. Google ScholarDigital Library
- P. Kollig, C. Osborne, and T. Henriksson. 2009. Heterogeneous multi-core platform for consumer multimedia applications. In Conference on DATE. 1254--1259. Google ScholarDigital Library
- K. Lakshmanan, D.-de Niz, and R. Rajkumar. 2009. Coordinated Task Scheduling, Allocation and Synchronization on Multiprocessors. In IEEE Real-Time Systems Symposium. 469--478. Google ScholarDigital Library
- D. Liu, J. Spasic, P. Wang, and T. Stefanov. 2016. Energy-Efficient Scheduling of Real-Time Tasks on Heterogeneous Multicores Using Task Splitting. In IEEE International Conference on Embedded and Real-Time Computing Systems and Applications. 149--158.Google Scholar
- J. M. López, J. L. Díaz, and D. F. García. 2004. Utilization bounds for EDF scheduling on real-time multiprocessor systems. Real-Time Systems 28, 1 (2004), 39--68. Google ScholarDigital Library
- L. Sha, R. Rajkumar, and J. P. Lehoczky. 1990. Priority Inheritance Protocols: An Approach to Real-Time Synchronization. IEEE Transactions on Computer 39, 9 (1990), 1175--1185. Google ScholarDigital Library
- Arm Techcon. 2011. Big.LITTLE processing with ARM Cortex-A15 and Cortex-A7. Eetimes Com (2011).Google Scholar
- G. Tong and C. Liu. 2016. Supporting Soft Real-Time Sporadic Task Systems on Uniform Heterogeneous Multiprocessors with No Utilization Loss. IEEE Trans. on Parallel Distrib. Syst. 27, 9 (2016), 2740--2752. Google ScholarDigital Library
- T. H. Tsai, L. F. Fan, Y. S. Chen, and T. S. Yao. 2016. Triple Speed: Energy-Aware Real-Time Task Synchronization in Homogeneous Multi-Core Systems. IEEE Trans. on Computers 65, 4 (2016), 1297--1309. Google ScholarDigital Library
- C. Zoubek and P. Trommler. 2017. Overview of worst case execution time analysis in single- and multicore environments. In International Conference on Architecture of Computing Systems. 1--5.Google Scholar
- Resource-aware partitioned scheduling for heterogeneous multicore real-time systems
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