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SLAQA: Quality-level Aware Scheduling of Task Graphs on Heterogeneous Distributed Systems

Published:09 July 2021Publication History
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Abstract

Continuous demands for higher performance and reliability within stringent resource budgets is driving a shift from homogeneous to heterogeneous processing platforms for the implementation of today’s cyber-physical systems (CPSs). These CPSs are typically represented as Directed-acyclic Task Graph (DTG) due to the complex interactions between their functional components that are often distributed in nature. In this article, we consider the problem of scheduling a real-time application modelled as a single DTG, where tasks may have multiple implementations designated as quality-levels, with higher quality-levels producing more accurate results and contributing to higher rewards/Quality-of-Service for the system. First, we introduce an optimal solution using Integer Linear Programming (ILP) for a DTG with multiple quality-levels, to be executed on a heterogeneous distributed platform. However, this ILP-based optimal solution exhibits high computational complexity and does not scale for moderately large problem sizes. Hence, we propose two low-overhead heuristic algorithms called Global Slack Aware Quality-level Allocator (G-SLAQA) and Total Slack Aware Quality-level Allocator (T-SLAQA), which are able to produce satisfactorily efficient as well as fast solutions within a reasonable time. G-SLAQA, the baseline heuristic, is greedier and faster than its counter-part T-SLAQA, whose performance is at least as efficient as G-SLAQA. The efficiency of all the proposed schemes have been extensively evaluated through simulation-based experiments using benchmark and randomly generated DTGs. Through the case study of a real-world automotive traction controller, we generate schedules using our proposed schemes to demonstrate their practical applicability.

References

  1. X. Zhu, J. Zhu, M. Ma, and D. Qiu. 2010. SAQA: A self-adaptive qos-aware scheduling algorithm for real-time tasks on heterogeneous clusters. In Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing. 224–232.Google ScholarGoogle Scholar
  2. Xiaomin Zhu, Xiao Qin, and Meikang Qiu. 2011. QoS-aware fault-tolerant scheduling for real-time tasks on heterogeneous clusters. IEEE Trans. Comput. 60, 6 (2011), 800–812.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Marcelo Ruaro, Axel Jantsch, and Fernando Gehm Moraes. 2019. Self-adaptive qos management of computation and communication resources in many-core socs. ACM Trans. Embed. Comput. Syst. 18, 4 (2019), 1–21.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Sparsh Mittal. 2016. A survey of techniques for approximate computing. ACM Comput. Surv. 48, 4, Article 62 (Mar. 2016), 33 pages.Google ScholarGoogle Scholar
  5. Dario Socci, Peter Poplavko, Saddek Bensalem, and Marius Bozga. 2015. Multiprocessor scheduling of precedence-constrained mixed-critical jobs. In Proceedings of the IEEE 18th International Symposium on Real-Time Distributed Computing (ISORC’15). IEEE, 198–207.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Xuan Wang, Jinghong Liu, and Qianfei Zhou. 2016. Real-time multi-target localization from unmanned aerial vehicles. Sensors 17, 1 (2016), 33.Google ScholarGoogle ScholarCross RefCross Ref
  7. Yun Hou and Changbin Yu. 2014. Autonomous target localization using quadrotor. In Proceedings of the 26th Chinese Control and Decision Conference (CCDC’14). IEEE, 864–869.Google ScholarGoogle ScholarCross RefCross Ref
  8. Jing Liu, Kenli Li, Dakai Zhu, Jianjun Han, and Keqin Li. 2016. Minimizing cost of scheduling tasks on heterogeneous multicore embedded systems. ACM Trans. Embed. Comput. Syst. 16, 2 (2016), 1–25.Google ScholarGoogle Scholar
  9. Hamid Arabnejad and Jorge G. Barbosa. 2014. List scheduling algorithm for heterogeneous systems by an optimistic cost table. IEEE Trans. Parallel Distrib. Syst. 25, 3 (2014), 682–694.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Giorgio C Buttazzo. 2011. Hard Real-time Computing Systems: Predictable Scheduling Algorithms and Applications. Vol. 24. Springer.Google ScholarGoogle Scholar
  11. Nagarajan Kandasamy, John P. Hayes, and Brian T. Murray. 2003. Transparent recovery from intermittent faults in time-triggered distributed systems. IEEE Trans. Comput. 52, 2 (2003), 113–125.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Rajesh Devaraj, Arnab Sarkar, and Santosh Biswas. 2017. Fault-tolerant preemptive aperiodic rt scheduling by supervisory control of TDES on multiprocessors. ACM Trans. Embed. Comput. Syst. 16, 3, Article 87 (Apr. 2017), 25 pages. https://doi.org/10.1145/3012278Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Rajesh Devaraj, Arnab Sarkar, and Santosh Biswas. 2017. Real-time scheduling of non-preemptive sporadic tasks on uniprocessor systems using supervisory control of timed DES. Proceedings of the IEEE American Control Conference (ACC’17). 3212–3217.Google ScholarGoogle ScholarCross RefCross Ref
  14. Rajesh Devaraj, Arnab Sarkar, and Santosh Biswas. 2021. Optimal work-conserving scheduler synthesis for real-time sporadic tasks using supervisory control of timed discrete-event systems. J. Schedul. 24, 1 (2021), 69–82.Google ScholarGoogle ScholarCross RefCross Ref
  15. Sanjit Kumar Roy, Rajesh Devaraj, Arnab Sarkar, Kankana Maji, and Sayani Sinha. 2020. Contention-aware optimal scheduling of real-time precedence-constrained task graphs on heterogeneous distributed systems. J. Syst. Arch. 105 (2020), 101706.Google ScholarGoogle ScholarCross RefCross Ref
  16. Sarad Venugopalan and Oliver Sinnen. 2015. ILP formulations for optimal task scheduling with communication delays on parallel systems. IEEE Trans. Parallel Distrib. Syst. 26, 1 (2015), 142–151.Google ScholarGoogle ScholarCross RefCross Ref
  17. Sanjit Kumar Roy, Rajesh Devaraj, and Arnab Sarkar. 2021. Contention cognizant scheduling of task graphs on shared bus based heterogeneous platforms. IEEE Trans. Comput.-Aid. Des. Integr. Circ. Syst. (2021).Google ScholarGoogle ScholarCross RefCross Ref
  18. Haluk Topcuoglu, Salim Hariri, and Min-you Wu. 2002. Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13, 3 (2002), 260–274.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Guoqi Xie, Renfa Li, and Keqin Li. 2015. Heterogeneity-driven end-to-end synchronized scheduling for precedence constrained tasks and messages on networked embedded systems. J. Parallel Distrib. Comput. 83 (2015), 1–12.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Sanjit Kumar Roy, Rajesh Devaraj, and Arnab Sarkar. 2019. Optimal scheduling of ptgs with multiple service levels on heterogeneous distributed systems. In Proceedings of the 2019 American Control Conference (ACC’19). IEEE, 157–162.Google ScholarGoogle ScholarCross RefCross Ref
  21. Benjamin Rouxel, Steven Derrien, and Isabelle Puaut. 2017. Tightening contention delays while scheduling parallel applications on multi-core architectures. ACM Trans. Embed. Comput. Syst. 16, 5s (2017), 1–20.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Benjamin Rouxel, Stefanos Skalistis, Steven Derrien, and Isabelle Puaut. 2019. Hiding communication delays in contention-free execution for spm-based multi-core architectures. In Proceedings of the 31st Euromicro Conference on Real-Time Systems (ECRTS’19). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik.Google ScholarGoogle Scholar
  23. Daniel Casini, Alessandro Biondi, Geoffrey Nelissen, and Giorgio Buttazzo. 2018. Partitioned fixed-priority scheduling of parallel tasks without preemptions. In Proceedings of the 2018 IEEE Real-Time Systems Symposium (RTSS’18). IEEE, 421–433.Google ScholarGoogle ScholarCross RefCross Ref
  24. Jing Li, Jian Jia Chen, Kunal Agrawal, Chenyang Lu, Chris Gill, and Abusayeed Saifullah. 2014. Analysis of federated and global scheduling for parallel real-time tasks. In Proceedings of the 2014 26th Euromicro Conference on Real-Time Systems. IEEE, 85–96.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Son Dinh, Christopher Gill, and Kunal Agrawal. 2020. Efficient deterministic federated scheduling for parallel real-time tasks. In Proceedings of the IEEE 26th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA’20). IEEE, 1–10.Google ScholarGoogle ScholarCross RefCross Ref
  26. Xu Jiang, Nan Guan, Xiang Long, and Wang Yi. 2017. Semi-federated scheduling of parallel real-time tasks on multiprocessors. In Proceedings of the IEEE Real-Time Systems Symposium (RTSS’17). IEEE, 80–91.Google ScholarGoogle ScholarCross RefCross Ref
  27. Hidehiro Kanemitsu, Masaki Hanada, and Hidenori Nakazato. 2016. Clustering-based task scheduling in a large number of heterogeneous processors. IEEE Trans. Parallel Distrib. Syst. 27, 11 (2016), 3144–3157.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. David S. Johnson and Michael R. Garey. 1979. Computers and Intractability: A Guide to the Theory of NP-completeness. WH Freeman.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Jeffrey D. Ullman. 1975. NP-complete scheduling problems. J. Comput. Syst. Sci. 10, 3 (1975), 384–393.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Cristina Boeres, Vinod E. F. Rebello, et al. 2004. A cluster-based strategy for scheduling task on heterogeneous processors. In Proceedings of the 16th Symposium on Computer Architecture and High Performance Computing. IEEE, 214–221.Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Behrouz Jedari and Mahdi Dehghan. 2009. Efficient DAG scheduling with resource-aware clustering for heterogeneous systems. In Computer and Information Science 2009. Springer, 249–261.Google ScholarGoogle Scholar
  32. Ishfaq Ahmad and Yu-Kwong Kwok. 1998. On exploiting task duplication in parallel program scheduling. IEEE Trans. Parallel Distrib. Syst. 9, 9 (1998), 872–892.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Rashmi Bajaj and Dharma P. Agrawal. 2004. Improving scheduling of tasks in a heterogeneous environment. IEEE Trans. Parallel Distrib. Syst. 15, 2 (2004), 107–118.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. M.-Y. Wu and Daniel D. Gajski. 1990. Hypertool: A programming aid for message-passing systems. IEEE Trans. Parallel Distrib. Syst. 1, 3 (1990), 330–343.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Te C. Hu. 1961. Parallel sequencing and assembly line problems. Operat. Res. 9, 6 (1961), 841–848.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Jaeyong Rho, Takuya Azumi, Mayo Nakagawa, Kenya Sato, and Nobuhiko Nishio. 2017. Scheduling parallel and distributed processing for automotive data stream management system. J. Parallel Distrib. Comput. 109 (2017), 286–300.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Jaeyeon Kang and Sanjay Ranka. 2010. Dynamic slack allocation algorithms for energy minimization on parallel machines. J. Parallel Distrib. Comput. 70, 5 (2010), 417–430.Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Guoqi Xie, Renfa Li, and Keqin Li. Semanticscholar, 2016. Distributed Computing for Functional Safety of Automotive Embedded Systems. Semanticscholar.Google ScholarGoogle Scholar
  39. Gideon Juve, Ann Chervenak, Ewa Deelman, Shishir Bharathi, Gaurang Mehta, and Karan Vahi. 2013. Characterizing and profiling scientific workflows. Fut. Gener. Comput. Syst. 29, 3 (2013), 682–692.Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Anne Benoit, Mourad Hakem, and Yves Robert. 2009. Contention awareness and fault-tolerant scheduling for precedence constrained tasks in heterogeneous systems. Parallel Comput. 35, 2 (2009), 83–108.Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Guoqi Xie, Junqiang Jiang, Yan Liu, Renfa Li, and Keqin Li. 2017. Minimizing energy consumption of real-time parallel applications using downward and upward approaches on heterogeneous systems. IEEE Trans. Industr. Inf. 13, 3 (2017), 1068–1078.Google ScholarGoogle ScholarCross RefCross Ref
  42. [n.d.]. CPLEX Optimizer. Retrieved from https://www.ibm.com/analytics/data-science/prescriptive-analytics/cplex-optimizer.Google ScholarGoogle Scholar
  43. Nagarajan Kandasamy, John P. Hayes, and Brian T. Murray. 2005. Dependable communication synthesis for distributed embedded systems. Reliabil. Eng. Syst. Saf. 89, 1 (2005), 81–92.Google ScholarGoogle ScholarCross RefCross Ref

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