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.
- 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 Scholar
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- Sparsh Mittal. 2016. A survey of techniques for approximate computing. ACM Comput. Surv. 48, 4, Article 62 (Mar. 2016), 33 pages.Google Scholar
- 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 Scholar
Digital Library
- Xuan Wang, Jinghong Liu, and Qianfei Zhou. 2016. Real-time multi-target localization from unmanned aerial vehicles. Sensors 17, 1 (2016), 33.Google Scholar
Cross Ref
- 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 Scholar
Cross Ref
- 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 Scholar
- 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 Scholar
Digital Library
- Giorgio C Buttazzo. 2011. Hard Real-time Computing Systems: Predictable Scheduling Algorithms and Applications. Vol. 24. Springer.Google Scholar
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
Cross Ref
- 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 Scholar
Cross Ref
- 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 Scholar
Cross Ref
- 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 Scholar
Cross Ref
- 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 Scholar
Cross Ref
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
Cross Ref
- 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 Scholar
Digital Library
- 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 Scholar
- 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 Scholar
Cross Ref
- 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 Scholar
Digital Library
- 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 Scholar
Cross Ref
- 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 Scholar
Cross Ref
- 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 Scholar
Digital Library
- David S. Johnson and Michael R. Garey. 1979. Computers and Intractability: A Guide to the Theory of NP-completeness. WH Freeman.Google Scholar
Digital Library
- Jeffrey D. Ullman. 1975. NP-complete scheduling problems. J. Comput. Syst. Sci. 10, 3 (1975), 384–393.Google Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- Te C. Hu. 1961. Parallel sequencing and assembly line problems. Operat. Res. 9, 6 (1961), 841–848.Google Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- Guoqi Xie, Renfa Li, and Keqin Li. Semanticscholar, 2016. Distributed Computing for Functional Safety of Automotive Embedded Systems. Semanticscholar.Google Scholar
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
Cross Ref
- [n.d.]. CPLEX Optimizer. Retrieved from https://www.ibm.com/analytics/data-science/prescriptive-analytics/cplex-optimizer.Google Scholar
- 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 Scholar
Cross Ref
Index Terms
SLAQA: Quality-level Aware Scheduling of Task Graphs on Heterogeneous Distributed Systems
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