Abstract
In real-time systems, to provide timing guarantees, pessimistic worst-case bounds are traditionally assumed for computation and communication tasks. In practice, tighter bounds can be established by allowing computation or communication to be dropped or to execute in an approximate or imprecise manner. This creates a fundamental tradeoff between tightness of bounds and degradations in application quality. In this chapter, we present scheduling of computation and communication tasks as a quality optimization problem in terms of computation and communication budget assignments for systems with independent and dependent tasks. For independent tasks, traditional mixed-criticality (MC) systems can be extended to derive the precision of low-criticality tasks such that combined quality degradation is minimized while satisfying schedule admissibility. For dependent tasks, we describe approaches to find an optimized mapping, scheduling, and budgeting of task graphs that maximizes overall quality while meeting end-to-end real-time constraints. We evaluate our proposed approaches on both artificial and real-world task sets and compare them to traditional solutions that do not allow for approximations.
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References
Wilhelm, R., Engblom, J., Ermedahl, A., Holsti, N., Thesing, S., Whalley, D., Bernat, G., Ferdinand, C., Heckmann, R., Mitra, T., Mueller, F., Puaut, I., Puschner, P., Staschulat, J., & Stenström, P. (2008). The worst-case execution-time problem—overview of methods and survey of tools. ACM Transactions on Embedded Computing Systems (TECS), 7(3), 1–53.
Burns, A., & Davis, R. (2013). Mixed criticality systems - a review. Tech. Rep., Department of Computer Science, University of York.
Derler, P., Feng, T. H., Lee, E. A., Matic, S., Patel, H. D., Zhao, Y., & Zou, J. (2008). PTIDES: A programming model for distributed real-time embedded systems. Tech. Rep. UCB/EECS-2008-72, EECS Department, University of California, Berkeley.
Schmidt, D., & Kuhns, F. (2000). An overview of the real-time CORBA specification. Computer, 33(6), 56–63.
Schulzrinne, H., Casner, S., Frederick, R., & Jacobson, V. (2003). RTP: A transport protocol for real-time applications. RFC 3550. RFC Editor.
Baruah, S., Bonifaci, V., DAngelo, G., Li, H., Marchetti-Spaccamela, A., Van Der Ster, S., & Stougie, L. (2012). The preemptive uniprocessor scheduling of mixed-criticality implicit-deadline sporadic task systems. In Euromicro Conference on Real-Time Systems (ECRTS).
Liu, C. L., & Layland, J. W. (1973). Scheduling algorithms for multiprogramming in a hard-real-time environment. Journal of the ACM, 20, 46–61.
Baruah, S., Chattopadhyay, B., Li, H., & Shin, I. (2014). Mixed-criticality scheduling on multiprocessors. Real-Time Systems, 50(1), 142–177.
Baruah, S. (2004). Optimal utilization bounds for the fixed-priority scheduling of periodic task systems on identical multiprocessors. IEEE Transactions on Computers (TC), 53(6), 781–784.
Li, H., & Baruah, S. (2012). Global mixed-criticality scheduling on multiprocessors. In Euromicro Conference on Real-Time Systems (ECRTS).
Funk, S., Levin, G., Sadowski, C., Pye, I., & Brandt, S. (2011). DP-Fair: A unifying theory for optimal hard real-time multiprocessor scheduling. Real-Time Systems, 47(5), 389–429.
Lee, J., Phan, K.-M., Gu, X., Lee, J., Easwaran, A., Shin, I., & Lee, I. (2014). MC-Fluid: fluid model-based mixed-criticality scheduling on multiprocessors. In Real-Time Systems Symposium (RTSS).
Han, J., & Orshansky, M. (2013). Approximate computing: an emerging paradigm for energy-efficient design. In IEEE European Test Symposium (ETS).
Liu, J. W.-S., Lin, K.-J., Shih, W. K., Yu, A. C.-S., Chung, J.-Y., & Zhao, W. (1991). Algorithms for scheduling imprecise computations (pp. 203–249). New York: Springer.
Liu, D., Spasic, J., Guan, N., Chen, G., Liu, S., Stefanov, T., & Yi, W. (2016). EDF-VD scheduling of mixed-criticality systems with degraded quality guarantees. In Real-Time Systems Symposium (RTSS).
Burns, A., & Baruah, S. (2013). Towards a more practical model for mixed criticality systems. In Workshop on Mixed-Criticality Systems.
Baruah, S., Burns, A., & Guo, Z. (2016). Scheduling mixed-criticality systems to guarantee some service under all non-erroneous behaviors. In Euromicro Conference on Real-Time Systems (ECRTS).
Pathan, R. M. (2017). Improving the quality-of-service for scheduling mixed-criticality systems on multiprocessors. In LIPIcs-Leibniz International Proceedings in Informatics (vol. 76).
Huang, L., Hou, I., Sapatnekar, S. S., & Hu, J. (2018). Graceful degradation of low-criticality tasks in multiprocessor dual-criticality systems. In International Conference on Real-Time Networks and Systems (RTNS).
Barijough, K. M., Zhao, Z., & Gerstlauer, A. (2019). Quality/latency-aware real-time scheduling of distributed streaming IoT applications. ACM Transactions on Embedded Computer Systems (TECS), 18, 83:1–83:23.
Francis, S., & Gerstlauer, A. (2017). A reactive and adaptive data flow model for network-of-system specification. IEEE Embedded System Letters (ESL), 9, 121–124.
Bovy, C., Mertodimedjo, H., Hooghiemstra, G., Uijterwaal, H., & Van Mieghem, P. (2002). Analysis of end-to-end delay measurements in internet. In The Passive and Active Measurement Workshop (PAM).
Lee, S., Lee, D., Han, K., Kim, T., Shriver, E., John, L. K., & Gerstlauer, A. (2016). Statistical quality modeling of approximate hardware. In IEEE International Symposium on Quality Electronic Design (ISQED).
Lee, S., & Gerstlauer, A. (2013). Fine grain word length optimization for dynamic precision scaling in DSP systems. In IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC).
ArtyZe. (2018). Image segmentation in darknet. Retrieved 15 Nov, 2020, from https://github.com/ArtyZe/yolo_segmentation
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: unified, real-time object detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv:1804.02767.
Zhao, Z., Barijough, K. M., & Gerstlauer, A. (2018). DeepThings: distributed adaptive deep learning inference on resource-constrained IoT edge clusters. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 37(11), 2348–2359.
Majdik, A. L., Till, C., & Scaramuzza, D. (2017). The Zurich urban micro aerial vehicle dataset. The International Journal of Robotics Research, 36(3), 269–273.
Ahrenholz, J. (2010). Comparison of core network emulation platforms. In Military Communications Conference (Milcom) (pp. 166–171). Piscataway: IEEE.
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Barijough, K.M., Huang, L., Hou, IH., Sapatnekar, S.S., Hu, J., Gerstlauer, A. (2022). Exploiting Approximations in Real-Time Scheduling. In: Bosio, A., Ménard, D., Sentieys, O. (eds) Approximate Computing Techniques. Springer, Cham. https://doi.org/10.1007/978-3-030-94705-7_10
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DOI: https://doi.org/10.1007/978-3-030-94705-7_10
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