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Decision-driven scheduling

  • Jung-Eun KimEmail author
  • Tarek Abdelzaher
  • Lui Sha
  • Amotz Bar-Noy
  • Reginald L. Hobbs
  • William Dron
Article
  • 51 Downloads

Abstract

This paper presents a scheduling model, called decision-driven scheduling, elaborates key optimality results for a fundamental scheduling model, and evaluates new heuristics solving more general versions of the problem. In the context of applications that need control and actuation, the traditional execution model has often been either time-driven or event-driven. In time-driven applications, sensors are sampled periodically, leading to the classical periodic task model. In event-driven applications, sensors are sampled when an event of interest occurs, such as motion-activated cameras, leading to an event-driven task activation model. In contrast, in decision-driven applications, sensors are sampled when a particular decision must be made. We offer a justification for why decision-driven scheduling might be of increasing interest to Internet-of-things applications, and explain why it leads to interesting new scheduling problems (unlike time-driven and event-driven scheduling), including the problems addressed in this paper.

Keywords

Internet of Things Smart cities Disaster response infrastructure Decision-driven Freshness 

Notes

Acknowledgements

This work is supported in part by Grants from US ARL W911NF-09-2-0053, Navy N00014-16-1-2151, ONR N00014-14-1-0717, and NSF CNS 13-02563, 13-29886, and 16-18627. Any opinions, findings, and conclusions or recommendations expressed here are those of the authors and do not necessarily reflect the views of sponsors.

References

  1. Adelberg B, Garcia-Molina H, Kao B (1995) Applying update streams in a soft real-time database system. In: Proceedings of the ACM SIGMOD international conference on management of data, ser SIGMODGoogle Scholar
  2. Adelberg B, Kao B, Garcia-Molina H (1996) Database support for efficiently maintaining derived data. In: Proceedings of 5th international conference on extending database technology, vol 1057. Springer, New YorkGoogle Scholar
  3. Cipar J, Ganger G, Keeton K, Morrey CB, III, Soules CA, Veitch A (2012) Lazybase: trading freshness for performance in a scalable database. In: Proceedings of the 7th ACM European conference on computer systems, ser EuroSysGoogle Scholar
  4. Gustafsson T, Hansson J (2004) Data management in real-time systems: a case of on-demand updates in vehicle control systems. In: Proceedings of the IEEE real-time and embedded technology and applications symposium, ser RTASGoogle Scholar
  5. Gustafsson T, Hansson J (2004) Dynamic on-demand updating of data in real-time database systems. In: Proceedings of the ACM symposium on applied computing, ser SACGoogle Scholar
  6. Hu S, Yao S, Jin H, Zhao Y, Hu Y, Liu X, Naghibolhosseini N, Li S, Kapoor A, Dron W, Su L, Bar-Noy A, Szekely P, Govindan R, Hobbs R, Abdelzaher TF (2015) Data acquisition for real-time decision-making under freshness constraints. In: RTSSGoogle Scholar
  7. Kang K-D, Son S, Stankovic J, Abdelzaher T (2002) A qos-sensitive approach for timeliness and freshness guarantees in real-time databases. In: Proceedings of the Euromicro conference on real-time systems, ser ECRTSGoogle Scholar
  8. Kang K-D, Son S, Stankovic J (2002) Star: secure real-time transaction processing with timeliness guarantees. In: Proceedings of the IEEE real-time systems symposium, ser RTSSGoogle Scholar
  9. Kang K-D, Son SH, Stankovic JA (2004) Managing deadline miss ratio and sensor data freshness in real-time databases. IEEE Trans Knowl Data Eng 16(10):1200–1216CrossRefGoogle Scholar
  10. Kao B, Lam K-Y, Adelberg B, Cheng R, Lee T (2003) Maintaining temporal consistency of discrete objects in soft real-time database systems. IEEE Trans Comput 52(3):373–389CrossRefGoogle Scholar
  11. Kim J-E (2017) Timing analysis in existing and emerging cyber physical systems. PhD dissertation, University of Illinois at Urbana-Champaign. http://hdl.handle.net/2142/97405
  12. Kim J-E, Abdelzaher T, Sha L, Bar-Noy A, Hobbs R (2016) Sporadic decision-centric data scheduling with normally-off sensors. In: Proc of IEEE int’l real-time systems symposium (RTSS)Google Scholar
  13. Kim J-E, Abdelzaher T, Sha L, Bar-Noy A, Hobbs R, Dron W (2016) On maximizing quality of information for the internet of things: a real-time scheduling perspective (Invited). In: Proc of IEEE int’l conference on embedded and real-time computing systems and applicationsGoogle Scholar
  14. Lee C-G, Kim Y-K, Son SH, Min SL, Kim CS (1996) Efficiently supporting hard/soft deadline transactions in real-time database systems. In: Proceedings of the third international workshop on real-time computing systems application, ser RTCSAGoogle Scholar
  15. Liu CL, Layland JW (1973) Scheduling algorithms for multiprogramming in a hard real-time environment. J ACM 20(1):46–61MathSciNetCrossRefzbMATHGoogle Scholar
  16. Qu H, Labrinidis A (2007) Preference-aware query and update scheduling in web-databases. In: Proceedings of the 23rd IEEE international conference on data engineeringGoogle Scholar
  17. Ramamritham K (1993) Real-time databases. Distrib Parallel Databases 1(2):199–226CrossRefGoogle Scholar
  18. Röhm U, Böhm K, Schek H-J, Schuldt H (2002) Fas: a freshness-sensitive coordination middleware for a cluster of olap components. In: Proceedings of the 28th international conference on very large data bases, ser VLDB ’02Google Scholar
  19. Song X, Liu JW-S (1995) Maintaining temporal consistency: pessimistic vs. optimitic concurrency control. IEEE Trans Knowl Data Eng 7(5):786–796CrossRefGoogle Scholar
  20. Xiong M, Ramamritham K (2004) Deriving deadlines and periods for real-time update transactions. IEEE Trans Comput 53(5):567–583CrossRefGoogle Scholar
  21. Xiong M, Han S, Lam K-Y (2005) A deferrable scheduling algorithm for real-time transactions maintaining data freshness. In: Proceedings of the IEEE international real-time systems symposium, ser RTSSGoogle Scholar
  22. Xiong M, Wang Q, Ramamritham K (2008) On earliest deadline first scheduling for temporal consistency maintenance. Real Time Syst 40(2):208–237CrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceYale UniversityNew HavenUSA
  2. 2.Department of Computer ScienceUniversity of Illinois at Urbana-ChampaignChampaignUSA
  3. 3.City University of New YorkNew YorkUSA
  4. 4.Multilingual Computing & Analytics Branch, U.S. Army Research LaboratoryAdelphiUSA
  5. 5.Raytheon BBN TechnologiesCambridgeUSA

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