Computing

, Volume 95, Issue 10–11, pp 993–1018 | Cite as

An effective fixed priority co-scheduling algorithm for periodic update and application transactions

  • Jian-Tao Wang
  • Kam-Yiu Lam
  • Song Han
  • Sang H. Son
  • Aloysius K. Mok
Article
First Online:
Received:
Accepted:

Abstract

An important function of many cyber-physical systems (CPS) is to provide a close monitoring of the operation environment to be able to adapt to changing situations effectively. One of the commonly applied techniques for that is to invoke time-constrained periodic application transactions to check the status of the operation environment. The status of the environment is represented by the values of the physical entities in the operation environment which are maintained as real-time data objects in a real-time database. Unfortunately, meeting the deadlines of application transactions and maintaining the quality of real-time data objects are conflicting with each other, because they compete for the same computation resources. To address this problem of update and application transactions co-scheduling problem, in this paper, we propose a fixed priority co-scheduling algorithm called periodic co-scheduling (PCS). PCS uses periodic update transactions to maintain the temporal validity of real-time data objects. It judiciously decides the priority orders among all the update and application transactions so that the constructed schedule can satisfy the deadline constraints of all the application transactions and at the same time maximize the qualities of the real-time data objects to ensure the correct execution of application transactions. The effectiveness of the algorithm is validated through extensive simulation experiments.

Keywords

Co-scheduling Real-time data monitoring Quality of data (QoD) Critical events Cyber-physical systems 

Mathematics Subject Classification

68M20 
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References

  1. 1.
    Ahmed Q, Vrbsky S (2000) Triggered updates for temporal consistency in real-time databases. Real-Time Syst 19(3):209–243CrossRefGoogle Scholar
  2. 2.
    AL-Khalidi H, Taniar D, Safar M (2012) Approximate algorithms for static and continuous range queries in mobile navigation. Computing, 1–28. doi: 10.1007/s00607-012-0219-7
  3. 3.
    Al-Khateeb A, Rashid N, Abdullah R (2012) An enhanced meta-scheduling system for grid computing that considers the job type and priority. Computing 94(5):389–410CrossRefGoogle Scholar
  4. 4.
    Amirijoo M et al (2006) Specification and management of QoS in real-time databases supporting imprecise computations. IEEE Trans Comput 55(3):304–319CrossRefGoogle Scholar
  5. 5.
    Baruah SK, Mok AK, Rosier LE (1990) Preemptively scheduling hard-real-time sporadic tasks on one processor. In: Proceedings of IEEE real-time systems symposium, 5–7 Dec 1990, pp 182–190Google Scholar
  6. 6.
    Bateni M, Golab L, Hajiaghayi M, Karloff H (2009) Scheduling to minimize staleness and stretch in real-time data warehouses. In: Proceedings of the annual symposium on parallelism in algorithms and architectures, New York, USA, pp 29–38Google Scholar
  7. 7.
    Golab L, Johnson T, Shkapenyuk V (2009) Scheduling updates in a real-time stream warehouse. In: Proceedings of IEEE international conference on data engineering, Shanghai, China, 29 Mar–2 Apr 2009, pp 1207–1210Google Scholar
  8. 8.
    González-Valenzuela S, Chen M, Leung V (2011) Mobility support for health monitoring at home using wearable sensors. IEEE Tran Inf Technol Biomed 15(4):539–549CrossRefGoogle Scholar
  9. 9.
    Gustafsson T, Hansson J (2004) Data management in real-time systems: a case of on-demand updates in vehicle control systems. In: Proceedings of IEEE real-time and embedded technology and applications symposium, 28 May 2004, pp 182–191Google Scholar
  10. 10.
    Han S, Song J, Zhu X, Mok AK, Chen D, Nixon M, Pratt W, Gondhalekar V (2009) Wi-HTest: compliance test suite for diagnosing devices in real-time WirelessHART network. In: Proceedings of IEEE real-time and embedded technology and applications symposium, San Francisco, CA, pp 327–336Google Scholar
  11. 11.
    Han S, Mok AK, Meng J, Wei YH, Zhu X, Sentis L, Kim KS, Miikkulainen R, Menashe J (2012) Architecture of a cyberphysical avatar. UTCS technical report \(\#\)TR-12-12Google Scholar
  12. 12.
    Ho SJ, Kuo TW, Mok AK (1997) Similarity-based load adjustment for real-time data-intensive applications. In: Proceedings of IEEE real-time systems symposium, 5 Dec 1997, pp 144–153Google Scholar
  13. 13.
    Kang KD, 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
  14. 14.
    Kang KD, Oh J, Son SH (2007) Chronos: Feedback control of a real database system performance. In: Proceedings of IEEE real-time systems symposium, Tucson, Arizona, USA, 3–6 Dec 2007, pp 267–276Google Scholar
  15. 15.
    Kang W, Son SH, Stankovic JA (2009) QeDB: A quality-aware embedded real-time database. In: Proceedings of the IEEE real-time and embedded technology and applications symposium, San Francisco, CA, USA, 13–16 Apr 2009, pp 108–117Google Scholar
  16. 16.
    Ko J, Lu C, Srivastava M, Stankovic J, Terzis A, Welsh M (2010) Wireless sensor networks for healthcare. Proc IEEE 98(11):1947–1960CrossRefGoogle Scholar
  17. 17.
    Kulkarni D, Ravishankar C, Cherniack M (2008) Real-time, load-adaptive processing of continuous queries over data streams. In: Proceedings of the international conference on distributed event-based systems, DEBS 2008, Rome, Italy, July 1-4 2008, pp 277–288Google Scholar
  18. 18.
    Kuo TW, Mok AK (1993) SSP: A semantics-based protocol for real-time data access. In: Proceedings of IEEE real-time systems symposium, Dec 1993, pp 76–86Google Scholar
  19. 19.
    Labrinidis A, Roussopoulos N (2001) Update propagation strategies for improving the quality of data on the Web. In: Proceedings of the international conference on very large data bases, Rome, Italy, Sept 2001, pp 391–400Google Scholar
  20. 20.
    Li M, Liu Y (2009) Underground coal mine monitoring with wireless sensor networks. ACM Trans Sens Netw 5(2):10:1–10:29CrossRefGoogle Scholar
  21. 21.
    Liu C, Layland J (1973) Scheduling algorithms for multiprogramming in a hard real-time environment. J ACM 20(1):46–61MathSciNetCrossRefMATHGoogle Scholar
  22. 22.
    Mok AK (1983) Fundamental design problems of distributed systems for the hard-real-time environment. PhD thesis, MIT, Cambridge, MassachusettsGoogle Scholar
  23. 23.
    Qu H, Labrinidis A (2007) Preference-aware query and update scheduling in web-databases. In: Proceedings of IEEE International Conference on Data Engineering, Istanbul, Turkey, 15–20 Apr 2007, pp 356–365Google Scholar
  24. 24.
    Ramamritham K (1993) Real-time databases. Distrib Parallel Databases 1(2):199–226Google Scholar
  25. 25.
    Ramamritham K, Son SH, Dipippo LC (2004) Real-time databases and data services. Real-Time Syst 28(2):179–215CrossRefMATHGoogle Scholar
  26. 26.
    Rauh A, Kersten J, Auer E, Aschemann H (2012) Sensitivity-based feedforward and feedback control for uncertain systems. Computing 94(2–4):357–367MathSciNetCrossRefMATHGoogle Scholar
  27. 27.
    Sha L, Rajkumar R, Lehoczky JP (1990) Priority inheritance protocols: an approach to real-time synchronization. IEEE Trans Comput 39(9):1175–1185MathSciNetCrossRefGoogle Scholar
  28. 28.
    Shanker U, Misra M, Sarje AK (2008) Distributed real time database systems: background and literature review. Int J Distrib Parallel Databases 23(2):127–149CrossRefGoogle Scholar
  29. 29.
    Song J, Han S, Mok AK, Chen D, Lucas M, Nixon M, Pratt W (2008) WirelessHART: Applying wireless technology in real-time industrial process control. In: Proceedings of the 14th IEEE real-time and embedded technology and applications symposium, RTAS 2008, 22–24 Apr 2008, St. Louis, Missouri, USAGoogle Scholar
  30. 30.
    Sprunt B, Sha L, Lehoczky J (1989) Scheduling sporadic and aperiodic events in a hard real-time system. Technical Report ESD-TR-89-19, Carnegie Mellon University, Pittsburgh, USAGoogle Scholar
  31. 31.
    Thiele M, Bader A, Lehner W (2009) Multi-objective scheduling for real-time data warehouses. Comput Sci Res Dev 24(3):137–151CrossRefGoogle Scholar
  32. 32.
    Xiong M, Ramamritham K (2004) Deriving deadlines and periods for real-time update transactions. IEEE Trans Comput 53(5):567–583CrossRefGoogle Scholar
  33. 33.
    Xiong M, Han S, Lam KY (2005) A deferrable scheduling algorithm for real-time transactions maintaining data freshness. In: Proceedings of the IEEE real-time systems symposium, (RTSS 2005), Miami, FL, USA, 6–8 Dec 2005, pp 27–37Google Scholar
  34. 34.
    Xiong M, Han S, Chen D (2006) Deferrable scheduling for temporal consistency: schedulability analysis and overhead reduction. In: Proceedings of the IEEE conference on embedded and real-time computing systems and applications, Sydney, Australia, 16–18 Aug 2006, pp 117–124Google Scholar
  35. 35.
    Xiong M, Han S, Lam KY, Chen D (2008) Deferrable scheduling for maintaining real-time data freshness: algorithms, analysis, and results. IEEE Trans Comput 57(7):952–964MathSciNetCrossRefGoogle Scholar
  36. 36.
    Xiong M, Wang Q, Ramamritham K (2008) On earliest deadline first scheduling for temporal consistency maintenance. Real-Time Syst 40(2):208–237CrossRefMATHGoogle Scholar
  37. 37.
    Xiong M, Han S, Chen D, Lam KY, Feng S (2010) DESH: overhead reduction algorithms for deferrable scheduling. Real-Time Syst 44(1):1–25CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Wien 2012

Authors and Affiliations

  • Jian-Tao Wang
    • 1
  • Kam-Yiu Lam
    • 1
  • Song Han
    • 2
  • Sang H. Son
    • 3
  • Aloysius K. Mok
    • 2
  1. 1.Department of Computer ScienceCity University of Hong KongHong KongPeople’s Republic of China
  2. 2.Department of Computer ScienceUniversity of Texas at AustinAustinUSA
  3. 3.Department of Computer ScienceUniversity of VirginiaCharlottesvilleUSA

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