Real-Time Systems

, Volume 39, Issue 1–3, pp 237–282

Hierarchical control of multiple resources in distributed real-time and embedded systems

  • Nishanth Shankaran
  • Xenofon D. Koutsoukos
  • Douglas C. Schmidt
  • Yuan Xue
  • Chenyang Lu
Article

Abstract

Real-time and embedded systems have traditionally been designed for closed environments where operating conditions, input workloads, and resource availability are known a priori, and are subject to little or no change at runtime. There is increasing demand, however, for adaptive capabilities in distributed real-time and embedded (DRE) systems that execute in open environments where system operational conditions, input workload, and resource availability cannot be characterized accurately a priori. A challenging problem faced by researchers and developers of such systems is devising effective adaptive resource management strategies that can meet end-to-end quality of service (QoS) requirements of applications. To address key resource management challenges of open DRE systems, this paper presents the Hierarchical Distributed Resource-management Architecture (HiDRA), which provides adaptive resource management using control techniques that adapt to workload fluctuations and resource availability for both bandwidth and processor utilization simultaneously.

This paper presents three contributions to research in adaptive resource management for DRE systems. First, we describe the structure and functionality of HiDRA. Second, we present an analytical model of HiDRA that formalizes its control-theoretic behavior and presents analytical assurance of system performance. Third, we evaluate the performance of HiDRA via experiments on a representative DRE system that performs real-time distributed target tracking. Our analytical and empirical results indicate that HiDRA yields predictable, stable, and efficient system performance, even in the face of changing workload and resource availability.

Keywords

Distributed systems Real-time systems Embedded systems Adaptive systems Quality of service Hierarchical control 

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References

  1. Abdelzaher TF, Stankovic J, Lu C, Zhang R, Lu Y (2003) Feedback performance control in software services. IEEE Control Syst 23(3):74–90 CrossRefGoogle Scholar
  2. Abeni L, Buttazzo G (2001) Hierarchical QoS management for time sensitive applications. In: Proceedings of the seventh real-time technology and applications symposium (RTAS). IEEE Computer Society, Washington, p 63 CrossRefGoogle Scholar
  3. Astrom KJ, Wittenmark B (1990) Computer-controlled systems: theory and design, 2nd edn. Prentice-Hall, Englewood Cliffs Google Scholar
  4. Bianchi G (2000) Performance analysis of the IEEE 802.11 distributed coordination function. IEEE J Sel Areas Commun 18(1–2):535–547 CrossRefGoogle Scholar
  5. Boyer SA (1993) Supervisory control and data acquisition. ISA Google Scholar
  6. Brandt S, Nutt G, Berk T, Mankovich J (1998) A dynamic quality of service middleware agent for mediating application resource usage. In: RTSS ’98: proceedings of the IEEE real-time systems symposium. IEEE Computer Society, Washington p 307 Google Scholar
  7. Carlson R (2002) High-security SCADA LDRD final report. Tech. rep., Advanced Information and Control Systems Department, Sandia National Laboratories, Albuquerque, NM Google Scholar
  8. Corman D (2001) WSOA-weapon systems open architecture demonstration-using emerging open system architecture standards to enable innovative techniques for time critical target (TCT) prosecution. In: Proceedings of the 20th IEEE/AIAA digital avionics systems conference (DASC) Google Scholar
  9. Corman D, Gossett J, Noll D (2002) Experiences in a distributed real-time avionics domain. In: Proceedings of the international symposium on object-oriented real-time distributed computing (ISORC), IEEE/IFIP, Washington Google Scholar
  10. CORPORATE Computer Science and Telecommunications Board (1992) Keeping the US computer industry competitive: systems integration, National Academy, Washington Google Scholar
  11. Cucinotta T, Palopoli L, Marzario L, Lipari G, Abeni L (2004) Adaptive reservations in a Linux environment. In: IEEE real-time and embedded technology and applications symposium, pp 238–245 Google Scholar
  12. Dellaert F, Thorpe C (1997) Robust car tracking using Kalman filtering and Bayesian templates. In: Conference on intelligent transportation systems Google Scholar
  13. Fernandez JD, Fernandez AE (2005) SCADA Systems: Vulnerabilities and Remediation. J Comput Small Coll 20(4):160–168 Google Scholar
  14. Franklin GF, Powell JD, Workman M (1997) Digital Control of Dynamic Systems, 3rd edn. Addison–Wesley, Reading Google Scholar
  15. Holland G, Vaidya N, Bahl P (2001) A rate-adaptive MAC protocol for multi-hop wireless networks. In: MobiCom ’01: proceedings of the 7th annual international conference on mobile computing and networking. ACM, New York, pp. 236–251 CrossRefGoogle Scholar
  16. IEEE Std 802.11-1997 Information Technology (1997) Telecommunications and information exchange between systems, local and metropolitan area networks, specific requirements, part 11: wireless lan medium access control (MAC) and physical layer (PHY) specifications. IEEE Computer Society, New York Google Scholar
  17. Institute SE (2006) Ultra-large-scale systems: software challenge of the future. Tech. rep., Carnegie Mellon University, Pittsburgh, PA Google Scholar
  18. Koutsoukos X, Tekumalla R, Natarajan B, Lu C (2005) Hybrid supervisory control of real-time systems. In: 11th IEEE real-time and embedded technology and applications symposium, San Francisco Google Scholar
  19. Lehoczky J, Sha L, Ding Y (1989) The rate monotonic scheduling algorithm: exact characterization and average case behavior. In: Proceedings of the 10th IEEE real-time systems symposium (RTSS 1989). IEEE Computer Society, Los Alamitos, pp 166–171 Google Scholar
  20. Li B, Nahrstedt K (1999) A control-based middleware framework for QoS adaptations. IEEE J Sel Areas Commun 17(9):1632–1650 CrossRefGoogle Scholar
  21. Lipari G, Lamastra G, Abeni L (2004) Task synchronization in reservation-based real-time systems. IEEE Trans Comput 53(12):1591–1601 CrossRefGoogle Scholar
  22. Liu C, Layland J (1973) Scheduling algorithms for multiprogramming in a hard-real-time environment. JACM 20(1):46–61 MATHCrossRefMathSciNetGoogle Scholar
  23. Loyall JP, Schantz RE, Corman D, Paunicka JL, Fernandez S (2005) A distributed real-time embedded application for surveillance, detection, and tracking of time critical targets. In: IEEE real-time and embedded technology and applications symposium, pp 88–97 Google Scholar
  24. Lu C, Stankovic JA, Son SH, Tao G (2002) Feedback control real-time scheduling: framework, modeling, and algorithms. Real-Time Syst 23(1-2):85–126 MATHCrossRefGoogle Scholar
  25. Lu C, Wang X, Gill C (2003) Feedback control real-time scheduling in ORB middleware. In: Proceedings of the 9th IEEE real-time and embedded technology and applications symposium (RTAS). IEEE, Washington Google Scholar
  26. Mills D (1988) The network time protocol. In: RFC 1059, Network Working Group Google Scholar
  27. Object Management Group (2002) Real-time CORBA specification, OMG Document formal/05-01-04 edition Google Scholar
  28. Schmidt DC, Levine DL, Mungee S (1998) The design and performance of real-time object request brokers. Comput Commun 21(4):294–324 CrossRefGoogle Scholar
  29. Schmidt DC, Schantz R, Masters M, Cross J, Sharp D, DiPalma L (2001) Towards adaptive and reflective middleware for network-centric combat systems. J Defense Softw Eng Google Scholar
  30. Shah SH, Chen K, Nahrstedt K (2005) Dynamic bandwidth management for single-hop ad hoc wireless networks. Mob Netw Appl 10(1-2):199–217 CrossRefGoogle Scholar
  31. Sharma P, Loyall J, Heineman G, Schantz R, Shapiro R, Duzan G (2004) Component-based dynamic QoS adaptations in distributed real-time and embedded systems. In: Proceedings of the International Symposium on Distributed Objects and Applications (DOA’04), Agia Napa, Cyprus Google Scholar
  32. Wallace GK (1991) The JPEG still image compression standard. Commun ACM 34(4):30–44 CrossRefGoogle Scholar
  33. Wang X, Huang H-M, Subramonian V, Lu C, Gill C (2004) CAMRIT: control-based adaptive middleware for real-time image transmission. In: Proceeding of the 10th IEEE real-time and embedded technology and applications symposium (RTAS), Toronto, Canada Google Scholar
  34. Welch G, Bishop G (2001) An introduction to the Kalman filter: course 8. In: Computer graphics, annual conference on computer graphics and interactive techniques, SIGGRAPH. ACM, Los Angeles Google Scholar
  35. White B, et al. (2002) An integrated experimental environment for distributed systems and networks. In: Proceedings of the fifth symposium on operating systems design and implement. USENIX Association, Boston, pp 255–270 Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Nishanth Shankaran
    • 1
  • Xenofon D. Koutsoukos
    • 1
  • Douglas C. Schmidt
    • 1
  • Yuan Xue
    • 1
  • Chenyang Lu
    • 2
  1. 1.Department of Electrical Engineering and Computer Science, Institute for Software Integrated Systems (ISIS)Vanderbilt UniversityNashvilleUSA
  2. 2.Department of Computer Science and EngineeringWashington University in St. LouisSt. LouisUSA

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