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Stability of Online Resource Managers for Distributed Systems under Execution Time Variations

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Published:09 March 2015Publication History
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Abstract

Today's embedded systems are exposed to variations in resource usage due to complex software applications, hardware platforms, and impact of the runtime environments. When these variations are large and efficiency is required, on-line resource managers may be deployed on the system to help it control its resource usage. An often neglected problem is whether these resource managers are stable, meaning that the resource usage is controlled under all possible scenarios. In distributed systems, this problem is particularly hard because applications distributed over many resources generate complex dependencies between their resources. In this article, we develop a mathematical model of the system, and derive conditions that, if satisfied, guarantee stability.

References

  1. T. F. Abdelzaher, J. A. Stankovic, C. Lu, R. Zhang, and Y. Lu. 2003. Feedback performance control in software services—using a control-theoretic approach to achieve quality of service guarantees. IEEE Control Systems Magazine 23, 74--90.Google ScholarGoogle ScholarCross RefCross Ref
  2. K. J. Åström and B. Wittenmark. 1997. Computer-Controlled Systems. Prentice Hall. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. S. K. Baruah, L. E. Rosier, and R. R. Howell. 1990. Algorithms and complexity concerning the preemptive scheduling of periodic real-time tasks on one processor. Journal of Real-Time Systems 2, 4, 301--324. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. M. Bramson. Stability of Queueing Networks. Springer.Google ScholarGoogle Scholar
  5. G. C. Buttazzo, G. Lipari, and L. Albeni. Elastic task model for adaptive rate control. In Proceedings of the IEEE Real-Time Systems Symposium. 286. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. G. C. Buttazzo and L. Albeni. 2002. Adaptive workload management through elastic scheduling. Journal of Real-Time Systems 23, 7--24. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. G. C. Buttazzo, M. Velasco, P. Marti, and G. Fohler. 2004. Managing quality-of-control performance under overload conditions. In Proceedings of the Euromicro Conference on Real-Time Systems. 53--60. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. A. Cervin, J. Eker, B. Bernhardsson, and K. E. Årzén. 2002. Feedback-feedforward scheduling of control tasks. Journal of Real-Time Systems 23, 25--53. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. A. Cervin and J. Eker. 2003. The control server: A computational model for real-time control tasks. In Proceedings of the 15th Euromicro Conference on Real-Time Systems.Google ScholarGoogle Scholar
  10. T. Cucinotta and L. Palopoli. 2010a. QoS control for pipelines of tasks using multiple resources. IEEE Transactions on Computers 59, 416--430. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. T. Cucinotta, L. Palopoli, L. Abeni, D. Faggioli, and G. Lipari. 2010b. On the integration of application level and resource level QoS control for real-time applications. IEEE Transactions Industrial Informatics (TII) 6, 4, 479--491.Google ScholarGoogle ScholarCross RefCross Ref
  12. Z. P. Jiang, and Y. Wang. 2001. Input-to-state stability for discrete-time nonlinear systems. Automatica 37, 6, 857--869. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. E. Kreyszing. 1989. Introduction to Functional Analysis with Applications. John Wiley & Sons.Google ScholarGoogle Scholar
  14. P. R. Kumar, S. Meyn. 1995. Stability of queueing networks and scheduling policies. IEEE Transactions on Automatic Control 40, 2, 251--260.Google ScholarGoogle ScholarCross RefCross Ref
  15. C. Lee, J. Lehoczky, R. Rajkumar, and D. Siewiorek. 1998. On quality of service optimization with discrete QoS options. In Proceedings of the Real-Time Technology and Applications Symposium. 276. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. J. P. Lehoczky, L. Sha, and Y. Ding. 1989. The rate-monotonic scheduling algorithm: Exact characterization and average case behavior. In Proceedings of the 10th IEEE Real-Time Systems Symposium. 166--172.Google ScholarGoogle Scholar
  17. X. Liu, X. Zhu, P. Padala, Z. Wang, and S. Singhal. 2007. Optimal multivariate control for differentiated services on a shared hosting platform. In Proceedings of the Conference on Decision and Control. 3792--3799.Google ScholarGoogle Scholar
  18. C. Lu, J. A. Stankovic, S. H. Son, and G. Tao. 2002. Feedback control real-time scheduling: Framework, modeling, and algorithms. Journal of Real-Time Systems 23, 85--126. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. C. Lu, X. Wang and X. Koutsoukos. 2005. Feedback utilization control in distributed real-time systems with end-to-end tasks. IEEE Transactions on Parallel and Distributed Systems 16, 550--561. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. M. Marioni and G. C. Buttazzo. 2007. Elastic DVS management in processors with discrete voltage/frequency modes. IEEE Transactions on Industrial Informatics 3, 51--62.Google ScholarGoogle ScholarCross RefCross Ref
  21. L. Palopoli, T. Cucinotta, L. Marzario, and G. Lipari. 2009. AQuoSA—adaptive quality of service architecture. Journal of Software--Practice and Experience 39, 1--31. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. S. Rafiliu, P. Eles, and Z. Peng. 2010. Low overhead dynamic qos optimization under variable execution times. In Proceedings of 16th IEEE Embedded and Real-Time computing Systems and Applications (RTCSA). 293--303. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. S. Rafiliu, P. Eles, and Z. Peng. 2013. Stability of adaptive feedback-based resource managers for systems with execution time variations. Journal of Real-Time Systems 49, 367--400. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. E. D. Sontag. 2001. The ISS philosophy as a unifying framework for stability-like behavior. Lecture Notes in Control and Information Sciences 256, 443--467.Google ScholarGoogle ScholarCross RefCross Ref
  25. J. Yao, X. Liu, M. Yuan, and Z. Gu. 2008. Online adaptive utilization control for real-time embedded multiprocessor systems. In Proceedings of the International Conference on Hardware/Software Codesign and System Synthesis. 85--90. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. K. Zhou, J. C. Doyle, and K. Glover. 1996. Robust and Optimal Control. Prentice Hall, 40. Google ScholarGoogle ScholarDigital LibraryDigital Library

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