Monitoring and Self-awareness for Heterogeneous, Adaptive Computing Systems

  • David KramerEmail author
  • Rainer Buchty
  • Wolfgang Karl
Part of the Autonomic Systems book series (ASYS, volume 1)


A comprehensive monitoring infrastructure is vital for upcoming heterogeneous, adaptive many-core systems. In order to enable required self-organising capabilities, a monitoring infrastructure has to provide self-awareness. Unfortunately, traditional approaches to monitoring, like hardware performance counters, lack required flexibility and are not suitable for self-organising systems.

We therefore present a flexible, hierarchical monitoring infrastructure for heterogeneous adaptive computing systems being able to provide a detailed and pristine view of the system state. On lower level, an associative counter array performs sustained monitoring of individual components of the system and provides this information to high level instances. These instances analyse and evaluate this information, and finally realise self-awareness. For this purpose, we employ a flexible, rule-based approach for runtime evaluation and classification of the system state. Further system instances, such as the task scheduler, may use the classified state as well as gathered information to realise self-x features, such as self-optimisation.


Self-awareness Monitoring Adaptive computing 


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  1. 1.
    HyperTransport™I/O Link Specification Revision 3.10 (2008).
  2. 2.
    Becker, J., Brändle, K., Brinkschulte, U., Henkel, J., Karl, W., Köster, T., Wenz, M., Wörn, H.: Digital on-demand computing organism for real-time systems. In: Karl, W., Becker, J., Großpietsch, K.-E., Hochberger, C., Maehle, E. (eds.) Workshop Proceedings of the 19th International Conference on Architecture of Computing Systems (ARCS’06). GI-Edition Lecture Notes in Informatics (LNI), vol. P81, pp. 230–245 (2006) Google Scholar
  3. 3.
    Buchty, R., Karl, W.: Design aspects of self-organizing heterogeneous multi-core architectures. In: Information Technology 5/2008 (Issue on Computer Architecture Challenges), pp. 293–299. Oldenbourg Wissenschaftsverlag, October 2008 Google Scholar
  4. 4.
    Buchty, R., Kicherer, M., Kramer, D., Karl, W.: An embrace-and-extend approach to managing the complexity of future heterogeneous systems. In: SAMOS ’09: Proceedings of the 9th International Workshop on Embedded Computer Systems: Architectures, Modeling, and Simulation, pp. 227–236. Springer, Berlin (2009) Google Scholar
  5. 5.
    Buchty, R., Kramer, D., Karl, W.: An organic computing approach to sustain-ed real-time monitoring. In: Proceedings of WCC2008/BICC (IFIP Vol. 268), pp. 151–162. Springer, Berlin (2008). ISBN 978-0-387-09654-4 Google Scholar
  6. 6.
    Kluge, F., Mische, J., Uhrig, S., Ungerer, T.: Building adaptive embedded systems by monitoring and dynamic loading of application module. In: Workshop on Adaptive and Reconfigurable Embedded Systems, St. Louis, MO, USA, April 2008 Google Scholar
  7. 7.
    Fröning, H., Nüessle, M., Slogsnat, D., Litz, H., Brüning, U.: The HTX-board: a rapid prototyping station. In: 3rd Annual FPGAWorld Conference (2006) Google Scholar
  8. 8.
    Guthaus, M.R., Ringenberg, J.S., Ernst, D., Austin, T.M., Mudge, T., Brown, R.B.: Mibench: A free, commercially representative embedded benchmark suite. In: Proceedings of the Workload Characterization, 2001. WWC-4. 2001 IEEE International Workshop, pp. 3–14. IEEE Comput. Soc., Washington, DC (2001) Google Scholar
  9. 9.
    Merkel, A., Bellosa, F.: Task activity vectors: a new metric for temperature-aware scheduling. In: Eurosys ’08: Proceedings of the 3rd ACM SIGOPS/EuroSys European Conference on Computer Systems 2008, pp. 1–12. ACM, New York (2008) Google Scholar
  10. 10.
    Mucci, P.J., Browne, S., Deane, C., Ho, G.: PAPI: A portable interface to hardware performance counters. In: Proceedings of the Department of Depense HPCMP User Group Conference (1999) Google Scholar
  11. 11.
    Müller-Schloer, C.: Organic computing: on the feasibility of controlled emergence. In: CODES+ISSS ’04: Proceedings of the 2nd IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis, pp. 2–5. ACM, New York (2004) Google Scholar
  12. 12.
    Nowak, F., Kicherer, M., Buchty, R., Karl, W.: Delivering guidance information in heterogeneous systems. In: Beigl, M., Cyzorla-Almeida, F.J. (eds.) ARCS 2010 Workshop Proceedings, pp. 95–101. VDE, February 2010 Google Scholar
  13. 13.
    Sprunt, B.: Pentium 4 performance-monitoring features. In: IEEE Micro, pp. 72–82 (2002) Google Scholar
  14. 14.
    Sprunt, B.: The basics of performance-monitoring hardware. In: IEEE Micro, pp. 64–71 (2002) Google Scholar
  15. 15.
    Trumler, W., Pietzowski, A., Satzger, B., Ungerer, T.: Adaptive self-optimization in distributed dynamic environments. In: International Conference on Self-Adaptive and Self-Organizing Systems, 320–323 (2007) Google Scholar
  16. 16.
    Zeppenfeld, J., Herkersdorf, A.: Autonomic workload management for multi-core processor systems. In: International Conference on Architecture of Computing Systems, ARCS, Hannover, Germany, pp. 49–60 (2010) Google Scholar

Copyright information

© Springer Basel AG 2011

Authors and Affiliations

  1. 1.Chair for Computer Architecture and Parallel Processing, Institute of Computer Science and EngineeringKarlsruhe Institute of Technology (KIT)KarlsruheGermany

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