Measuring the quality of an artificial hormone system based task mapping

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Measuring the quality of an artificial hormone system based task mapping

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International Conference on Autonomic Computing and Communication Systems archive
Proceedings of the 2nd International Conference on Autonomic Computing and Communication Systems table of contents

Turin, Italy

Article No. 32

Year of Publication: 2008

ISBN:978-963-9799-34-9

Authors

Uwe Brinkschulte	University of Karlsruhe (TH), Germany
Alexander von Renteln	University of Karlsruhe (TH), Germany
Mathias Pacher	University of Karlsruhe (TH), Germany

Sponsors

: ICST
ACM: Association for Computing Machinery
: Create-Net

Publisher

ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering) ICST, Brussels, Belgium, Belgium

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ABSTRACT

The idea of Organic Computing is a trend to counter the problems arising from the fact that computing systems are getting smaller and smaller, and we will soon be surrounded by large numbers of little computers which will be hard to configure, maintain, and control.

We reintroduce an organic middleware - the artificial hormone system (AHS) which can map tasks onto a grid of heterogeneous processing elements while providing the system with self-X properties and even guaranteeing upper bounds for the self-configuration and self-healing.

This paper investigates the quality of task mappings on a grid of heterogeneous processing elements.

An algorithm is proposed to measure the quality of such task mappings. Experiments with randomly generated configurations will show results of mappings done by our artificial hormone system and compare them with ordinary load balancing.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	W. Becker. Dynamische adaptive Lastbalancierung für große, heterogen konkurrierende Anwendungen. Dissertation, Universität Stuttgart, Fakultät Informatik, Dezember 1995.
	2	L. F. Bittencourt, E. R. M. Madeira, F. R. L. Cicerre, and L. E. Buzato. A path clustering heuristic for scheduling task graphs onto a grid. In 3rd International Workshop on Middleware for Grid Computing (MGC05), Grenoble, France, 2005.
	3	U. Brinkschulte, M. Pacher, and A. von Renteln. Towards an artificial hormone system for self-organizing real-time task allocation. 5th IFIP Workshop on Software Technologies for Future Embedded & Ubiquitous Systems (SEUS), 2007.
	4	U. Brinkschulte, M. Pacher, and A. von Renteln. An artificial hormone system for self-organizing real-time task allocation in organic middleware. Organic Computing - Springer (ISBN: 978-3-540-77656-7), pages 261--284, Mar. 2008.
	5	Ewerson Carvalho , Ney Calazans , Fernando Moraes, Heuristics for Dynamic Task Mapping in NoC-based Heterogeneous MPSoCs, Proceedings of the 18th IEEE/IFIP International Workshop on Rapid System Prototyping, p.34-40, May 28-30, 2007 [doi>10.1109/RSP.2007.26]
	6	T. Decker, R. Diekmann, R. Lüling, and B. Monien. Universelles dynamisches task-mapping. In Konferenzband des PARS'95 Workshops in Stuttgart, PARS-Mitteilung 14, pages 122--131, 1995.
	7	J. Finke, K. M. Passino, and A. Sparks. Cooperative control via task load balancing for networked uninhabited autonomous vehicles. In 42nd IEEE Conference onDecision and Control, 2003. Proceedings, volume 1, pages 31--36, 2003.
	8	J. Finke, K. M. Passino, and A. Sparks. Stable task load balancing strategies for cooperative control of networked autonomous air vehicles. In IEEE Transactions on Control Systems Technology, volume 14, pages 789--803, 2006.
	9	H.-U. Heiss and M. Schmitz. Decentralized dynamic load balancing: The particles approach. In Proc. 8th Int. Symp. on Computer and Information Sciences, Istanbul, Turkey, November 1993.
	10	P. I. R. Horn. Autonomic computing manifesto: IBM's perspective on the state of information technology, October 2001.
	11	C. Mueller-Schloer, C. von der Malsburg, and R. P. Wuertz. Organic computing. Informatik Spektrum, 27(4):332--336, 2004.
	12	Andrei Radulescu , Arjan J. C. Van Gemund, Fast and Effective Task Scheduling in Heterogeneous Systems, Proceedings of the 9th Heterogeneous Computing Workshop, p.229, May 01-01, 2000
	13	Larry Rudolph , Miriam Slivkin-Allalouf , Eli Upfal, A simple load balancing scheme for task allocation in parallel machines, Proceedings of the third annual ACM symposium on Parallel algorithms and architectures, p.237-245, July 21-24, 1991, Hilton Head, South Carolina, United States [doi>10.1145/113379.113401]
	14	W. Trumler, T. Thiemann, and T. Ungerer. An artificial hormone system for self-organization of networked nodes. In Biologically inspired Cooperative Computing, IFIP 19th World Computer Congress 2006, Santiago de Chile, Chile, 2006.
	15	VDE/ITG/GI. VDE/ITG/GI-Positionspapier Organic Computing: Computer und Systemarchitektur im Jahr 2010, 2003.
	16	C. Xu and F. Lau. Decentralized remapping of data parallel computations with the generalized dimension exchange method. In Proceedings of Scalable High-Performance Computing Conference, pages 414--421, 1994.