Abstract
With the increasing size and complexity, adaptability is among the most badly needed properties in today’s Grid systems. Adaptability refers to the degree to which adjustments in practices, processes, or structures of systems are possible to projected or actual changes of their environment.
In this paper, we review concepts, methods, algorithms, and implementations that are deemed useful for designing adaptable Grid systems, illustrating them with examples. Contrary to the existing literature, the portfolio of the proposed approaches includes unorthodox tools such as game theory. We also discusses methods which have not been fully exploited for purposes of adaptability, such as automated planning or time series analysis. Our inventory is done along the stages of the feedback loop known from control theory. These stages include monitoring, analyzing, predicting, planning, decision taking, and finally executing the plan.
Our discussion reveals that several of the problems paving the way to fully adaptable system are of fundamental nature, which makes a ‘quantum leap’ progress in this area unlikely.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
A. Andrzejak and M. Ceyran. Characterizing and Predicting Resource Demand by Periodicity Mining. Journal of Network and System Management, special issue on Self-Managing Systems and Networks, Vol. 13, No. 1, Mar 2005.
A. Andrzejak, J. Rolia, and M. Arlitt. Bounding the Resource Savings of Several Utility Computing Models for a Data Center. HPL Technical Report HPL-2002–339, Hewlett-Packard Laboratories Palo Alto, December 2002.
A. Andrzejak, U. Hermann, and A. Sahai. Feedbackflow-An Adaptive Workflow Generator for System Management, 2nd IEEE International Conference on Autonomic Computing (ICAC-05), 2005.
D. Bernard, E. Gamble, N. Rouquette, B. Smith, Y. Tung, N. Muscetola, G. Dorias, B. Kanefsky, J. Kurien, W. Millar, P. Nayak, and K. Rajan, Remote Agent Experiment. DS1 Technology Validation Report. NASA Ames and JPL report, 1998.
M. Broy and R. Steinbrüggen. Modellbildung in der Informatik. Springer-Verlag, Berlin, 2004, ISBN 3-540-44292-8.
G. Candea, A.B. Brown, A. Fox, and D. Patterson. Recovery-oriented computing: Building multitier dependability. IEEE Computer, Nov. 2004, pp. 60–67.
A. Colmerauer and P. Roussel, The Birth of Prolog. 2. SIGPLAN conference on History of Programming Languages, 1993, pp 37–52.
N. Damianou, A. K. Bandara, M. Sloman, and E. C. Lupu. A Survey of Policy Specification Approaches., April 2002.
N. Damianou, N. Dulay, et al. The Ponder Policy Specification Language. Policy 2001: Workshop on Policies for Distributed Systems and Networks, Bristol, UK, Springer-Verlag, 2001.
Distributed Management Task Force (DMTF). DMTF CIM Concepts White Paper. http://www.dmtf.org/standards/published_documents.php
S. Dolev. Self-Stabilization. MIT Press, Cambridge MA, 2000.
J. Fischer and E. Holz. SDL-2000 Tutorial. SAM 2000 Workshop Grenoble, 2000.
P. A. Flach and N. Lachiche. Confirmation-Guided Discovery of first-order rules with Tertius. Machine Learning, 42, 1999, pp. 61–95.
M. Fox and D. Long, PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains. Journal of Artificial Intelligence Research, vol. 20, 2003, pp. 61–124.
M. Ghallab, D. Nau, and P. Traverso Automated Planning — theory and practice. Morgan Kaufmann Publishers, 2004, ISBN 1-55860-856-7.
T. Glad and L. Ljung. Control Theory: Multivariable and Nonlinear Methods. CRC Press, June 2000.
D. A. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Company, Inc., 1989.
J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 2001.
International Telecommunication Union (ITU). Specification and description language (SDL). TU-T Recommendation Z. 100, August 2002.
The Internet Society. RFC 3198 — Terminology for Policy-Based Management. 2001.
M. Karlsson and C. Karamanolis. Choosing Replica Placement Heuristics for Wide-Area Systems. Int. Conf. on Distributed Computing Systems (ICDCS), March 2004, Tokyo, Japan, pp. 350–359.
J.O. Kephart and D.M. Chess. The vision of autonomic computing. IEEE Computer, Jan. 2003, pp. 41–50.
S. Makridakis, S. C. Wheelwright, and R. J. Hyndman. Forecasting — Methods and Applications. 3rd edition, John Wiley & Sons, Inc., 1999.
O. Morgenstern and J. v. Neumann. The Theory of Games and Economic Behaviour. 1944.
J. Nash. Equilibrium Points in N-Person Games. Procs. of the National Academy of Sciences, 36, 1950, 48–49.
J. v. Neumann. Zur Theorie der Gesellschaftsspiele. Mathematische Annalen, vol. 100, 295–320, 1928.
J. Reason. Human Error. Cambridge University Press, 1990.
A. Reinefeld, F. Schintke, and T. Schütt. Scalable and Self-Optimizing Data Grids. Chapter 2 (pp. 30–60) in: Yuen Chung Kwong (ed.), Annual Review of Scalable Computing, vol. 6, June 2004.
T. Röblitz et al. Autonomic Management of Large Clusters and their Integration into the Grid. J. of Grid Computing, 2(3):247–260, September 2004.
A. Turing. On Computable Numbers, with an application to the Entscheidungsproblem. Proceedings London Mathematical Society (series 2) vol 42, 1936, pp.230–265.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer Science+Business Media, Inc.
About this chapter
Cite this chapter
Andrzejak, A., Reinefeld, A., Schintke, F., Schütt, T. (2006). On Adaptability in Grid Systems. In: Getov, V., Laforenza, D., Reinefeld, A. (eds) Future Generation Grids. Springer, Boston, MA . https://doi.org/10.1007/978-0-387-29445-2_2
Download citation
DOI: https://doi.org/10.1007/978-0-387-29445-2_2
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-27935-0
Online ISBN: 978-0-387-29445-2
eBook Packages: Computer ScienceComputer Science (R0)