On Adaptability in Grid Systems
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.
Keywordsadaptability non-functional properties autonomic computing decentralized service architecture
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- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- G. Candea, A.B. Brown, A. Fox, and D. Patterson. Recovery-oriented computing: Building multitier dependability. IEEE Computer, Nov. 2004, pp. 60–67.Google Scholar
- A. Colmerauer and P. Roussel, The Birth of Prolog. 2. SIGPLAN conference on History of Programming Languages, 1993, pp 37–52.Google Scholar
- N. Damianou, A. K. Bandara, M. Sloman, and E. C. Lupu. A Survey of Policy Specification Approaches., April 2002.Google Scholar
- 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.Google Scholar
- Distributed Management Task Force (DMTF). DMTF CIM Concepts White Paper. http://www.dmtf.org/standards/published_documents.phpGoogle Scholar
- J. Fischer and E. Holz. SDL-2000 Tutorial. SAM 2000 Workshop Grenoble, 2000.Google Scholar
- M. Ghallab, D. Nau, and P. Traverso Automated Planning — theory and practice. Morgan Kaufmann Publishers, 2004, ISBN 1-55860-856-7.Google Scholar
- T. Glad and L. Ljung. Control Theory: Multivariable and Nonlinear Methods. CRC Press, June 2000.Google Scholar
- D. A. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Company, Inc., 1989.Google Scholar
- J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 2001.Google Scholar
- International Telecommunication Union (ITU). Specification and description language (SDL). TU-T Recommendation Z. 100, August 2002.Google Scholar
- The Internet Society. RFC 3198 — Terminology for Policy-Based Management. 2001.Google Scholar
- 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.Google Scholar
- J.O. Kephart and D.M. Chess. The vision of autonomic computing. IEEE Computer, Jan. 2003, pp. 41–50.Google Scholar
- S. Makridakis, S. C. Wheelwright, and R. J. Hyndman. Forecasting — Methods and Applications. 3rd edition, John Wiley & Sons, Inc., 1999.Google Scholar
- O. Morgenstern and J. v. Neumann. The Theory of Games and Economic Behaviour. 1944.Google Scholar
- J. Reason. Human Error. Cambridge University Press, 1990.Google Scholar
- 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.Google Scholar