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Techniques for general purpose optimization have been derived from the Metropolis Monte Carlo method of simulating the behavior of particles in substances as they are slowly cooled to form crystals. Simulated Annealing is such a derivative and its value for placement problems (e.g., in circuit board layout design) suggests that it could be advantageously applied to clustering tuples in databases in order to enhance responsiveness to queries. In this article we investigate this issue and compare the performance of this technique with a Graph-Collapsing clustering method which is known to perform very well, in order to gain insights into which approach is better for incorporation in a performanceoriented database design tool. We judge that, whilst the new method does give superior results to the graphbased method in many cases, these improvements are gained at such a very considerable expense of algorithm run time as to rule the new technique out of our consideration as a real world general purpose design tool (but perhaps not for some special-purpose databases).

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The clustering of objects in a layered object storage system is by common consent an exceedingly difficult problem. Studies the performance of three heuristic placement algorithms. A series of eight reasonably realistic case studies were used as a benchmark battery, and several hundred experiments were carried out to evaluate results of using the algorithms. Presents the results and the insights gained from the study.