A Self-Adaptive Database Buffer Replacement Scheme.

The overall performance of a database system is very sensitive to the buffer replacement algorithm used. However, the performance evaluation of database buffer replacement algorithms commonly assumes that database accesses are independent and the probability for each individual database record to be accessed is fixed. Due to these rigid assumptions, the results of performance evaluation are not always reliable. In this dissertation, we apply Simon\u27s model of information accessing to model database accessing frequencies. This approach relaxes the independent assumption, and since it also allows certain dynamic behavior in accessing frequencies; thus, it is more robust and preferable over the traditional artificial data approach. Furthermore, taking advantage of the conceptual similarity between the self-organizing linear search heuristics and the traditional buffer replacement algorithms, we propose a self-adaptive buffer replacement scheme that outperforms conventional database buffer replacement algorithms. The findings of our study can be further applied to many other computer applications, e.g. the more complex problem of archival storage design in larger database systems

Self-Organizing doubly-linked lists

In this paper, we study the problem of maintaining a doubly-linked list (DLL) in approximately optimal order, with respect to the mean search time. We study two types of DLL reorganization strategies. Move-To-End (MTE) [12] and SWAP [14] are two memoryless DLL heuristics obtained from natural extensions of the well-known singly-linked-list (SLL) heuristics, move-to-front and transposition, respectively. We first derive a general sufficient condition which permits comparison of any two DLL heuristics. We use this condition as a guideline to identify families of access distributions for which SWAP yields a lower expected cost than the MTE. We have also presented an absorbing DLL heuristic. The strategy requires one additional memory location and is analogous to the scheme presented in [15]. The reorganization is achieved by moving each element exactly once to its final position in the reorganized list. The scheme is stochastically absorbing and it is shown to be optimal for a restricted family of distributions. Thus, for these distributions, the probability of the scheme converging to the optimal list order can be made as close to unity as desired