Synopses & Reviews
The architectural concept of a memory hierarchy has been immensely successful, making possible today's spectacular pace of technology evolution in both the volume of data and the speed of data access. Its success is difficult to understand, however, when examined within the traditional "memoryless" framework of performance analysis. The `memoryless' framework cannot properly reflect a memory hierarchy's ability to take advantage of patterns of data use that are transient. The Fractal Structure of Data Reference: Applications to the Memory Hierarchy both introduces, and justifies empirically, an alternative modeling framework in which arrivals are driven by a statistically self-similar underlying process, and are transient in nature. The substance of this book comes from the ability of the model to impose a mathematically tractable structure on important problems involving the operation and performance of a memory hierarchy. It describes events as they play out at a wide range of time scales, from the operation of file buffers and storage control cache, to a statistical view of entire disk storage applications. Striking insights are obtained about how memory hierarchies work, and how to exploit them to best advantage. The emphasis is on the practical application of such results. The Fractal Structure of Data Reference: Applications to the Memory Hierarchy will be of interest to professionals working in the area of applied computer performance and capacity planning, particularly those with a focus on disk storage. The book is also an excellent reference for those interested in database and data structure research.
Table of Contents
List of Figures. List of Tables. Preface. Acknowledgments. 1. Hierarchical Reuse Model. 2. Hierarchical Reuse Daemon. 3. Use of Memory by Multiple Workloads. 4. Use of Memory at the I/O Interface. 5. Memory Management in an LRU Cache. 6. Free Space Collection in a Log. 7. Transient and Persistent Data Access. 8. Hierarchical Storage Management. 9. Disk Applications: A Statistical View. References. Index.