The performance of many object-oriented database applications suffers from the page fetch latency which is determined by the expense of disk access. In this work we suggest several prefetching techniques to avoid, or at least to reduce, page fetch latency. In practice no prediction technique is perfect and no prefetching technique can entirely eliminate delay due to page fetch latency. Therefore we are interested in the trade-off between the level of accuracy required for obtaining good results in terms of elapsed time reduction and the processing overhead needed to achieve this level of accuracy. If prefetching accuracy is high then the total elapsed time of an application can be reduced significantly otherwise if the prefetching accuracy is low, many incorrect pages are prefetched and the extra load on the client, network, server and disks decreases the whole system performance. Access pattern of object-oriented databases are often complex and usually hard to predict accurately. The ...

We present a new approach to predicting page accesses to facilitate prefetching persistent objects in a client/server computing environment. The relationships between persistent objects are modelled by a discrete-time Markov Chain, which allowed us to use a method called hitting times to compute the page access probability and the mean time to access a page. If the probability of a page is higher than a threshold defined by cost/benefit parameters then the page is a candidate for prefetching. To determine the prefetching threshold we consider various cost parameters to compare the benefit of a correct prefetch with the cost of an incorrect prefetch. In addition, we compute the best possible time to start the prefetch operation. We incorporated this prefetching algorithm into the EXODUS storage manager, and used the timing results in a simulation. Keywords: prefetching, persistent object stores, object-oriented databases, discrete-time Markov Chains, hitting times and hitting probabil...

this paper, we examine the way in which prefetching can exploit parallelism. Prefetching has been studied before in many areas of computing such as operating systems, microprocessor design, compiler construction, the world wide web and databases. Although prefetching has been studied for a long time, the problem prefetching is facing has changed over the years as a result of technology advances. Fig. 1 shows the performance/price development of semiconductor memories and magnetic disks. There is a two-order of magnitude gap in access time between memory and disks. Memory access is faster and the rate of improvement is also higher. The prices of memory are falling which makes database N. Knafla / An Adaptable Multithreaded Prefetching Technique 3 caches cheaper and buffer replacement less of a problem. Prices of disks are also falling dramatically which is good for cheap secondary storage, allowing increased use of RAIDs. CPU performance improves at an even higher rate than memory (fig. 2). CPU is doubling its performance every 18 months whereas disk retrieval time only improves about 5% per year. Memory access time improves at about 10 to 12% per year. Client workstations will become powerful multi-processors machines with high speed CPUs, most of which will tend to be idle most of the time. 1 10 100 1000 1980 1985 1990 1995

Bioinformatics in general and bioimaging in particular are characterized by large, often distributed datastores. Datastores involve more than just a database, as relational databases cannot, for example, store and protect an image. This paper examines the e#ects of network performance on a bioimaging datastore when it is locally connected, LAN connected, and WAN connected. Issues of node configuration are also considered.