Operating systems only provide general-purpose I/O optimisation since they have to service various types of applications. However, application level I/O optimisation can achieve better performance since an application has a better knowledge of how to optimise disk I/O for the application. In this paper we provide a solution for applicationspecific I/O for optimising a search engine. It shows a 28% improvement when compared to the general-purpose I/O optimisation of Linux. Our result also shows a 11 % improvement when the Linux I/O optimisation is bypassed. 1

Conventional prefetching schemes regard prediction accuracy as important because useless data prefetched by a faulty prediction may pollute the cache. If prefetching requires considerably low read cost but the prediction is not accurate, it may or may not be beneficial depending on the situation. However, the problem of low prediction accuracy can be dramatically reduced if we efficiently manage prefetched data by considering the total hit rate for both prefetched data and cached data. To achieve this goal, we propose an adaptive strip prefetching (ASP) scheme, which provides low prefetching cost and evicts prefetched data at the proper time by using differential feedback that maximizes the hit rate of both prefetched data and cached data in a given cache management scheme. Additionally, ASP controls prefetching by using an online disk simulation that investigates whether prefetching is beneficial for the current workloads and stops prefetching if it is not. Finally, ASP provides methods that resolve both independency loss and parallelism loss that may arise in striped disk arrays. We implemented a kernel module in Linux version 2.6.18 as a RAID-5 driver with our scheme, which significantly outperforms the sequential prefetching of Linux from several times to an order of magnitude in a variety of realistic workloads. 1

(SSDs) have been rapidly adopted in laptops, desktops, and server storage systems because their performance is superior to that of traditional magnetic disks. However, NAND flash memory has some limitations such as out-of-place updates, bulk erase operations, and a limited number of write operations. To alleviate these unfavorable characteristics, various techniques for improving internal software and hardware components have been devised. In particular, the internal device cache of SSDs has a significant impact on the performance. The device cache is used for two main purposes: to absorb frequent read/write requests and to store logical-to-physical address mapping information. In the device cache, we observed that the optimal ratio of the data buffering and the address mapping space changes according to workload characteristics. To achieve optimal performance in SSDs, the device cache should be appropriately partitioned between the two main purposes. In this paper, we propose an adaptive partitioning scheme, which is based on a ghost caching mechanism, to adaptively tune the ratio of the buffering and the mapping space in the device cache according to the workload characteristics. The simulation results demonstrate that the performance of the proposed scheme approximates the best performance. I.