Prefetching multiple files per prediction can improve the predictive accuracy. However, it comes with the cost of using extra cache space and disk bandwidth. This pa- per discusses the most Recent distinct 􏰋 Successor (RnS) model and uses it to demonstrate the effectiveness of our earlier work, Program-based Last 􏰋 Successor (PLnS) model, a program-based prediction algorithm [21]. We analyze the simulation results from different trace data and show that PLnS can perform better than RnS while it only predicts at most 59% of the number of files pre- dicted by RnS when the 􏰋 in PLnS equals to two. PLnS is a good candidate when considering prefetching multiple files per prediction to improve predictive accuracy.

Recent increases in CPU performance have outpaced increases in hard drive performance. As a result, disk operations have become more expensive in terms of CPU cycles spent waiting for disk operations to complete. File pre- diction can mitigate this problem by prefetching files into cache before they are accessed. However, incorrect prediction is to a certain degree both unavoidable and costly. We present the Program-based Last N Successors (PLNS) file prediction model that identifies relationships between files through the names of the programs accessing them. Our simulation results show that PLNS makes at least 21.11% fewer incorrect predictions and roughly the same number of correct predictions as the last-successor model. We also examine the cache hit ratio of applying PLNS to the Least Recently Used (LRU) caching algorithm and show that a cache using PLNS and LRU together can perform as well as a cache up to 40 times larger using LRU alone.

Recent increases in CPU performance have outpaced increases in hard drive performance. As a result, disk operations have become more expensive in terms of CPU cycles spent waiting for disk operations to complete. File prediction can mitigate this problem by prefetching files into cache before they are accessed. However, incorrect prediction is to a certain degree both unavoidable and costly. Battery is a valuable resource in a mobile computing environment. The utility of mobile computers is greatly affected the battery life. Incorrect prediction not only wastes cache space, also consumes battery energy. Consequently, incorrect prediction is more expensive to mobile computers than its counterpart to desktop computers. Last Successor (LS) is a commonly-used predicting algorithm in practice. We present the Program-based Last Successor (PLS) file prediction model that identifies relationships between files through the names of the programs accessing them. Our simulation results show that PLS makes 21% fewer incorrect predictions and roughly the same number of correct predictions as the last-successor model. Hence, the amount of battery energy wasted on incorrect prediction could be greatly reduced. We finally examine the cache hit ratio of applying PLS to the Least Recently Used (LRU) caching algorithm and show that a cache using PLS and LRU together can perform better than a cache up to 40 times larger using LRU alone. This shows that saving battery energy and increasing performance can be reached at the same time.

Recent increases in CPU performance have outpaced increases in hard drive performance. As a result, disk operations have become more expensive in terms of CPU cycles spent waiting for disk operations to complete. File prediction can mitigate this problem by prefetching files into cache before they are accessed However, incorrect prediction is to a certain degree both unavoidable and costly. We present the Program-based and User-based Last n Successors (PULnS) file prediction model that identifies relationships between files through the names of the programs and the users accessing them. Our simulation results show that, in the worst case, PULnS makes at least 20% fewer incorrect predictions and roughly the same number of correct predictions as the last-successor model.

Recent increases in CPU performance have surpassed those in hard drives. As a result, disk operations have become more expensive in terms of the number of CPU cycles spent waiting for them to complete. File prediction can mitigate this problem by prefetching files into cache before they are accessed. Identifying relationships between individual files plays a key role in successfully performing file prefetching. It is well-known that previous patterns of file references can be used to predict future references. Nevertheless, knowledge about the programs producing the relationships between individual files has rarely been investigated. We present a Program-Based Successor (PBS) model that identifies relationships between files through the names of the programs accessing them. We develop a Program-based Last Successor (PLS) model derived from PBS to do file prediction. Our simulation results show that PLS makes 21% fewer incorrect predictions and roughly the same number of correct predictions as the Last-Successor (LS) model. We also examine the cache hit ratio achieved by applying PLS to the Least Recently Used (LRU) caching algorithm and show that a cache using PLS and LRU together can perform better than a cache up to 40 times larger using LRU alone. Finally, we argue that because program-based successors are more likely to be used soon, incorrectly prefetched program-based successors are more likely to be used and thus less incorrect than incorrectly prefetched files from non-program-based models.