A status report on research in transparent informed prefetching
ACM SIGOPS Operating Systems Review
Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Fido: A Cache That Learns to Fetch
VLDB '91 Proceedings of the 17th International Conference on Very Large Data Bases
An Adaptive Web Cache Access Predictor Using Neural Network
IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
Design and Implementation of a Predictive File Prefetching Algorithm
Proceedings of the General Track: 2002 USENIX Annual Technical Conference
The Case for Efficient File Access Pattern Modeling
HOTOS '99 Proceedings of the The Seventh Workshop on Hot Topics in Operating Systems
Using Multiple Predictors to Improve the Accuracy of File Access Predictions
MSS '03 Proceedings of the 20 th IEEE/11 th NASA Goddard Conference on Mass Storage Systems and Technologies (MSS'03)
Group-Based Management of Distributed File Caches
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
Long Term Distributed File Reference Tracing: Implementation and Experience
Long Term Distributed File Reference Tracing: Implementation and Experience
Predictive data grouping using successor prediction
Predictive data grouping using successor prediction
Predicting When Not to Predict
MASCOTS '04 Proceedings of the The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
A stochastic approach to file access prediction
SNAPI '03 Proceedings of the international workshop on Storage network architecture and parallel I/Os
File access prediction with adjustable accuracy
PCC '02 Proceedings of the Performance, Computing, and Communications Conference, 2002. on 21st IEEE International
Reducing file system latency using a predictive approach
USTC'94 Proceedings of the USENIX Summer 1994 Technical Conference on USENIX Summer 1994 Technical Conference - Volume 1
Predicting file system actions from prior events
ATEC '96 Proceedings of the 1996 annual conference on USENIX Annual Technical Conference
An analytical approach to file prefetching
ATEC '97 Proceedings of the annual conference on USENIX Annual Technical Conference
Why does file system prefetching work?
ATEC '99 Proceedings of the annual conference on USENIX Annual Technical Conference
Neural network hot spot prediction algorithm for shared web caching system
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
Estimating neural networks-based algorithm for adaptive cachereplacement
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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One of the most vexing issues in design of a high-speed computer is the wide gap of access times between the memory and the disk. To solve this problem, static file access predictors have been used. In this paper, we propose dynamic file access predictors using neural networks to significantly improve upon the accuracy, success-per-reference, and effective-success-rate-per-reference by using neural-network-based file access predictor with proper tuning. In particular, we verified that the incorrect prediction has been reduced from 53.11% to 43.63% for the proposed neural network prediction method with a standard configuration than the recent popularity (RP) method. With manual tuning for each trace, we are able to improve upon the misprediction rate and effective-success-rate-per-reference using a standard configuration. Simulations on distributed file system (DFS) traces reveal that exact fit radial basis function (RBF) gives better prediction in high end system whereas multilayer perceptron (MLP) trained with Levenberg-Marquardt (LM) backpropagation outperforms in system having good computational capability. Probabilistic and competitive predictors are the most suitable for work stations having limited resources to deal with and the former predictor is more efficient than the latter for servers having maximum system calls. Finally, we conclude that MLP with LM backpropagation algorithm has better success rate of file prediction than those of simple perceptron, last successor, stable successor, and best k out of m predictors.