File Access Prediction Using Neural Networks

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.

[1]  Darrell D. E. Long,et al.  Predictive data grouping using successor prediction , 2002 .

[2]  Scott A. Brandt,et al.  ACME: Adaptive Caching Using Multiple Experts , 2002, WDAS.

[3]  Stanley B. Zdonik,et al.  Profile-driven cache management , 2003, Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405).

[4]  Jong Ho Park,et al.  Neural Network Hot Spot Prediction Algorithm for Shared Web Caching System , 2005, APWeb.

[5]  I-Fan Chen,et al.  Improving the System Performance by a Dynamic File Prediction Model , 2006, CDES.

[6]  Randal C. Burns,et al.  Using multiple predictors to improve the accuracy of file access predictions , 2003, 20th IEEE/11th NASA Goddard Conference on Mass Storage Systems and Technologies, 2003. (MSST 2003). Proceedings..

[7]  Ahmed Amer,et al.  Dynamic relationships and the persistence of pairings , 2001, Proceedings 21st International Conference on Distributed Computing Systems Workshops.

[8]  Mohammad S. Obaidat,et al.  Estimating neural networks-based algorithm for adaptive cache replacement , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[9]  Darrell D. E. Long,et al.  The case for efficient file access pattern modeling , 1999, Proceedings of the Seventh Workshop on Hot Topics in Operating Systems.

[10]  Dan Duchamp,et al.  Detection and exploitation of file working sets , 1991, [1991] Proceedings. 11th International Conference on Distributed Computing Systems.

[11]  Ahmed Amer,et al.  A stochastic approach to file access prediction , 2003, SNAPI '03.

[12]  Karl S. Brandt.  Using Multiple Experts To Perform File Prediction , 2004 .

[13]  Mohsen Hayati,et al.  Multilayer perceptron neural networks with novel unsupervised training method for numerical solution of the partial differential equations , 2009, Appl. Soft Comput..

[14]  Jehan-François Pâris,et al.  Making Early Predictions of File Accesses , 2005 .

[15]  Thomas M. Kroeger,et al.  Predicting file system actions from prior events , 1996 .

[16]  Ahmed Amer,et al.  Identifying Stable File Access Patterns , 2004, MSST.

[17]  Stanley B. Zdonik,et al.  Fido: A Cache That Learns to Fetch , 1991, VLDB.

[18]  Darrell D. E. Long,et al.  Design and Implementation of a Predictive File Prefetching Algorithm , 2001, USENIX Annual Technical Conference, General Track.

[19]  Darrell D. E. Long,et al.  Noah: low-cost file access prediction through pairs , 2001, Conference Proceedings of the 2001 IEEE International Performance, Computing, and Communications Conference (Cat. No.01CH37210).

[20]  Ahmed Amer,et al.  Predicting when not to predict , 2004, The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, 2004. (MASCOTS 2004). Proceedings..

[21]  Christopher Small,et al.  Why does file system prefetching work? , 1999, USENIX Annual Technical Conference, General Track.

[22]  Ahmed Amer,et al.  File access prediction with adjustable accuracy , 2002, Conference Proceedings of the IEEE International Performance, Computing, and Communications Conference (Cat. No.02CH37326).

[23]  Jim Griffioen,et al.  Reducing File System Latency using a Predictive Approach , 1994, USENIX Summer.

[24]  Mahadev Satyanarayanan,et al.  Long Term Distributed File Reference Tracing: Implementation and Experience , 1996, Softw. Pract. Exp..

[26]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1994 .

[27]  Mahadev Satyanarayanan,et al.  A status report on research in transparent informed prefetching , 1993, OPSR.

[28]  Hui Lei,et al.  An analytical approach to file prefetching , 1997 .

[29]  Randal C. Burns,et al.  Group-based management of distributed file caches , 2002, Proceedings 22nd International Conference on Distributed Computing Systems.

[30]  P. K. Patra,et al.  Probabilistic neural network for pattern classification , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[31]  Vir V. Phoha,et al.  An Adaptive Web Cache Access Predictor Using Neural Network , 2002, IEA/AIE.

[32]  Jiwu Shu,et al.  Dynamic File Allocation in Storage Area Networks with Neural Network Prediction , 2004, ISNN.

[33]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .