1. Improved File Accesses with
Neural Networks
Hemanth Mantri
Makarand Damle
Department of Computer Science

2. Motivation
• Processor and Memory Speeds
– Very High
• Disk access speeds
– Bottle Neck
• How to overcome?
– Caching
• Replacement (LRU, LFU)
– Prefetching
• Need prediction

3. Current Approach
• Linux Read Ahead
– Limited to file level
– Sequential / Random
• Application has limited say
– Hints to the kernel
– fadvise(), madvise()
– Only sequential or Random

4. Type of Predictors
• Access Tree based
– Track the processes along with file accesses
• Hint Based (Applications)
– find, grep, etc

5. Recent Predictors - Dynamic
• Composite
– Takes input from 4 static predictors
• Multiple Experts (ME)
– Voting
• Perceptron
– Weights

6. Perceptron Approach
• Neural network inputs
– File IDs of the last 6 observed successors of the
requested file
• Outputs:
– Probability of access Type
• Sequential (1, 2, 3, 4, 5)
• Alternating (1, 3, 5, 7, 9)
• Every third (1, 4, 7, 10, 13)
• Random (10, 4, 11, 6, 2)

7. The Neural Network
Last 6 P(Seq)
accessed 4
Layer
P(Alt)
File IDs MLP
P(E3) PT
Access Type
Neural
Network
P(Rand)

8. Characteristics
• 4 Layer, Back Propagation NN
• Tunable # hidden units
• Input unit: Identity function
• Hidden unit: Sigmoid function
• Used FANN Library

9. Job of the NN
• Predict the type of access pattern
• Let the File System know of it before every
read
• Evaluated on a NN simulator
– Generated 0.3 million traces (inode numbers)
– Training set: 0.3 million
– Testing set: 0.3 million

10. Sample Application
• 4 Steps
– Read files in a directory
– Update the access sequence
– Invoke the NN predictor
– Hint the kernel about the access pattern
• Informed prefetching
– Reduces unnecessary reads
– Decreased read response time

11. Pseudo Code
• function: directory_read
while (cond) {
hint = predict_pattern(sequence);
read(file , O_RDONLY, buffer, hint);
update_access_seq(file, sequence);
do_something(buffer);
file = get_next_file();
}

12. NN Tuning
• Inputs (5)
• Size of the hidden layer (10)
• Probability Threshold (0.6)
• Learning rate (0.3)

13. Evaluation Metrics - NN

14. Preliminary Evaluation - App

15. Work In Progress
• Bench Marking
– Evaluation metrics such as EMR
– Different work loads
– Fine grained profiling
• Learn block level access patterns
– Per file
• Bigger data set

16. Thank You!

17. References
• Fast Artificial Neural Network
– http://leenissen.dk/fann/wp/
• File Access Prediction using Neural
Networks
– Patra et.al