A User Behavior Perception Model Based on Markov Process
WISM '09 Proceedings of the International Conference on Web Information Systems and Mining
Does virtualization make disk scheduling passé?
ACM SIGOPS Operating Systems Review
An improved association rules mining method
Expert Systems with Applications: An International Journal
Self-tuning of disk input-output in operating systems
Journal of Systems and Software
DADS: dynamic and automatic disk scheduling
Proceedings of the 27th Annual ACM Symposium on Applied Computing
I/O scheduling in Android devices with flash storage
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
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Performance of disk I/O schedulers is affected by many factors, such as workloads, file systems, and disk systems. Disk scheduling performance can be improved by tuning scheduler parameters, such as the length of read timers. Scheduler performance tuning is mostly done manually. To automate this process, we propose four self-learning disk scheduling schemes: Change-sensing Round-Robin, Feedback Learning, Per-request Learning, and Two-layer Learning. Experiments show that the novel Two-layer Learning Scheme performs best. It integrates the workload-level and request-level learning algorithms. It employs feedback learning techniques to analyze workloads, change scheduling policy, and tune scheduling parameters automatically. We discuss schemes to choose features for workload learning, divide and recognize workloads, generate training data, and integrate machine learning algorithms into the Two-layer Learning Scheme. We conducted experiments to compare the accuracy, performance, and overhead of five machine learning algorithms: Decision Tree, Logistic Regression, Naïve Bayes, Neural Network, and Support Vector Machine Algorithms. Experiments with real-world and synthetic workloads show that self-learning disk scheduling can adapt to a wide variety of workloads, file systems, disk systems, and user preferences. It outperforms existing disk schedulers by as much as 15.8% while consuming less than 3%-5% of CPU time.