Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Motivation and design goals


Related Work


Conformal prediction
4.1. Mondrian conformal prediction (MCP)
4.2. Evaluation metrics


Mondrian conformal prediction for Disk Scrubbing: our approach
5.1. System and Storage statistics
5.2. Which disk to scrub: Drive health predictor
5.3. When to scrub: Workload predictor


Experimental setting and 6.1. Open-source Baidu dataset
6.2. Experimental results


Discussion
7.1. Optimal scheduling aspect
7.2. Performance metrics and 7.3. Power saving from selective scrubbing


Conclusion and References Motivation and design goals Motivation and design goals Motivation and design goals Related Work Related Work Related Work Conformal prediction
4.1. Mondrian conformal prediction (MCP)
4.2. Evaluation metrics Conformal prediction Conformal prediction 4.1. Mondrian conformal prediction (MCP) 4.1. Mondrian conformal prediction (MCP) 4.2. Evaluation metrics 4.2. Evaluation metrics Mondrian conformal prediction for Disk Scrubbing: our approach
5.1. System and Storage statistics
5.2. Which disk to scrub: Drive health predictor
5.3. When to scrub: Workload predictor Mondrian conformal prediction for Disk Scrubbing: our approach Mondrian conformal prediction for Disk Scrubbing: our approach 5.1. System and Storage statistics 5.1. System and Storage statistics 5.2. Which disk to scrub: Drive health predictor 5.2. Which disk to scrub: Drive health predictor 5.3. When to scrub: Workload predictor 5.3. When to scrub: Workload predictor Experimental setting and 6.1. Open-source Baidu dataset
6.2. Experimental results Experimental setting and 6.1. Open-source Baidu dataset Experimental setting and 6.1. Open-source Baidu dataset 6.2. Experimental results 6.2. Experimental results Discussion
7.1. Optimal scheduling aspect
7.2. Performance metrics and 7.3. Power saving from selective scrubbing Discussion Discussion 7.1. Optimal scheduling aspect 7.1. Optimal scheduling aspect 7.2. Performance metrics and 7.3. Power saving from selective scrubbing 7.2. Performance metrics and 7.3. Power saving from selective scrubbing Conclusion and References Conclusion and References Conclusion and References 7.2. Performance metrics We captured the effective coverage (i.e., for any chosen confidence level, prediction intervals will fail to include the correct label) and prediction set size for the open-source dataset in. The plot in Figure 6 demonstrates that there is a positive correlation between the confidence level and the coverage. The split-conformal method results in a higher mean coverage than the cross-validation method, indicating that the calibration set selection has a considerable influence on the effective coverage. Furthermore, the right side of the figure displays the average size of the prediction set, which increases as the confidence level increases. Similarly, the split-conformal method yields a consistently higher mean prediction set size than the cross-validation method. The metrics can be used to evaluate how well the Mondrian conformal predictor is performing. 7.3. Power saving from selective scrubbing Scrubbing is a resource-intensive operation that can impact the performance of the system during its execution. The time taken to complete a scrubbing operation depends on various factors, such as the size of the HDD being scrubbed. For instance, scrubbing a 1TB HDD may take a few to several hours, while scrubbing an 8TB HDD could take significantly longer, potentially a day or more. Assuming an average power consumption of 7 watts during a 6-hour scrubbing operation for a single HDD, the total energy consumed would be 42 watt-hours (Wh). It’s important to note that power consumption during scrubbing can vary for different disks in a data center, depending on factors like disk size, manufacturer, and storage operations. Taking an average value for power usage comparison, if selective scrubbing is performed on 28,000 disks instead of scrubbing all 120,000 disks in a data center based on results from the Baidu open-source dataset, significant power and energy savings can be achieved for the entire data center. 8. Conclusion The complexity and uncertainty of individual storage components in large-scale data centers pose challenges to business continuity. While proactive approaches like monitoring and failure analysis have been implemented, machine learning approaches may have false positive concerns in real-world applications with numerous disk drives. In this paper, we propose a fine-grained approach to disk scrubbing using a learning framework based on Mondrian conformal prediction, evaluated on the Baidu open-source dataset. Our method provides a modest yet effective contribution from a methodological perspective. It tackles the issue of aggressive scrubbing of the entire storage array by utilizing Mondrian conformal predictors to assign health scores to each drive and selectively targeting disks with lower scores for scrubbing. This approach generates a prioritized list for the scheduler engine, leveraging drive failure analysis and quantifying disk health across the entire storage pool. As a result, only 22.7% of the drives need to be scrubbed, leading to power savings and improved reliability. Future work could involve incorporating Venn-Abers predictors, which offer calibrated probabilities for predictions and could further enhance the accuracy and effectiveness of our approach (Vovk and Petej, 2012). 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This paper is available on arxiv under CC BY-NC-ND 4.0 Deed (Attribution-Noncommercial-Noderivs 4.0 International) license. available on arxiv Authors:
(1) Rahul Vishwakarma, California State University Long Beach, 1250 Bellflower Blvd, Long Beach, CA 90840, United States (rahuldeo.vishwakarma01@student.csullb.edu);
(2) Jinha Hwang, California State University Long Beach, 1250 Bellflower Blvd, Long Beach, CA 90840, United States (jinha.hwang01@student.csulb.edu);
(3) Soundouss Messoudi, HEUDIASYC - UMR CNRS 7253, Universit´e de Technologie de Compiegne, 57 avenue de Landshut, 60203 Compiegne Cedex - France (soundouss.messoudi@hds.utc.fr);
(4) Ava Hedayatipour, California State University Long Beach, 1250 Bellflower Blvd, Long Beach, CA 90840, United States (ava.hedayatipour@csulb.edu). Authors: Authors: (1) Rahul Vishwakarma, California State University Long Beach, 1250 Bellflower Blvd, Long Beach, CA 90840, United States (rahuldeo.vishwakarma01@student.csullb.edu); (2) Jinha Hwang, California State University Long Beach, 1250 Bellflower Blvd, Long Beach, CA 90840, United States (jinha.hwang01@student.csulb.edu); (3) Soundouss Messoudi, HEUDIASYC - UMR CNRS 7253, Universit´e de Technologie de Compiegne, 57 avenue de Landshut, 60203 Compiegne Cedex - France (soundouss.messoudi@hds.utc.fr); (4) Ava Hedayatipour, California State University Long Beach, 1250 Bellflower Blvd, Long Beach, CA 90840, United States (ava.hedayatipour@csulb.edu).

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How Machine Learning Optimizes Data Center Disk Health and Power Efficiency

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