Published September 5, 2022 | Version v1
Conference paper Open

LOGI: An Empirical Model of Heat-Induced Disk Drive Data Loss and its Implications for Data Recovery

  • 1. University of Michigan
  • 2. MathWorks
  • 3. St. Lawrence University
  • 4. Polytechnique Montreal


This artifact represents the replication materials for our paper on heat-induced disk drive data loss.

Disk storage continues to be an important medium for data recording in software engineering, and recovering data from a failed storage disk can be expensive and time-consuming. Unfortunately, while physical damage instances are well documented, existing studies of data loss are limited, often only predicting times between failures. We present an empirical measurement of patterns of heat damage on indicative, low-cost commodity hard drives. Because damaged hard drives require many hours to read, we propose an efficient, accurate sampling algorithm. Using our empirical measurements, we develop LOGI, a formal mathematical model that, on average, predicts sector damage with precision, recall, F-measure, and accuracy values of over 0.95. We also present a case study on the usage of LOGI and discuss its implications for file carver software.

We hope that this model is used by other researchers to simulate damage and bootstrap further study of disk failures, helping engineers make informed decisions about data storage for software systems.


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