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Data center downtime typically centers around IT equipment failure. Storage devices are the most frequently failing components in data centers. We present a comparative study of hard disk drives (HDDs) and solid state drives (SSDs) that constitute the typical storage in data centers. Using six-year field data of 100,000 HDDs of different models from the same manufacturer from the Backblaze dataset and six-year field data of 30,000 SSDs of three models from a Google data center, we characterize the workload conditions that lead to failures. We illustrate that their root failure causes differ from common expectations and that they remain difficult to discern. For the case of HDDs we observe that young and old drives do not present many differences in their failures. Instead, failures may be distinguished by discriminating drives based on the time spent for head positioning. For SSDs, we observe high levels of infant mortality and characterize the differences between infant and non-infant failures. We develop several machine learning failure prediction models that are shown to be surprisingly accurate, achieving high recall and low false positive rates. These models are used beyond simple prediction as they aid us to untangle the complex interaction of workload characteristics that lead to failures and identify failure root causes from monitored symptoms.
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Ultra-Sensitive and Selective Detection of DNA and Protein Biomarkers Using Frequency-Locked Microtoroid Optical Resonators
We use frequency-locked microtoroid optical resonators to detect protein and DNA biomarkers at attomolar to femtomolar concentrations, depending on the target. We measure binding affinities and validate our results against existing technologies.
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- Award ID(s):
- 1842045
- PAR ID:
- 10303918
- Date Published:
- Journal Name:
- Conference on Lasers and Electro-Optics
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1364/CLEO_SI.2021.SM3O.1
