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  • Yang, FangkaiMicrosoft, China (author)

Diffusion-Based Time Series Data Imputation for Cloud Failure Prediction at Microsoft 365

  • Article/chapterEnglish2023

Publisher, publication year, extent ...

  • Association for Computing Machinery (ACM),2023
  • printrdacarrier

Numbers

  • LIBRIS-ID:oai:DiVA.org:kth-341954
  • https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-341954URI
  • https://doi.org/10.1145/3611643.3613866DOI

Supplementary language notes

  • Language:English
  • Summary in:English

Part of subdatabase

Classification

  • Subject category:ref swepub-contenttype
  • Subject category:kon swepub-publicationtype

Notes

  • Part of ISBN 9798400703270QC 20240108
  • Ensuring reliability in large-scale cloud systems like Microsoft 365 is crucial. Cloud failures, such as disk and node failure, threaten service reliability, causing service interruptions and financial loss. Existing works focus on failure prediction and proactively taking action before failures happen. However, they suffer from poor data quality, like data missing in model training and prediction, which limits performance. In this paper, we focus on enhancing data quality through data imputation by the proposed Diffusion+, a sample-efficient diffusion model, to impute the missing data efficiently conditioned on the observed data. Experiments with industrial datasets and application practice show that our model contributes to improving the performance of downstream failure prediction.

Subject headings and genre

Added entries (persons, corporate bodies, meetings, titles ...)

  • Yin, WenjieKTH,Robotik, perception och lärande, RPL(Swepub:kth)u1it1cm1 (author)
  • Wang, LuMicrosoft, China (author)
  • Li, TianciMicrosoft, China (author)
  • Zhao, PuMicrosoft, China (author)
  • Liu, BoMicrosoft, China (author)
  • Wang, PaulMicrosoft, China (author)
  • Qiao, BoMicrosoft, China (author)
  • Liu, YudongMicrosoft, China (author)
  • Björkman, Mårten,1970-KTH,Robotik, perception och lärande, RPL(Swepub:kth)u1cs4x4i (author)
  • Rajmohan, SaravanMicrosoft, USA (author)
  • Lin, QingweiMicrosoft, China (author)
  • Zhang, DongmeiMicrosoft, China (author)
  • Microsoft, ChinaRobotik, perception och lärande, RPL (creator_code:org_t)

Related titles

  • In:ESEC/FSE 2023 - Proceedings of the 31st ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering: Association for Computing Machinery (ACM), s. 2050-2055

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