PERSES: Data Layout for Low Impact Failures

Appeared in 22th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2014).

Abstract

Growth in disk capacity continues to outpace advances in read speed and device reliability. This has led to storage systems spending increasing amounts of time in a degraded state while failed disks reconstruct. Users and applications that do not use the data on the failed or degraded drives are negligibly impacted by the failure, increasing the perceived performance of the system. We leverage this observation with PERSES, a statistical data allocation scheme to reduce the performance impact of reconstruction after disk failure. PERSES reduces degradation from the perspective of the user by clustering data on disks such that data with high probability of co-access is placed on the same device as often as possible. Trace-driven simulations show that, by laying out data with PERSES, we can reduce the perceived time lost due to failure over three years by up to 80% compared to arbitrary allocation.

Publication date:
September 2014

Authors:
Avani Wildani
Ethan L. Miller
Ian Adams
Darrell D. E. Long

Projects:
Archival Storage
Prediction and Grouping

Available media

Full paper text: PDF

Bibtex entry

@inproceedings{wildani-mascots14,
  author       = {Avani Wildani and Ethan L. Miller and Ian Adams and Darrell D. E. Long},
  title        = {{PERSES}: Data Layout for Low Impact Failures},
  booktitle    = {22th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2014)},
  month        = sep,
  year         = {2014},
}