Maximizing Efficiency By Trading Storage for Computation

Appeared in Proceedings of the Workshop on Hot Topics in Cloud Computing (HotCloud ’09).


Traditionally, computing has meant calculating results and then storing those results for later use. Unfortunately, committing large volumes of rarely used data to storage wastes space and energy, making it a very expensive strategy. Cloud computing, with its readily available and flexibly allocatable computing resources, suggests an alternative: storing the provenance data, and means to recomputing results as needed. While computation and storage are equivalent, finding the balance between the two that maximizes efficiency is difficult. One of the fundamental challenges of this issue is rooted in the knowledge gap separating the users and the cloud administrators—neither has a completely informed view. Users have a semantic understanding of their data, while administrators have an understanding of the cloud’s underlying structure. We detail the user knowledge and system knowledge needed to construct a comprehensive cost model for analyzing the trade-off between storing a result and regenerating a result, allowing users and administrators to make an informed cost-benefit analysis.

Publication date:
June 2009

Ian Adams
Darrell D. E. Long
Ethan L. Miller
Shankar Pasupathy
Mark W. Storer

Archival Storage
Ultra-Large Scale Storage

Available media

Full paper text: PDF

Bibtex entry

  author       = {Ian Adams and Darrell D. E. Long and Ethan L. Miller and Shankar Pasupathy and Mark W. Storer},
  title        = {Maximizing Efficiency By Trading Storage for Computation},
  booktitle    = {Proceedings of the Workshop on Hot Topics  in Cloud Computing (HotCloud ’09)},
  month        = jun,
  year         = {2009},
Last modified 28 May 2019