Maximizing efficiency by trading storage for computation

Abstract

Traditionally, computing has meant calculating results and then storing those results for later use. Unfortu-nately, committing large volumes of rarely used data to storage wastes space and energy, making it a very expen-sive 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 is-sue 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, allow-ing users and administrators to make an informed cost-benefit analysis. 1