Trustworthy 100-year digital objects: durable encoding for when it's too late to ask
ACM Transactions on Information Systems (TOIS)
Provenance-aware storage systems
ATEC '06 Proceedings of the annual conference on USENIX '06 Annual Technical Conference
Distributed Computing Economics
Queue - Object-Relational Mapping
Measurement and analysis of large-scale network file system workloads
ATC'08 USENIX 2008 Annual Technical Conference on Annual Technical Conference
Price fixing in the memory market [DRAM prices]
IEEE Spectrum
Journal of Parallel and Distributed Computing
MATE-EC2: a middleware for processing data with AWS
Proceedings of the 2011 ACM international workshop on Many task computing on grids and supercomputers
The purge threat: scientists' thoughts on peta-scale usability
Proceedings of the sixth workshop on Parallel Data Storage
A data dependency based strategy for intermediate data storage in scientific cloud workflow systems
Concurrency and Computation: Practice & Experience
Analyzing compute vs. storage tradeoff for video-aware storage efficiency
HotStorage'12 Proceedings of the 4th USENIX conference on Hot Topics in Storage and File Systems
Proceedings of the 6th International Systems and Storage Conference
Role of acquisition intervals in private and public cloud storage costs
Decision Support Systems
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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.