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Space-Efficient Predictive Block Management

Essary, David and Amer, Ahmed (2009) Space-Efficient Predictive Block Management. Technical Report. Department of Computer Science, University of Pittsburgh, Pittsburgh. (Unpublished)

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With growing disk and storage capacities, the amount of required metadata for tracking all blocks in a system becomes a daunting task by itself. In previous work, we have demonstrated a system software effort in the area of predictive data grouping for reducing power and latency on hard disks. The structures used, very similar to prior efforts in prefetching and prefetch caching, track access successor information at the block level, keeping a fixed number of immediate successors per block. While providing powerful predictive expansion capabilities and being more space efficient in the amount of required metadata than many previous strategies, there remains a growing concern of how much data is actually required. In this paper, we present a novel method of storing equivalent information, SESH, a Space Efficient Storage of Heredity. This method utilizes the high amount of block-level predictability observed in a number of workload trace sets to reduce the overall metadata storage by up to 99% without any loss of information. As a result, we are able to provide a predictive tool that is adaptive, accurate, and robust in the face of workload noise, for a tiny fraction of the metadata cost previously anticipated; in some cases, reducing the required size from 12 gigabytes to less than 150 megabytes.


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Item Type: Monograph (Technical Report)
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Essary, David
Amer, Ahmed
Monograph Type: Technical Report
Date: 2009
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Publisher: Department of Computer Science, University of Pittsburgh
Place of Publication: Pittsburgh
Institution: University of Pittsburgh
Department: Computer Science
Schools and Programs: Dietrich School of Arts and Sciences > Computer Science
Dietrich School of Arts and Sciences > Computer Science > Computer Science Technical Reports
Refereed: No
University of Pittsburgh Series: Computer Science Technical Reports
Other ID: CS TR 09 165
Date Deposited: 12 Mar 2013 18:13
Last Modified: 20 Dec 2018 00:55


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