Access the full text.
Sign up today, get DeepDyve free for 14 days.
Scott Leutenegger, D. Dias (1993)
A modeling study of the TPC-C benchmarkProceedings of the 1993 ACM SIGMOD international conference on Management of data
V. Vellanki, A. Chervenak (1999)
A Cost-Benefit Scheme for High Performance Predictive PrefetchingACM/IEEE SC 1999 Conference (SC'99)
André Seifert, M. Scholl (2002)
A Multi-version Cache Replacement and Prefetching Policy for Hybrid Data Delivery Environments
Z. Chen, Yuanyuan Zhou, Kai Li
Proceedings of the General Track: 2003 Usenix Annual Technical Conference Eviction Based Cache Placement for Storage Caches
P. Perner (2002)
Data Mining - Concepts and TechniquesKünstliche Intell., 16
Jay Ayres, J. Flannick, J. Gehrke, Tomi Yiu (2002)
Sequential PAttern mining using a bitmap representationProceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
P. Cao, E. Felten, Kai Li (1994)
Application-Controlled File Caching Policies
H. Wedekind, Georg Zörntlein (1986)
Prefetching in realtime database applications
J. Wilkes, Richard Golding, Carl Staelin, Tim Sullivan (1995)
The HP AutoRAID hierarchical storage systemProceedings of the fifteenth ACM symposium on Operating systems principles
Jeerey Scott, Vitter Krishnan, Jeerey Vitter, P. Krishnan (1991)
Optimal prefetching via data compression[1991] Proceedings 32nd Annual Symposium of Foundations of Computer Science
Muthian Sivathanu, Vijayan Prabhakaran, Florentina Popovici, Timothy Denehy, Andrea Arpaci-Dusseau, Remzi Arpaci-Dusseau
Proceedings of Fast '03: 2nd Usenix Conference on File and Storage Technologies 2nd Usenix Conference on File and Storage Technologies Semantically-smart Disk Systems
Mohammed Zaki (2004)
SPADE: An Efficient Algorithm for Mining Frequent SequencesMachine Learning, 42
H. Chou, D. DeWitt (1986)
An evaluation of buffer management strategies for relational database systemsAlgorithmica, 1
T. Madhyastha, Garth Gibson, C. Faloutsos (1999)
Informed Prefetching of Collective Input/Output RequestsACM/IEEE SC 1999 Conference (SC'99)
J. Schindler, J. Griffin, Christopher Lumb, G. Ganger (2002)
Track-Aligned Extents: Matching Access Patterns to Disk Drive Characteristics
P. Cao, E. Felten, Anna Karlin, Kai Li (1995)
A study of integrated prefetching and caching strategies
R. Agrawal, R. Srikant (1995)
Mining sequential patternsProceedings of the Eleventh International Conference on Data Engineering
K. Keeton, J. Wilkes (2002)
Automating data dependability
Angela Brown, T. Mowry, O. Krieger (2001)
Compiler-based I/O prefetching for out-of-core applicationsACM Trans. Comput. Syst., 19
W. Link (1952)
PracticalPastoral Psychology, 3
T. Wong, J. Wilkes (2002)
My Cache or Yours? Making Storage More Exclusive
Jiawei Han, M. Kamber (2000)
Data Mining: Concepts and Techniques
L. Barroso, K. Gharachorloo, Edouard Bugnion (1998)
Memory system characterization of commercial workloadsProceedings. 25th Annual International Symposium on Computer Architecture (Cat. No.98CB36235)
G. Kuenning, G. Popek (1997)
Automated hoarding for mobile computersProceedings of the sixteenth ACM symposium on Operating systems principles
A. Smith (1978)
Sequentiality and prefetching in database systemsACM Trans. Database Syst., 3
B. Smith (1986)
A pipelined, shared resource MIMD computer
A. Tomkins, R. Patterson, Garth Gibson (1997)
Informed multi-process prefetching and caching
E. Carrera, Eduardo Pinheiro, R. Bianchini (2003)
Conserving disk energy in network servers
Andrea Arpaci-Dusseau, Remzi Arpaci-Dusseau (2001)
Information and control in gray-box systemsProceedings of the eighteenth ACM symposium on Operating systems principles
James Appleton (1995)
Performance Measurements of Automatic Prefetching
(2005)
Mining Block Correlations to Improve Storage Performance @BULLET 245
T. Kimbrel, A. Tomkins, R. Patterson, B. Bershad, P. Cao, E. Felten, Garth Gibson, Anna Karlin, Kai Li (1996)
A trace-driven comparison of algorithms for parallel prefetching and caching
Xifeng Yan, Jiawei Han, R. Afshar (2003)
CloSpan: Mining Closed Sequential Patterns in Large Datasets
T. Mowry, Angela Demke, O. Krieger (1996)
Automatic compiler-inserted I/O prefetching for out-of-core applications
N. Megiddo, D. Modha
Proceedings of Fast '03: 2nd Usenix Conference on File and Storage Technologies 2nd Usenix Conference on File and Storage Technologies Arc: a Self-tuning, Low Overhead Replacement Cache
Fay Chang, Garth Gibson (1999)
Automatic I/O hint generation through speculative execution
R. Patterson, Garth Gibson, E. Ginting, Daniel Stodolsky, J. Zelenka (1995)
Informed prefetching and cachingProceedings of the fifteenth ACM symposium on Operating systems principles
Jongmoo Choi, S. Noh, S. Min, Yookun Cho (2000)
Towards application/file-level characterization of block references: a case for fine-grained buffer management
J. Pitkow, P. Pirolli (1999)
Mining Longest Repeating Subsequences to Predict World Wide Web Surfing
Brandon Salmon, Eno Thereska, Craig Soules, G. Ganger (2003)
A Two-Tiered Software Architecture for Automated Tuning of Disk Layouts (CMU-CS-03-130)
H. Lei, D. Duchamp (1997)
An analytical approach to file prefetching
Thomas Kroeger, D. Long (1996)
Predicting file system actions from prior events
Chris Clifton, Gary Gengo (2000)
Developing custom intrusion detection filters using data miningMILCOM 2000 Proceedings. 21st Century Military Communications. Architectures and Technologies for Information Superiority (Cat. No.00CH37155), 1
Edward Lee, C. Thekkath (1996)
Petal: distributed virtual disks
(1993)
UNIX Disk Access Patterns
J. Pei, Jiawei Han, B. Mortazavi-Asl, Helen Pinto, Qiming Chen, U. Dayal, M. Hsu (2001)
PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growthProceedings 17th International Conference on Data Engineering
M. Palmer, S. Zdonik (1991)
Fido: A Cache That Learns to Fetch
T. Madhyastha, D. Reed (1997)
Input/output access pattern classification using hidden Markov models
J. Griffioen, R. Appleton (1994)
Reducing File System Latency using a Predictive Approach
(1999)
Symmetrix 3000 and 5000 Enterprise Storage Systems Product Description Guide
G. Ganger (1995)
System-oriented evaluation of I/O subsystem performance
Mengzhi Wang, N. Chan, S. Papadimitriou, C. Faloutsos, T. Madhyastha (2002)
Data mining meets performance evaluation: fast algorithms for modeling bursty trafficProceedings 18th International Conference on Data Engineering
Carl Tait, H. Lei, S. Acharya, Henry Chang (1995)
Intelligent file hoarding for mobile computers
G. Kuenning (1994)
The Design of the SEER Predictive Caching System1994 First Workshop on Mobile Computing Systems and Applications
J. Pei, Jiawei Han, B. Mortazavi-Asl, Helen Pinto, Qiming Chen, U. Dayal, M. Hsu (2001)
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
(2002)
Storage Tank, a distributed storage system. IBM White paper. Web site: http://www.almaden.ibm.com/StorageSystems/file systems/storage tank/papers
Carsten Gerlhof, A. Kemper (1994)
A Multi-Threaded Architecture for Prefetching in Object Bases
(1999)
Clump: Improving file system performance through adaptive optimizations
V. Soloviev (1996)
Prefetching in segmented disk cache for multi-disk systems
(2002)
Storage virtualization: The next step
Jong Kim, Jongmoo Choi, Jesung Kim, S. Noh, S. Min, Yookun Cho, Chong-Sang Kim (2000)
A low-overhead high-performance unified buffer management scheme that exploits sequential and looping references
Carsten Gerlhof, A. Kemper (1994)
Prefetch Support Relations in Object Bases
C. Ruemmler, J. Wilkes (1993)
A trace-driven analysis of disk working set sizes
Ismail Ari, A. Amer, E. Miller, S. Brandt, D. Long (2002)
Who Is More Adaptive ? ACME : Adaptive Caching using Multiple Experts
Yanyong Zhang, Jianyong Zhang, A. Sivasubramaniam, Chun Liu, H. Franke (2003)
Decision-support workload characteristics on a clustered database server from the OS perspective23rd International Conference on Distributed Computing Systems, 2003. Proceedings.
Yuanyuan Zhou, J. Philbin, Kai Li (2001)
The Multi-Queue Replacement Algorithm for Second Level Buffer Caches
Stuart Schechter, Murali Krishnan, Michael Smith (1998)
Using Path Profiles to Predict HTTP RequestsComput. Networks, 30
W. Hsu, A. Smith, H. Young (1999)
I/O reference behavior of production database workloads and the TPC benchmarks—an analysis at the logical levelACM Trans. Database Syst., 26
Wenke Lee, S. Stolfo (1998)
Data Mining Approaches for Intrusion Detection
(2004)
Received September ACM Transactions on Storage
T. Madhyastha, D. Reed (2002)
Learning to Classify Parallel Input/Output Access PatternsIEEE Trans. Parallel Distributed Syst., 13
Eric Anderson, M. Hobbs, K. Keeton, Susan Spence, Mustafa Uysal, Alistair Veitch (2002)
Hippodrome: Running Circles Around Storage Administration
Jiawei Han (2002)
How Can Data Mining Help Bio-Data Analysis?
Garth Gibson, D. Nagle, Khalil Amiri, Jeff Butler, Fay Chang, H. Gobioff, C. Hardin, E. Riedel, David Rochberg, J. Zelenka (1998)
A cost-effective, high-bandwidth storage architecture
(2004)
SPC I/O traces. Web site
Block correlations are common semantic patterns in storage systems. They can be exploited for improving the effectiveness of storage caching, prefetching, data layout, and disk scheduling. Unfortunately, information about block correlations is unavailable at the storage system level. Previous approaches for discovering file correlations in file systems do not scale well enough for discovering block correlations in storage systems.In this article, we propose two algorithms, C-Miner and C-Miner *, that use a data mining technique called frequent sequence mining to discover block correlations in storage systems. Both algorithms run reasonably fast with feasible space requirement, indicating that they are practical for dynamically inferring correlations in a storage system. C-Miner is a direct application of a frequent-sequence mining algorithm with a few modifications; compared with C-Miner , C-Miner * is redesigned for mining block correlations by making concessions for the specific problem of long sequences in storage system traces. Therefore, C-Miner * can discover 7--109% more correlation rules within 2--15 times shorter time than C-Miner . Moreover, we have also evaluated the benefits of block correlation-directed prefetching and data layout through experiments. Our results using real system workloads show that correlation-directed prefetching and data layout can reduce average I/O response time by 12--30% compared to the base case, and 7--25% compared to the commonly used sequential prefetching scheme for most workloads.
ACM Transactions on Storage (TOS) – Association for Computing Machinery
Published: May 1, 2005
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.