research-article

SOPA: Selecting the optimal caching policy adaptively

Authors:

Guangyan Zhang,

Weimin ZhengAuthors Info & Claims

ACM Transactions on Storage (TOS), Volume 6, Issue 2

Article No.: 7, Pages 1 - 18

https://doi.org/10.1145/1807060.1807064

Published: 30 July 2010 Publication History

Abstract

With the development of storage technology and applications, new caching policies are continuously being introduced. It becomes increasingly important for storage systems to be able to select the matched caching policy dynamically under varying workloads. This article proposes SOPA, a cache framework to adaptively select the matched policy and perform policy switches in storage systems. SOPA encapsulates the functions of a caching policy into a module, and enables online policy switching by policy reconstruction. SOPA then selects the policy matched with the workload dynamically by collecting and analyzing access traces. To reduce the decision-making cost, SOPA proposes an asynchronous decision making process. The simulation experiments show that no single caching policy performed well under all of the different workloads. With SOPA, a storage system could select the appropriate policy for different workloads. The real-system evaluation results show that SOPA reduced the average response time by up to 20.3% and 11.9% compared with LRU and ARC, respectively.

References

[1]

Ari, I., Amer, A., Gramarcy, R., Miller, E., Brandt, S., and Long, D. 2002. ACME: Adaptive caching using multiple experts. In Proceedings of the Workshop on Distributed Data and Structures, Carleton Scientific, 2002.

[2]

Ari, I., Gottwals, M., and Henze, D. 2004. SANBoost: Automated SAN-Level caching in storage area networks, In Proceedings of the International Conference on Autonomic Computing (ICAC).

Digital Library

[3]

Bansal, S. and Modha, D. S. 2004. CAR: Clock with adaptive replacement. In Proceedings of the USENIX File and. Storage Technologies Conference (FAST), 142--163.

Digital Library

[4]

Cao. P. and Irani, S. 1997. Cost-aware WWW proxy caching algorithms. In Proceedings of the USENIX Symposium on Internet Technologies and Systems.

Digital Library

[5]

Chen, Z., Zhou, Y., and Li, K. 2003. Eviction-based cache placement for storage caches. In Proceedings of the USENIX Annual Technical Conference, 269--281.

[6]

Chen, Z., Zhang, Y., Zhou, Y., Scott, H., and Schiefer, B. 2005. Empirical evaluation of multi-level buffer cache collaboration for storage systems. In Proceedings of the ACM SIGMETRICS Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), 2005.

Digital Library

[7]

Chou, H. T. and Dewitt, D. J. 1985. An evaluation of buffer management strategies for relational database systems. In Proceedings of the VLDB Conference.

Digital Library

[8]

Gill, B. S. and Modha, D. S. 2005. WOW: Wise ordering for writes&quest;combining spatial and temporal locality in non-volatile caches. In Proceedings of the USENIX Conference on File and Storage Technologies (FAST).

Digital Library

[9]

HP TRACES. http://tesla.hpl.hp.com/public_software/

[10]

Jiang, S. and Zhang, X. 2002. LIRS: An efficient low inter-reference recency set replacement policy to improve buffer cache performance. In Proceedings of the ACM SIGMETRICS Conference on Measurement and Modeling of Computer Systems. 31--42.

Digital Library

[11]

Jiang. S., Ding, X., Chen, F., Tan, E., and Zhang. X. 2005. DULO: An effective buffer cache management scheme to exploit both temporal and spatial localities. In Proceedings of the USENIX Conference on File and Storage Technologies (Fast).

Digital Library

[12]

Johnson, T. and Shasha, D. 1994. 2Q: A low overhead high performance buffer management replacement algorithm,” In Proceedings of the VLDB Conference. 297--306.

Digital Library

[13]

Lee, D., Choi, J., Kim, J. H., Noh, S. H., Mim, S. L., Cho, Y., and Kim, C. S. 2001. LRFU: A spectrum of policies that subsumes the least recently used and least frequently used policies. IEEE Trans. Comput. 50, 12, 1352--1360.

Digital Library

[14]

Li, X., Aboulnaga, A., Sachedina, A., Salem, K., and Gao, S. B. 2005. Second-tier cache management using write hints. In Proceedings of the 4th USENIX Conference on File and Storage Technologies (FAST), 115--128.

Digital Library

[15]

Mcvoy, L. and Staelin, C. 1996. lmbench: Portable tools for performance analysis. In Proceedings of the USENIX Technical Conference. 279--295.

Digital Library

[16]

Megiddo, N. and Modha, D. S. 2003. ARC: A self-tuning, low overhead replacement cache. In Proceedings of the USENIX File and Storage Technologies Conference (FAST), 115--130.

Digital Library

[17]

Menon, J. 1994. Performance of RAID5 disk arrays with read and write caching. Distrib. Parall. Datab. 2, 3, 261--293.

Digital Library

[18]

Menon, J. and Hartung, M. 1988. The IBM 3990 disk cache. In Proceedings of the IEEE Computer Society International COMPCON Conference.

[19]

Nelson, M. N., Welch, B. B., and Ousterhout, J. K. 1998. Caching in the Sprite network file system. ACM Trans. Comput. Syst. 6, 1, 134--154.

Digital Library

[20]

O'neil, E. J., O'neil, P. E., and Weikum, G. 1993. The LRU-K page replacement algorithm for database disk buffering. In Proceedings of the International Conference on Management of Data, 297--306.

Digital Library

[21]

Robinson, J. T. and Devarakonda, M. V. 1990. Data cache management using frequency-based replacement. In Proceedings of the ACM SIGMETRIC Conference on Measuring and Modeling of Computer Systems, 134--142

Digital Library

[22]

Ruemmler, C. and Wilkes, J. 1993. A trace-driven analysis of disk working set sizes. Tech. rep. HPL{OSR{93{23, Hewlett-Packard Laboratories, Palo Alto, CA, USA.

[23]

Salmon, B., Thereska, E., Soules, C. A. N, and Ganger, G. R. 2003. A two-tiered software architecture for automated tuning of disk layouts. In Proceedings of the Workshop on Algorithms and Architectures for Self-Managing Systems.

[24]

SPC-1 TRACES. http://traces.cs.umass.edu/index.php/Storage/Storage

[25]

TPC-C TRACE. http://tds.cs.byu.edu/tds/tracelist.jsp?searchby=attribute&type=Disk+I%2FO&length=All&platform=All&cache=All&pageNum=0&searchAction=Go&x=52&y=19

[26]

UNH PROJECT. 2006. http://unh-iscsi.sourceforge.net/

[27]

Yadgar, G. and Factor, M. 2007. Karma: Know-it-all replacement for a multilevel cache. In Proceedings of the USENIX File and Storage Technologies Conference (FAST).

Digital Library

[28]

Zhou, Y. and Philbin, J. F. 2001. The multi-queue replacement algorithm for second level buffer caches. In Proceedings of the USENIX Annual Technical Conference. 91--104.

Digital Library

Cited By

Pan CWang XLuo YWang Z(2021)Penalty- and Locality-aware Memory Allocation in Redis Using Enhanced AETACM Transactions on Storage10.1145/344757317:2(1-45)Online publication date: 28-May-2021
https://dl.acm.org/doi/10.1145/3447573
Nicolae BRiteau PZhen ZKeahey K(2019)Transparent Throughput Elasticity for Modern Cloud StorageApplying Integration Techniques and Methods in Distributed Systems and Technologies10.4018/978-1-5225-8295-3.ch007(156-191)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-8295-3.ch007
Jeremic NParzyjegla HMuhl G(2019)An Adaptive SSD Cache Architecture Simultaneously Using Multiple Caches2019 IEEE International Conference on Networking, Architecture and Storage (NAS)10.1109/NAS.2019.8834724(1-10)Online publication date: Aug-2019
https://doi.org/10.1109/NAS.2019.8834724
Show More Cited By

Index Terms

SOPA: Selecting the optimal caching policy adaptively
1. Information systems
  1. Information storage systems
    1. Record storage systems
    2. Storage management
2. Software and its engineering
  1. Software organization and properties
    1. Contextual software domains
      1. Operating systems
        File systems management
        Memory management

Recommendations

Caching across heterogeneous information sources: an object-based approach
Information processing and technology

Information exchange has been increased drastically and rapidly due to the major large scale Internet expansion. The structure of information sources worldwide has been altered and both structured and semi-structured data are stored in various ...
Streaming Machine Learning for Supporting Data Prefetching in Modern Data Storage Systems
AI4Sys '23: Proceedings of the First Workshop on AI for Systems

Modern data storage systems optimize data access by distributing data across multiple storage tiers and caches, based on numerous tiering and caching policies. The policies' decisions, and in particular the ones related to data prefetching, can severely ...
Performance study of a collaborative method for hierarchical caching in proxy servers
WWW7: Proceedings of the seventh international conference on World Wide Web 7

Reviews

Reviewer: Seetharami R Seelam

Wang et al. describe a framework for adaptively selecting input/output (I/O) caching policy when given a set of policies. The main idea is to capture a set of policies and the data required, in the form of a module, and select a policy that is appropriate for a given workload, based on the current policy cache hit rate. If the hit rate falls below a user-definable threshold, other policies are evaluated on the metadata captured from the workload. It is well known that there is no "silver bullet"-that is, there is no single caching policy that will result in optimal caching performance. That being said, this paper provides a sound framework for realizing multiple policies and allowing for optimal cache performance for applications. Online Computing Reviews Service

Access critical reviews of Computing literature here

Become a reviewer for Computing Reviews.

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Storage

ACM Transactions on Storage Volume 6, Issue 2

July 2010

89 pages

ISSN:1553-3077

EISSN:1553-3093

DOI:10.1145/1807060

Issue’s Table of Contents

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 July 2010

Accepted: 01 May 2010

Revised: 01 May 2010

Received: 01 May 2010

Published in TOS Volume 6, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

17
Total Citations
View Citations
683
Total Downloads

Downloads (Last 12 months)12
Downloads (Last 6 weeks)3

Reflects downloads up to 09 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Pan CWang XLuo YWang Z(2021)Penalty- and Locality-aware Memory Allocation in Redis Using Enhanced AETACM Transactions on Storage10.1145/344757317:2(1-45)Online publication date: 28-May-2021
https://dl.acm.org/doi/10.1145/3447573
Nicolae BRiteau PZhen ZKeahey K(2019)Transparent Throughput Elasticity for Modern Cloud StorageApplying Integration Techniques and Methods in Distributed Systems and Technologies10.4018/978-1-5225-8295-3.ch007(156-191)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-8295-3.ch007
Jeremic NParzyjegla HMuhl G(2019)An Adaptive SSD Cache Architecture Simultaneously Using Multiple Caches2019 IEEE International Conference on Networking, Architecture and Storage (NAS)10.1109/NAS.2019.8834724(1-10)Online publication date: Aug-2019
https://doi.org/10.1109/NAS.2019.8834724
Gu RLi CShu PYuan CHuang Y(2019)Adaptive cache policy scheduling for big data applications on distributed tiered storage systemConcurrency and Computation: Practice and Experience10.1002/cpe.513831:15Online publication date: 17-Jan-2019
https://doi.org/10.1002/cpe.5138
Cheng YXiang YChen WHassan HAlelaiwi A(2018)Efficient cache resource aggregation using adaptive multi-level exclusive caching policiesFuture Generation Computer Systems10.1016/j.future.2017.09.04486(964-974)Online publication date: Sep-2018
https://doi.org/10.1016/j.future.2017.09.044
Priyadarshi RSoni SNath V(2018)Energy efficient cluster head formation in wireless sensor networkMicrosystem Technologies10.1007/s00542-018-3873-724:12(4775-4784)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1007/s00542-018-3873-7
Waldspurger CSaemundson TAhmad IPark NSilva DFord B(2017)Cache modeling and optimization using miniature simulationsProceedings of the 2017 USENIX Conference on Usenix Annual Technical Conference10.5555/3154690.3154737(487-498)Online publication date: 12-Jul-2017
https://dl.acm.org/doi/10.5555/3154690.3154737
Bhuiyan MWu JWang GWang THassan M(2017)e-SamplingACM Transactions on Autonomous and Adaptive Systems10.1145/299415012:1(1-29)Online publication date: 27-Mar-2017
https://dl.acm.org/doi/10.1145/2994150
Souza ÉNakamura EPazzi R(2016)Target Tracking for Sensor NetworksACM Computing Surveys10.1145/293863949:2(1-31)Online publication date: 30-Jun-2016
https://dl.acm.org/doi/10.1145/2938639
Nicolae BRiteau PKeahey K(2015)Towards Transparent Throughput Elasticity for IaaS Cloud StorageInternational Journal of Distributed Systems and Technologies10.4018/IJDST.20151001026:4(21-44)Online publication date: 1-Oct-2015
https://dl.acm.org/doi/10.4018/IJDST.2015100102
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents