research-article

Pesto: online storage performance management in virtualized datacenters

Authors:

Ganesha Shanmuganathan,

Carl Waldspurger,

Mustafa UysalAuthors Info & Claims

SOCC '11: Proceedings of the 2nd ACM Symposium on Cloud Computing

Article No.: 19, Pages 1 - 14

https://doi.org/10.1145/2038916.2038935

Published: 26 October 2011 Publication History

Abstract

Virtualized datacenters strive to reduce costs through workload consolidation. Workloads exhibit a diverse set of IO behaviors and varying IO load that makes it difficult to estimate the IO performance on shared storage. As a result, system administrators often resort to gross overprovisioning or static partitioning of storage to meet application demands. In this paper, we introduce Pesto, a unified storage performance management system for heterogeneous virtualized datacenters. Pesto is the first system that completely automates storage performance management for virtualized datacenters, providing IO load balancing with cost-benefit analysis, per-device congestion management, and initial placement of new workloads.

At its core, Pesto constructs and adapts approximate black-box performance models of storage devices automatically, leveraging our analysis linking device throughput and latency to outstanding IOs.Experimental results for a wide range of devices and configurations validate the accuracy of these models. We implemented Pesto in a commercial product and tested its performance on tens of devices, running hundreds of test cases over the past year. End-to-end experiments demonstrate that Pesto is efficient, adapts to changes quickly and can improve workload performance by up to 19%, achieving our objective of lowering storage management costs through automation.

References

[1]

Filebench. http://solarisinternals.com/si/tools/filebench/index.php.

[2]

Iometer. http://www.iometer.org.

[3]

G. A. Alvarez, E. Borowsky, S. Go, T. H. Romer, R. Becker-Szendy, R. Golding, A. Merchant, M. Spasojevic, A. Veitch, and J. Wilkes. Minerva: An Automated Resource Provisioning Tool for Large-Scale Storage Systems. In ACM Transactions on Computer Systems, Nov. 2001.

Digital Library

[4]

E. Anderson. Simple table-based modeling of storage devices. Technical report, HPL-SSP-2001-4, HP Labs, July 2001.

[5]

E. Anderson, M. Hobbs, K. Keeton, S. Spence, M. Uysal, and A. Veitch. Hippodrome: running circles around storage administration. In Proceedings of the 1st USENIX conference on File and Storage Technologies, FAST'02, Berkeley, CA, USA, 2002. USENIX Association.

Digital Library

[6]

S. Chen and D. Towsley. The Design and Evaluation of RAID 5 and Parity Striping Disk Array Architectures. Journal on Parallel and Distributed Computing, 17(1--2):58--74, 1993.

Digital Library

[7]

S. Chen and D. Towsley. A performance evaluation of RAID architectures. IEEE Transactions on Computers, 45:1116--1130, 1996.

Digital Library

[8]

T. E. Denehy, J. Bent, F. I. Popovici, A. C. Arpaci-Dusseau, and R. H. Arpaci-Dusseau. Deconstructing storage arrays. In Proceedings of the 11th international conference on Architectural support for programming languages and operating systems, ASPLOS-XI, pages 59--71, New York, NY, USA, 2004. ACM.

Digital Library

[9]

G. Ganger. Automated disk drive characterization. http://www.pdl.cmu.edu/Dixtrac/index.shtml.

[10]

C. C. Gotlieb and G. H. MacEwen. Performance of Movable-Head Disk Storage Devices. Journal of the ACM, 20(4):604--623, 1973.

Digital Library

[11]

A. Gulati, I. Ahmad, and C. A. Waldspurger. PARDA: Proportional allocation of resources for distributed storage access. In Proceedings of the 7th conference on File and Storage Technologies, pages 85--98, Berkeley, CA, USA, 2009. USENIX Association.

Digital Library

[12]

A. Gulati, C. Kumar, and I. Ahmad. Storage Workload Characterization and Consolidation in Virtualized Environments. In Workshop on Virtualization Performance: Analysis, Characterization, and Tools (VPACT), 2009.

[13]

A. Gulati, C. Kumar, I. Ahmad, and K. Kumar. BASIL: Automated IO load balancing across storage devices. In Proceedings of the 8th USENIX conference on File and Storage Technologies, FAST'10, Berkeley, CA, USA, 2010. USENIX Association.

Digital Library

[14]

J. L. Hennessy and D. A. Patterson. Computer Architecture: A Quantitative Approach, Fourth edition. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2007.

Digital Library

[15]

R. Jain and I. Chlamtac. The P2 algorithm for dynamic calculation of quantiles and histograms without storing observations. Communications of the ACM, 28:1076--1085, October 1985.

Digital Library

[16]

T. Kelly, I. Cohen, M. Goldszmidt, and K. Keeton. Inducing models of black-box storage arrays. Technical Report HPL-2004-108, HP Labs, 2004.

[17]

M. Kim and A. Tantawi. Asynchronous disk interleaving: Approximating access delays. IEEE Transactions on Computers, 40(7):801--810, 1991.

Digital Library

[18]

E. K. Lee and R. H. Katz. An analytic performance model of disk arrays. SIGMETRICS Performance Evaluation Review, 21(1):98--109, 1993.

Digital Library

[19]

J. D. C. Little. A Proof for the Queuing Formula: L = λW. Operations Research, 9(3), 1961.

[20]

A. Mashtizadeh, E. Celebi, T. Garfinkel, and M. Cai. The Design and Evolution of Live Storage Migration in VMware ESX. In Proc. USENIX Annual Technical Conference (ATC '11), June 2011.

Digital Library

[21]

A. Merchant and P. S. Yu. An analytical model of reconstruction time in mirrored disks. Performance Evaluation, 20:115--129, May 1994.

Digital Library

[22]

A. Merchant and P. S. Yu. Analytic Modeling of Clustered RAID with Mapping Based on Nearly Random Permutation. IEEE Transactions on Computers, 45(3), 1996.

Digital Library

[23]

D. R. Merrill. Storage economics: Four principles for reducing total cost of ownership. May 2009. http://www.hds.com/assets/pdf/four-principles-for-reducing-total-cost-of-ownership.pdf.

[24]

M. P. Mesnier, M. Wachs, R. R. Sambasivan, A. X. Zheng, and G. R. Ganger. Modeling the relative fitness of storage. SIGMETRICS Performance Evaluation Review, 35(1), 2007.

Digital Library

[25]

J. D. Padhye, A. L. Rahatekar., and L. W. Dowdy. A Simple LAN File Placement Strategy. In International CMG Conference, 1995.

[26]

C. Ruemmler and J. Wilkes. An introduction to disk drive modeling. IEEE Computer, 27:17--28, 1994.

Digital Library

[27]

E. Shriver, A. Merchant, and J. Wilkes. An Analytic Behavior Model for Disk Drives with Readahead Caches and Request Reordering. SIGMETRICS Performance Evaluation Review, 26(1):182--191, 1998.

Digital Library

[28]

N. Simpson. Building a data center cost model. Jan 2010. http://www.burtongroup.com/Research/DocumentList.aspx?cid=49.

[29]

E. Thereska, M. Abd-El-Malek, J. J. Wylie, D. Narayanan, and G. R. Ganger. Informed data distribution selection in a self-predicting storage system. In International Conference on Autonomic Computing, 2006.

Digital Library

[30]

E. Thereska and G. R. Ganger. IRONModel: Robust performance models in the wild. SIGMETRICS Performance Evaluation Review, 36:253--264, June 2008.

Digital Library

[31]

A. Thomasian and J. Menon. Performance analysis of RAID5 disk arrays with a vacationing server model for rebuild mode operation. In Proceedings of the Tenth International Conference on Data Engineering, pages 111--119. IEEE Computer Society, 1994.

Digital Library

[32]

M. Uysal, G. A. Alvarez, and A. Merchant. A modular, analytical throughput model for modern disk arrays. In IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems (MASCOTS), 2001.

Digital Library

[33]

E. Varki, A. Merchant, J. Xu, and X. Qiu. Issues and challenges in the performance analysis of real disk arrays. IEEE Transactions on Parallel and Distributed Systems, 15:559--574, 2004.

Digital Library

[34]

VMware, Inc. VMware Storage VMotion: Non-Disruptive, Live Migration of Virtual Machine Storage, 2007. http://vmware.com/files/pdf/storage_vmotion_datasheet.pdf.

[35]

VMware, Inc. vSphere Resource Management Guide: ESX 4.1, ESXi 4.1, vCenter Server 4.1. 2010.

[36]

VMware, Inc. VMware vSphere. 2011. http://www.vmware.com/products/vsphere/overview.html.

[37]

VMware, Inc. VMware vStorage VMFS. 2011. http://www.vmware.com/files/pdf/VMware-vStorage-VMFS-DS-EN.pdf.

[38]

T. Voellm. Useful IO profiles for simulating various workloads. http://blogs.msdn.com/b/tvoellm/archive/2009/05/07/useful-io-profiles-for-simulating-various-workloads.aspx.

[39]

M. Wang, K. Au, A. Ailamaki, A. Brockwell, C. Faloutsos, and G. R. Ganger. Storage Device Performance Prediction with CART Models. In IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems (MASCOTS), pages 588--595, 2004.

Digital Library

[40]

P. S. Yu and A. Merchant. Analytic modeling and comparisons of striping strategies for replicated disk arrays. IEEE Transactions on Computers, 44:419--433, March 1995.

Digital Library

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Published In

cover image ACM Conferences

SOCC '11: Proceedings of the 2nd ACM Symposium on Cloud Computing

October 2011

377 pages

ISBN:9781450309769

DOI:10.1145/2038916

Program Chairs:
Jeffrey S. Chase
Duke University
,
Amr El Abbadi
Univ of California, Santa Barbara

Copyright © 2011 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]

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Published: 26 October 2011

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SOCC '11: ACM Symposium on Cloud Computing in conjunction with SOSP 2011

October 26 - 28, 2011

Cascais, Portugal

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