research-article

The Yahoo!: cloud datastore load balancer

Authors:

Adam Silberstein,

Masood Mortazavi,

Sahaya Andrews Albert,

P.P.S. Narayan,

Brian CooperAuthors Info & Claims

CloudDB '12: Proceedings of the fourth international workshop on Cloud data management

Pages 33 - 40

https://doi.org/10.1145/2390021.2390028

Published: 29 October 2012 Publication History

Abstract

Sherpa is a large-scale distributed and globally replicated multi-tenant cloud data storage system. Sherpa scales by horizontally partitioning data into tablets and distributing these tablets across multiple servers. While Sherpa scales for increasing workload sizes by adding servers, it is vulnerable to load imbalance among tablets that cause hotspots to develop on just a few servers.

In this paper we describe Yak, the Sherpa load balancer. Yak detects hotspots and then automatically balances load by migrating tablets from the overloaded servers, and also by splitting data into new tablets. We describe Yak's design principles, algorithms and architecture. We then evaluate Yak on workloads based on Sherpa production scenarios.

References

[1]

E. Anderson, J. Hall, J. D. Hartline, M. Hobbs, A. R. Karlin, J. Saia, R. Swaminathan, and J. Wilkes. An experimental study of data migration algorithms. In Proceedings of the 5th International Workshop on Algorithm Engineering, WAE '01, pages 145--158, London, UK, 2001. Springer-Verlag.

Digital Library

[2]

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

[3]

P. Bodík, A. Fox, M. J. Franklin, M. I. Jordan, and D. A. Patterson. Characterizing, modeling, and generating workload spikes for stateful services. In SoCC, pages 241--252, 2010.

Digital Library

[4]

F. Chang, J. Dean, S. Ghemawat, W. C. Hsieh, D. A. Wallach, M. Burrows, T. Chandra, A. Fikes, and R. E. Gruber. Bigtable: a distributed storage system for structured data. In Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation - Volume 7, pages 15--15, Berkeley, CA, USA, 2006. USENIX Association.

Digital Library

[5]

B. F. Cooper, R. Ramakrishnan, U. Srivastava, A. Silberstein, P. Bohannon, H.-A. Jacobsen, N. Puz, D. Weaver, and R. Yerneni. Pnuts: Yahoo!'s hosted data serving platform. PVLDB, 1(2):1277--1288, 2008.

Digital Library

[6]

B. F. Cooper, A. Silberstein, E. Tam, R. Ramakrishnan, and R. Sears. Benchmarking Cloud Serving Systems with YCSB. In Proceedings of the 1st ACM Symposium on Cloud Computing, SoCC '10, pages 143--154, New York, NY, USA, 2010. ACM.

Digital Library

[7]

G. P. Copeland, W. Alexander, E. E. Boughter, and T. W. Keller. Data placement in bubba. In SIGMOD, pages 99--108, 1988.

Digital Library

[8]

G. DeCandia, D. Hastorun, M. Jampani, G. Kakulapati, A. Lakshman, A. Pilchin, S. Sivasubramanian, P. Vosshall, and W. Vogels. Dynamo: Amazon's highly available key-value store. In Proceedings of Twenty-First ACM SIGOPS Symposium on Operating Systems Principles, SOSP'07, pages 205--220, New York, NY, USA, 2007. ACM.

Digital Library

[9]

P. Ganesan, M. Bawa, and H. Garcia-Molina. Online balancing of range-partitioned data with applications to peer-to-peer systems. In Proceedings of the Thirtieth international conference on Very Large Data Bases - Volume 30, VLDB '04, pages 444--455. VLDB Endowment, 2004.

Digital Library

[10]

J. Hall, J. Hartline, A. R. Karlin, J. Saia, and J. Wilkes. On algorithms for efficient data migration. In Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms, SODA '01, pages 620--629, Philadelphia, PA, USA, 2001. Society for Industrial and Applied Mathematics.

Digital Library

[11]

D. R. Karger and M. Ruhl. Simple efficient load balancing algorithms for peer-to-peer systems. In Proceedings of the sixteenth annual ACM symposium on Parallelism in algorithms and architectures, SPAA '04, pages 36--43, New York, NY, USA, 2004. ACM.

Digital Library

[12]

D. Kunkle and J. Schindler. A load balancing framework for clustered storage systems. In Proceedings of the 15th international conference on High Performance Computing, HiPC'08, pages 57--72, Berlin, Heidelberg, 2008. Springer-Verlag.

Digital Library

[13]

A. Lakshman and P. Malik. Cassandra: structured storage system on a p2p network. In Proceedings of the 28th ACM symposium on Principles of distributed computing, PODC '09, pages 5--5, New York, NY, USA, 2009. ACM.

Digital Library

[14]

C. Lu, G. A. Alvarez, and J. Wilkes. Aqueduct: Online data migration with performance guarantees. In Proceedings of the 1st USENIX Conference on File and Storage Technologies, FAST '02, Berkeley, CA, USA, 2002. USENIX Association.

Digital Library

[15]

B. Trushkowsky, P. Bodík, A. Fox, M. J. Franklin, M. I. Jordan, and D. A. Patterson. The scads director: scaling a distributed storage system under stringent performance requirements. In Proceedings of the 9th USENIX conference on File and stroage technologies, FAST'11, pages 12--12, Berkeley, CA, USA, 2011. USENIX Association.

Digital Library

[16]

B. Urgaonkar, P. J. Shenoy, A. Chandra, and P. Goyal. Dynamic provisioning of multi-tier internet applications. In International Conference on Autonomic Computing (ICAC), pages 217--228, 2005.

Digital Library

Cited By

Agrawal KKuszmaul WWang ZZhao JAgrawal KPetrank E(2024)Distributed Load Balancing in the Face of Reappearance DependenciesProceedings of the 36th ACM Symposium on Parallelism in Algorithms and Architectures10.1145/3626183.3659968(321-330)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3626183.3659968
Park HGanger GAmvrosiadis GGilad YKostic DMoatti YBiran O(2023)Mimir: Finding Cost-efficient Storage Configurations in the Public CloudProceedings of the 16th ACM International Conference on Systems and Storage10.1145/3579370.3594776(22-34)Online publication date: 5-Jun-2023
https://dl.acm.org/doi/10.1145/3579370.3594776
Wang ZZhao JAgrawal KLiu HXu MLi JDehnavi MKulkarni MKrishnamoorthy S(2023)Provably Good Randomized Strategies for Data Placement in Distributed Key-Value StoresProceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming10.1145/3572848.3577501(27-38)Online publication date: 25-Feb-2023
https://dl.acm.org/doi/10.1145/3572848.3577501
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The Yahoo!: cloud datastore load balancer

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Published In

cover image ACM Conferences

CloudDB '12: Proceedings of the fourth international workshop on Cloud data management

October 2012

74 pages

ISBN:9781450317085

DOI:10.1145/2390021

General Chair:
Xiaofeng Meng
Renmin University of China, China
,
Program Chairs:
Adam Silberstein
Linkedin, USA
,
Fusheng Wang
Emory University, USA

Copyright © 2012 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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New York, NY, United States

Publication History

Published: 29 October 2012

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CIKM'12

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CIKM'12: 21st ACM International Conference on Information and Knowledge Management

October 29, 2012

Hawaii, Maui, USA

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Overall Acceptance Rate 12 of 17 submissions, 71%

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Cited By

Agrawal KKuszmaul WWang ZZhao JAgrawal KPetrank E(2024)Distributed Load Balancing in the Face of Reappearance DependenciesProceedings of the 36th ACM Symposium on Parallelism in Algorithms and Architectures10.1145/3626183.3659968(321-330)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3626183.3659968
Park HGanger GAmvrosiadis GGilad YKostic DMoatti YBiran O(2023)Mimir: Finding Cost-efficient Storage Configurations in the Public CloudProceedings of the 16th ACM International Conference on Systems and Storage10.1145/3579370.3594776(22-34)Online publication date: 5-Jun-2023
https://dl.acm.org/doi/10.1145/3579370.3594776
Wang ZZhao JAgrawal KLiu HXu MLi JDehnavi MKulkarni MKrishnamoorthy S(2023)Provably Good Randomized Strategies for Data Placement in Distributed Key-Value StoresProceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming10.1145/3572848.3577501(27-38)Online publication date: 25-Feb-2023
https://dl.acm.org/doi/10.1145/3572848.3577501
Zhang JCheng SXue ZDeng JFu CZhou WWang SChen CLi FIves ZBonifati AEl Abbadi A(2022)ESDB: Processing Extremely Skewed Workloads in Real-timeProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3526051(2286-2298)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3526051
Wang QWu SLi YXu YChen FWang PHan L(2022)HPUCache: Toward High Performance and Resource Utilization in Clustered Cache via Data Copying and Instance Merging2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS)10.1109/IWQoS54832.2022.9812925(1-10)Online publication date: 10-Jun-2022
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Cao WFeng XLiang BZhang TGao YZhang YLi FLi GLi ZIdreos SSrivastava D(2021)LogStoreProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457565(2464-2476)Online publication date: 9-Jun-2021
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Li JNelson JMichael EJin XPorts DLu SHowell J(2020)PegasusProceedings of the 14th USENIX Conference on Operating Systems Design and Implementation10.5555/3488766.3488788(387-406)Online publication date: 4-Nov-2020
https://dl.acm.org/doi/10.5555/3488766.3488788
Chen YXiang XLing XZhang XWu FGao J(2020)Dynamic Load Balance for Hot-spot and Unbalance Region Problems in HBase2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9378465(2583-2589)Online publication date: 10-Dec-2020
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Liu ZBai ZLiu ZLi XKim CBraverman VJin XStoica IMerchant AWeatherspoon H(2019)DistCacheProceedings of the 17th USENIX Conference on File and Storage Technologies10.5555/3323298.3323313(143-157)Online publication date: 25-Feb-2019
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Jin XLi XZhang HSoulé RLee JFoster NKim CStoica I(2017)NetCacheProceedings of the 26th Symposium on Operating Systems Principles10.1145/3132747.3132764(121-136)Online publication date: 14-Oct-2017
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