More Web Proxy on the site http://driver.im/

research-article

Bridging the tenant-provider gap in cloud services

Authors:

Virajith Jalaparti,

Hitesh Ballani,

Thomas Karagiannis,

Ant RowstronAuthors Info & Claims

SoCC '12: Proceedings of the Third ACM Symposium on Cloud Computing

Article No.: 10, Pages 1 - 14

https://doi.org/10.1145/2391229.2391239

Published: 14 October 2012 Publication History

Abstract

The disconnect between the resource-centric interface exposed by today's cloud providers and tenant goals hurts both entities. Tenants are encumbered by having to translate their performance and cost goals into the corresponding resource requirements, while providers suffer revenue loss due to un-informed resource selection by tenants. Instead, we argue for a "job-centric" cloud whereby tenants only specify high-level goals regarding their jobs and applications. To illustrate our ideas, we present Bazaar, a cloud framework offering a job-centric interface for data analytics applications.

Bazaar allows tenants to express high-level goals and predicts the resources needed to achieve them. Since multiple resource combinations may achieve the same goal, Bazaar chooses the combination most suitable for the provider. Using large-scale simulations and deployment on a Hadoop cluster, we demonstrate that Bazaar enables a symbiotic tenant-provider relationship. Tenants achieve their performance goals. At the same time, holistic resource selection benefits providers in the form of increased goodput.

References

[1]

G. Ananthanarayanan, S. Kandula, A. Greenberg, I. Stoica, Y. Lu, B. Saha, and E. Harris. Reining in the Outliers in Map-Reduce Clusters using Mantri. In OSDI, 2010.

Digital Library

[2]

H. Ballani, P. Costa, T. Karagiannis, and A. Rowstron. Towards Predictable Datacenter Networks. In SIGCOMM, 2011.

Digital Library

[3]

A. Borodin and R. El-Yaniv. Online Computation and Competitive Analysis. Cambridge University Press, 2005.

Digital Library

[4]

B. M. Cantrill, M. W. Shapiro, and A. H. Leventhal. Dynamic instrumentation of production systems. In USENIX ATC, 2004.

Digital Library

[5]

R. Chaiken, B. Jenkins, P.-A. Larson, B. Ramsey, D. Shakib, S. Weaver, and J. Zhou. SCOPE: easy and efficient parallel processing of massive data sets. In VLDB, 2008.

Digital Library

[6]

J. Dean and S. Ghemawat. MapReduce: Simplified Data Processing on Large Clusters. In OSDI, 2004.

Digital Library

[7]

J. Dean and S. Ghemawat. MapReduce: Simplified Data Processing on Large Clusters. Comm. of ACM, 51(1), 2008.

Digital Library

[8]

N. R. Devanur, K. Jain, B. Sivan, and C. A. Wilkens. Near optimal online algorithms and fast approximation algorithms for resource allocation problems. In EC, 2011.

Digital Library

[9]

A. Ferguson, P. Bodik, S. Kandula, E. Boutin, and R. Fonseca. Jockey: guaranteed job latency in data parallel clusters. In EuroSys, 2012.

Digital Library

[10]

A. Ganapathi, Y. Chen, A. Fox, R. H. Katz, and D. A. Patterson. Statistics-Driven Workload Modeling for the Cloud. In SMDB, 2010.

[11]

A. Greenberg, J. R. Hamilton, N. Jain, S. Kandula, C. Kim, P. Lahiri, D. A. Maltz, P. Patel, and S. Sengupta. VL2: a scalable and flexible data center network. In SIGCOMM, 2009.

Digital Library

[12]

A. Gulati, I. Ahmad, and C. A. Waldspurger. PARDA: proportional allocation of resources for distributed storage access. In FAST, 2009.

Digital Library

[13]

C. Guo, G. Lu, H. J. Wang, S. Yang, C. Kong, P. Sun, W. Wu, and Y. Zhang. SecondNet: A Data Center Network Virtualization Architecture with Bandwidth Guarantees. In CoNEXT, 2010.

Digital Library

[14]

H. Herodotou, F. Dong, and S. Babu. No One (Cluster) Size Fits All: Automatic Cluster Sizing for Data-intensive Analytics. In ACM SOCC, 2011.

Digital Library

[15]

A. Iosup, N. Yigitbasi, and D. Epema. On the Performance Variability of Production Cloud Services. Technical report, Delft University of Technology, 2010.

[16]

M. Isard, M. Budiu, Y. Yu, A. Birrell, and D. Fetterly. Dryad: Distributed Data-Parallel Programs from Sequential Building Blocks. In EuroSys, 2007.

Digital Library

[17]

M. Isard, V. Prabhakaran, J. Currey, U. Wieder, K. Talwar, and A. Goldberg. Quincy: Fair Scheduling for Distributed Computing Clusters. In SOSP, 2009.

Digital Library

[18]

N. Joukov, A. Traeger, R. Iyer, C. P. Wright, and E. Zadok. Operating system profiling via latency analysis. In OSDI, 2006.

Digital Library

[19]

A. Kamath, O. Palmon, and S. Plotkin. Routing and admission control in general topology networks with poisson arrivals. In ACM-SIAM SODA, 1996.

Digital Library

[20]

K. Kambatla, A. Pathak, and H. Pucha. Towards Optimizing Hadoop Provisioning in the Cloud. In HotCloud, 2009.

Digital Library

[21]

M. Karlsson, C. Karamanolis, and X. Zhu. Triage: Performance differentiation for storage systems using adaptive control. ACM Trans. Storage, 1, 2005.

Digital Library

[22]

M. Kremer and J. Gryz. A Survey of Query Optimization in Parallel Databases. Technical report, York University, 1999.

[23]

E. Krevat, J. Tucek, and G. R. Ganger. Disks Are Like Snowflakes: No Two Are Alike. In HotOS, 2011.

Digital Library

[24]

E. Lazowska, J. Zahorjan, S. Graham, and K. Sevcik. Quantitative system performance: computer system analysis using queuing network models. 1984.

Digital Library

[25]

S. Lee, R. Panigrahy, V. Prabhakaran, V. Ramasubramanian, K. Talwar, L. Uyeda, and U. Wieder. Validating Heuristics for Virtual Machines Consolidation. Technical Report MSR-TR-2011-9, MSR, 2011.

[26]

A. Li, X. Yang, S. Kandula, and M. Zhang. CloudCmp: comparing public cloud providers. In IMC, 2010.

Digital Library

[27]

A. Li, X. Zong, S. Kandula, X. Yand, and M. Zhang. CloudProphet: Towards Application Performance Prediction in Cloud. In SIGCOMM (Poster), 2011.

Digital Library

[28]

Michael Armburst et al. Above the Clouds: A Berkeley View of Cloud Computing. Technical report, UCB, 2009.

[29]

K. Morton, M. Balazinska, and D. Grossman. ParaTimer: a progress indicator for MapReduce DAGs. In SIGMOD, 2010.

Digital Library

[30]

J. Schad, J. Dittrich, and J.-A. Quiané-Ruiz. Runtime measurements in the cloud: observing, analyzing, and reducing variance. In VLDB, 2010.

Digital Library

[31]

A. Shieh, S. Kandula, A. Greenberg, and C. Kim. Sharing the Datacenter Network. In NSDI, 2011.

Digital Library

[32]

D. Shue, M. J. Freedman, and A. Shaikh. Performance Isolation and Fairness for Multi-Tenant Cloud Storage. In OSDI, 2012.

Digital Library

[33]

D. Tertilt and H. Krcmar. Generic Performance Prediction for ERP and SOA Applications. In ECIS, 2011.

[34]

F. Tian and K. Chen. Towards Optimal Resource Provisioning for Running MapReduce programs in Public Cloud. In CLOUD, 2011.

Digital Library

[35]

A. Verma, L. Cherkasova, and R. Campbell. ARIA: automatic resource inference and allocation for mapreduce environments. In ICAC, 2011.

Digital Library

[36]

E. Walker. Benchmarking Amazon EC2 for high-performance scientific computing. Login, 2008.

[37]

G. Wang, A. R. Butt, P. Pandey, and K. Gupta. A Simulation Approach to Evaluating Design Decisions in MapReduce Setups. In MASCOTS, 2009.

[38]

T. White. Hadoop: The Definitive Guide. O'Reilly, 2009.

Digital Library

[39]

A. Wieder, P. Bhatotia, A. Post, and R. Rodrigues. Orchestrating the Deployment of Computations in the Cloud with Conductor. In NSDI, 2012.

Digital Library

[40]

M. Zaharia, D. Borthakur, J. Sen Sarma, K. Elmeleegy, S. Shenker, and I. Stoica. Delay Scheduling: a Simple Technique for Achieving Locality and Fairness in Cluster Scheduling. In EuroSys, 2010.

Digital Library

[41]

Amazon Elastic MapReduce. http://aws.amazon.com/elasticmapreduce/.

[42]

Amazon's EC2 Generating 220M+ Annually. http://bit.ly/8rZdu.

[43]

Big Data @ Foursquare. http://goo.gl/FAmpz.

[44]

Hadoop Wiki: PoweredBy. http://goo.gl/Bbfu.

[45]

Measuring EC2 system performance. http://bit.ly/48Wui.

[46]

Mumak: Map-Reduce Simulator. http://bit.ly/MoOax.

[47]

Tom's Hardware Blog. http://bit.ly/rkjJwX.

Cited By

Darwish D(2024)Fundamental Concepts of Cloud ComputingEmerging Trends in Cloud Computing Analytics, Scalability, and Service Models10.4018/979-8-3693-0900-1.ch001(1-43)Online publication date: 22-Mar-2024
https://doi.org/10.4018/979-8-3693-0900-1.ch001
Singh ASinghal S(2023)Selection of best Cloud service using WPM Method2023 1st International Conference on Cognitive Computing and Engineering Education (ICCCEE)10.1109/ICCCEE55951.2023.10424651(1-5)Online publication date: 27-Apr-2023
https://doi.org/10.1109/ICCCEE55951.2023.10424651
FUKUCHI KYAMADA H(2022)Leveraging Scale-Up Machines for Swift DBMS Replication on IaaS Platforms Using BalenaDBIEICE Transactions on Information and Systems10.1587/transinf.2020ZDP7505E105.D:1(92-104)Online publication date: 1-Jan-2022
https://doi.org/10.1587/transinf.2020ZDP7505
Show More Cited By

Index Terms

Bridging the tenant-provider gap in cloud services
1. Networks
  1. Network services

Recommendations

On the Viability of a Cloud Virtual Service Provider
Performance evaluation review

Cloud service providers (CSPs) often face highly dynamic user demands for their resources, which can make it difficult for them to maintain consistent quality-of-service. Some CSPs try to stabilize user demands by offering sustained-use discounts to ...
Bridging the tenant-provider gap in networked cloud services
VTDC '13: Proceedings of the 7th international workshop on Virtualization technologies in distributed computing

The shared nature of the network in today's multi-tenant data centers implies that network performance for tenants can vary significantly. Network performance variability hurts application performance, which makes tenant costs unpredictable and causes ...
On the Viability of a Cloud Virtual Service Provider
SIGMETRICS '16: Proceedings of the 2016 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Science

Cloud service providers (CSPs) often face highly dynamic user demands for their resources, which can make it difficult for them to maintain consistent quality-of-service. Some CSPs try to stabilize user demands by offering sustained-use discounts to ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

SoCC '12: Proceedings of the Third ACM Symposium on Cloud Computing

October 2012

325 pages

ISBN:9781450317610

DOI:10.1145/2391229

Program Chairs:
Michael Carey
UC Irvine
,
Steven Hand
University of Cambridge

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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 October 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SOCC '12

Sponsor:

SOCC '12: ACM Symposium on Cloud Computing

October 14 - 17, 2012

California, San Jose

Acceptance Rates

Overall Acceptance Rate 169 of 722 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

86
Total Citations
View Citations
684
Total Downloads

Downloads (Last 12 months)8
Downloads (Last 6 weeks)2

Reflects downloads up to 25 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Darwish D(2024)Fundamental Concepts of Cloud ComputingEmerging Trends in Cloud Computing Analytics, Scalability, and Service Models10.4018/979-8-3693-0900-1.ch001(1-43)Online publication date: 22-Mar-2024
https://doi.org/10.4018/979-8-3693-0900-1.ch001
Singh ASinghal S(2023)Selection of best Cloud service using WPM Method2023 1st International Conference on Cognitive Computing and Engineering Education (ICCCEE)10.1109/ICCCEE55951.2023.10424651(1-5)Online publication date: 27-Apr-2023
https://doi.org/10.1109/ICCCEE55951.2023.10424651
FUKUCHI KYAMADA H(2022)Leveraging Scale-Up Machines for Swift DBMS Replication on IaaS Platforms Using BalenaDBIEICE Transactions on Information and Systems10.1587/transinf.2020ZDP7505E105.D:1(92-104)Online publication date: 1-Jan-2022
https://doi.org/10.1587/transinf.2020ZDP7505
Kim JSon MLee K(2022)MPEC: Distributed Matrix Multiplication Performance Modeling on a Scale-Out Cloud Environment for Data Mining JobsIEEE Transactions on Cloud Computing10.1109/TCC.2019.295040010:1(521-538)Online publication date: 1-Jan-2022
https://doi.org/10.1109/TCC.2019.2950400
Gao WYe ZSun PWen YZhang T(2021)ChronusProceedings of the ACM Symposium on Cloud Computing10.1145/3472883.3486978(609-623)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1145/3472883.3486978
Li BRoy RPatel TGadepally VGettings KTiwari Dde Supinski BHall MGamblin T(2021)RIBBONProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3458817.3476168(1-13)Online publication date: 14-Nov-2021
https://dl.acm.org/doi/10.1145/3458817.3476168
Park JLee K(2021)S-MPEC: Sparse Matrix Multiplication Performance Estimator on a Cloud EnvironmentCluster Computing10.1007/s10586-021-03287-326:5(2563-2576)Online publication date: 12-May-2021
https://doi.org/10.1007/s10586-021-03287-3
Ho JHsiu PChen M(2020)Deadline Flow Scheduling in Datacenters with Time-Varying Bandwidth AllocationsIEEE Transactions on Services Computing10.1109/TSC.2017.270136313:3(437-450)Online publication date: 1-May-2020
https://doi.org/10.1109/TSC.2017.2701363
Abdi MMosayyebzadeh AHajkazemi MTurk AKrieger ODesnoyers PYadgar GNoh S(2019)Caching in the multiverseProceedings of the 11th USENIX Conference on Hot Topics in Storage and File Systems10.5555/3357062.3357087(19-19)Online publication date: 8-Jul-2019
https://dl.acm.org/doi/10.5555/3357062.3357087
Kumar PDukkipati NLewis NCui YWang YLi CValancius VAdriaens JGribble SFoster NVahdat AWu JHall W(2019)PicNICProceedings of the ACM Special Interest Group on Data Communication10.1145/3341302.3342093(351-366)Online publication date: 19-Aug-2019
https://dl.acm.org/doi/10.1145/3341302.3342093
Show More Cited By

View Options

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 Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten