research-article

Mitigating interference in cloud services by middleware reconfiguration

Authors:

Saurabh Bagchi,

Akshat VermaAuthors Info & Claims

Middleware '14: Proceedings of the 15th International Middleware Conference

Pages 277 - 288

https://doi.org/10.1145/2663165.2663330

Published: 08 December 2014 Publication History

Abstract

Application performance has been and remains one of top five concerns since the inception of cloud computing. A primary determinant of application performance is multi-tenancy or sharing of hardware resources in clouds. While some hardware resources can be partitioned well among VMs (such as CPUs), many others cannot (such as memory bandwidth). In this paper, we focus on understanding the variability in application performance on a cloud and explore ways for an end customer to deal with it. Based on rigorous experiments using CloudSuite, a popular Web2.0 benchmark, running on EC2, we found that interference-induced performance degradation is a reality. On a private cloud testbed, we also observed that interference impacts the choice of best configuration values for applications and middleware. We posit that intelligent reconfiguration of application parameters presents a way for an end customer to reduce the impact of interference. However, tuning the application to deal with interference is challenging because of two fundamental reasons --- the configuration depends on the nature and degree of interference and there are inter-parameter dependencies. We design and implement the IC² system (Interference-aware Cloud application Configuration) to address the challenges of detection and mitigation of performance interference in clouds. Compared to an interference-agnostic configuration, the proposed solution provides up to 29% and 40% improvement in average response time on EC2 and a private cloud testbed respectively.

References

[1]

Basic Linear Algebra Subprograms. http://www.netlib.org/blas.

[2]

X. Bu, J. Rao, and C.-Z. Xu. Coordinated self-configuration of virtual machines and appliances using a model-free learning approach. Parallel and Distributed Systems, IEEE Transactions on, 24(4):681--690, 2013.

Digital Library

[3]

I.-H. Chung and J. K. Hollingsworth. Automated cluster-based web service performance tuning. In High performance Distributed, Computing, 2004. Proceedings. 13th IEEE International Symposium on, pages 36--44. IEEE, 2004.

Digital Library

[4]

C. Delimitrou and C. Kozyrakis. Paragon: Qos-aware scheduling for heterogeneous datacenters. In Proceedings of the eighteenth international conference on Architectural support for programming languages and operating systems, ASPLOS '13, pages 77--88, New York, NY, USA, 2013. ACM.

Digital Library

[5]

Y. Diao, N. Gandhi, J. Hellerstein, S. Parekh, and D. Tilbury. Using mimo feedback control to enforce policies for interrelated metrics with application to the apache web server. In Network Operations and Management Symposium, 2002. NOMS 2002. 2002 IEEE/IFIP, pages 219--234.

[6]

EPFL. CloudSuite. http://parsa.epfl.ch/cloudsuite/cloudsuite.html.

[7]

S. Govindan, A. R. Nath, A. Das, B. Urgaonkar, and A. Sivasubramaniam. Xen and co.: communication-aware cpu scheduling for consolidated xen-based hosting platforms. In Proceedings of the 3rd international conference on Virtual execution environments, VEE '07, pages 126--136, New York, NY, USA, 2007. ACM.

Digital Library

[8]

A. Gulati, A. Merchant, and P. J. Varman. mclock: handling throughput variability for hypervisor io scheduling. In Proceedings of the 9th USENIX conference on Operating systems design and implementation, pages 1--7. USENIX Association, 2010.

Digital Library

[9]

D. Gupta, L. Cherkasova, R. Gardner, and A. Vahdat. Enforcing performance isolation across virtual machines in xen. In Proceedings of the ACM/IFIP/USENIX 2006 International Conference on Middleware, pages 342--362. Springer-Verlag New York, Inc., 2006.

Digital Library

[10]

M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The weka data mining software: An update. SIGKDD Explor. Newsl., 11(1):10--18, Nov. 2009.

Digital Library

[11]

R. J. F. Inc.). More details on today's outage, 2011. https://www.facebook.com/note.php?note\_id=431441338919\&id=9445547199.

[12]

R. Koller, A. Verma, and A. Neogi. Wattapp: An application aware power meter for shared data centers. In ICAC, 2010.

Digital Library

[13]

X. Liu, L. Sha, Y. Diao, S. Froehlich, J. L. Hellerstein, and S. Parekh. Online response time optimization of apache web server. In Proceedings of the 11th international conference on Quality of service, IWQoS'03, pages 461--478, Berlin, Heidelberg, 2003. Springer-Verlag.

Digital Library

[14]

R. P. Mahowald and M. Rounds. It buyer market guide: Cloud services. In IDCReport, July, 2013.

[15]

A. K. Maji. Httpd with Online Reconfiguration, 2014. https://github.com/amaji/httpd-online-2.4.3.git.

[16]

A. K. Maji, S. Mitra, B. Zhou, S. Bagchi, and A. Verma. IC2: Interference-aware Application Configuration in Clouds, September 2014. Technical Report, School of Electrical and Computer Engineering, Purdue University, http://docs.lib.purdue.edu/ecetr/.

[17]

T. Moscibroda and O. Mutlu. Memory performance attacks: Denial of memory service in multi-core systems. In Proceedings of 16th USENIX Security Symposium on USENIX Security Symposium, pages 1--18. USENIX Association, 2007.

Digital Library

[18]

R. Nathuji, A. Kansal, and A. Ghaffarkhah. Q-clouds: Managing performance interference effects for qos-aware clouds. In Proceedings of the 5th European Conference on Computer Systems, EuroSys '10, pages 237--250, New York, NY, USA, 2010. ACM.

Digital Library

[19]

Netcraft. May 2014 Web Server Survey, 2014. http://news.netcraft.com/archives/2014/05/07/may-2014-web-server-survey.html.

[20]

D. Novakovic, N. Vasic, S. Novakovic, D. Kostic, and R. Bianchini. Deepdive: Transparently identifying and managing performance interference in virtualized environments. In USENIX ATC, 2013.

Digital Library

[21]

Olio. The Workload. http://incubator.apache.org/olio/the-workload.html.

[22]

OProfile. OProfile. http://oprofile.sourceforge.net/about/.

[23]

Oracle. Jdk-6558100: Cms crash following parallel work queue overflow, 2011. http://bugs.sun.com/view_bug.do?bug_id=6558100.

[24]

K. T. (PCWorld). Thanks, Amazon: The Cloud Crash Reveals Your Importance, 2011, http://www.pcworld.com/article/226033/thanks\_amazon\_for\_making\_possible\_much\_of\_the\_internet.html.

[25]

T. Ristenpart, E. Tromer, H. Shacham, and S. Savage. Hey, you, get off of my cloud: exploring information leakage in third-party compute clouds. In Proceedings of the 16th ACM conference on Computer and communications security, CCS '09,' pages 199--212, New York, NY, USA, 2009. ACM.

Digital Library

[26]

B. Sharma, P. Jayachandran, A. Verma, and C. Das. Cloudpd: Problem determination and diagnosis in. shared dynamic clouds. In Proc. DSN, 2013.

Digital Library

[27]

A. Shieh, S. Kandula, A. Greenberg, and C. Kim. Seawall: performance isolation for cloud datacenter networks. In Proceedings of the 2nd USENIX conference on Hot topics in cloud computing (HotCloud), pages 1--6. USENIX Association, 2010.

Digital Library

[28]

V. Varadarajan, T. Kooburat, B. Farley, T. Ristenpart, and M. M. Swift. Resource-freeing attacks: improve your cloud performance (at your neighbor's expense). In Proceedings of the 2012 ACM conference on Computer and communications security, CCS '12, pages 281--292, New York, NY, USA, 2012. ACM.

Digital Library

[29]

A. Verma, P. Ahuja, and A. Neogi. pmapper: Power and migration cost aware application placement in virtualized systems. In Proc. Middleware, 2008.

Digital Library

[30]

A. Verma, G. Kumar, R. Koller, and A. Sen. Cosmig: Modeling the impact of reconfiguration in a cloud. In IEEE MASCOTS, 2011.

Digital Library

[31]

L. Wang, J. Xu, and M. Zhao. Application-aware cross-layer virtual machine resource management. In Proceedings of the 9th international conference on Autonomic computing, ICAC '12, pages 13--22, New York, NY, USA, 2012. ACM.

Digital Library

[32]

A. Whitaker, R. S. Cox, and S. D. Gribble. Configuration debugging as search: Finding the needle in the haystack. In Proceedings of the 6th conference on Symposium on Opearling Systems Design & Implementation, volume 6, pages 1--14, 2004.

Digital Library

[33]

B. Xi, Z. Liu, M. Raghavachari, C. H. Xia, and L. Zhang. A smart hill-climbing algorithm for application server configuration. In Proceedings of the 13th international conference on World Wide Web, pages 287--296. ACM, 2004.

Digital Library

[34]

C.-Z. Xu, J. Rao, and X. Bu. Url: A unified reinforcement learning approach for autonomic cloud management. Journal on Parallel and Distributed, Computing (JPDC), 72(2):95--105, Feb. 2012.

Digital Library

[35]

W. Zheng, R. Bianchini, and T. D. Nguyen. Automatic configuration of internet services. In Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007, EuroSys '07, pages 219--229, New York, NY, USA, 2007. ACM.

Digital Library

Cited By

Liu QYang YDu DXia YZhang PFeng JLarus JChen HBagchi SZhang Y(2024)Harmonizing efficiency and practicabilityProceedings of the 2024 USENIX Conference on Usenix Annual Technical Conference10.5555/3691992.3691993(1-17)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.5555/3691992.3691993
Wang LYang JZhai JYang G(2024)Optimizing I/O Performance Through Effective vCPU Scheduling Interference ManagementIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.332929835:12(2315-2330)Online publication date: Dec-2024
https://doi.org/10.1109/TPDS.2023.3329298
Liu QDu DXia YZhang PChen H(2023)The Gap Between Serverless Research and Real-world SystemsProceedings of the 2023 ACM Symposium on Cloud Computing10.1145/3620678.3624785(475-485)Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1145/3620678.3624785
Show More Cited By

Index Terms

Mitigating interference in cloud services by middleware reconfiguration
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
    2. Search engine architectures and scalability
      1. Distributed retrieval
      2. Peer-to-peer retrieval
  2. Information storage systems
    1. Storage architectures
      1. Distributed storage
2. Software and its engineering
  1. Software notations and tools
    1. Software configuration management and version control systems

Recommendations

ICE: An Integrated Configuration Engine for Interference Mitigation in Cloud Services
ICAC '15: Proceedings of the 2015 IEEE International Conference on Autonomic Computing

Performance degradation due to imperfect isolation of hardware resources such as cache, network, and I/O has been a frequent occurrence in public cloud platforms. A web server that is suffering from performance interference degrades interactive user ...
Middleware enabled data sharing on cloud storage services
MW4SOC '10: Proceedings of the 5th International Workshop on Middleware for Service Oriented Computing

With the emergence of public cloud storage platforms like Amazon, Microsoft and Google etc, individual applications and some enterprise storage are being increasingly deployed on Clouds. However, dynamic data sharing in public clouds face problems of ...
Middleware and Cloud Computing: Oracle on Amazon Web Services (AWS), Rackspace Cloud and RightScale

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

Middleware '14: Proceedings of the 15th International Middleware Conference

December 2014

334 pages

ISBN:9781450327855

DOI:10.1145/2663165

General Chair:
Laurent Réveillère
LaBRI, University of Bordeaux, France
,
Program Chairs:
Lucy Cherkasova
HP Labs, USA
,
François Taïani
Université de Rennes 1 / IRISA, France

Copyright © 2014 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

Orange
Conseil Régional d'Aquitaine
LaBRI: LaBRI
Raytheon BBN Technologies: Raytheon BBN Technologies
ACM: Association for Computing Machinery
Red Hat JBoss Middleware: Red Hat JBoss Middleware
Bordeaux: City of Bordeaux
USENIX Assoc: USENIX Assoc
GDR ASR: GDR Architecture, Systèmes et Réseaux
IBM: IBM
HP: HP
IFIP

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 December 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Division of Civil, Mechanical and Manufacturing Innovation

Conference

Middleware '14

Sponsor:

LaBRI
Raytheon BBN Technologies
ACM
Red Hat JBoss Middleware
Bordeaux
USENIX Assoc
GDR ASR
IBM
HP

Middleware '14: 15th International Middleware Conference

December 8 - 12, 2014

Bordeaux, France

Acceptance Rates

Middleware '14 Paper Acceptance Rate 27 of 144 submissions, 19%;

Overall Acceptance Rate 203 of 948 submissions, 21%

Upcoming Conference

MIDDLEWARE '25

26th International Middleware Conference

December 15 - 19, 2025

Nashville , TN , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

59
Total Citations
View Citations
358
Total Downloads

Downloads (Last 12 months)18
Downloads (Last 6 weeks)3

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Liu QYang YDu DXia YZhang PFeng JLarus JChen HBagchi SZhang Y(2024)Harmonizing efficiency and practicabilityProceedings of the 2024 USENIX Conference on Usenix Annual Technical Conference10.5555/3691992.3691993(1-17)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.5555/3691992.3691993
Wang LYang JZhai JYang G(2024)Optimizing I/O Performance Through Effective vCPU Scheduling Interference ManagementIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.332929835:12(2315-2330)Online publication date: Dec-2024
https://doi.org/10.1109/TPDS.2023.3329298
Liu QDu DXia YZhang PChen H(2023)The Gap Between Serverless Research and Real-world SystemsProceedings of the 2023 ACM Symposium on Cloud Computing10.1145/3620678.3624785(475-485)Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1145/3620678.3624785
Liu JWang QZhang SHu LDa Silva D(2023)SoraProceedings of the 24th International Middleware Conference10.1145/3590140.3592851(43-56)Online publication date: 27-Nov-2023
https://dl.acm.org/doi/10.1145/3590140.3592851
Im JKim JKwon YMaeng S(2023)On-Demand Virtualization for Post-Copy OS Migration in Bare-Metal CloudIEEE Transactions on Cloud Computing10.1109/TCC.2022.317948511:2(2028-2038)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TCC.2022.3179485
Stewart CMorris NChen LBirke R(2022)Performance Modeling for Short-Term Cache AllocationProceedings of the 51st International Conference on Parallel Processing10.1145/3545008.3545094(1-11)Online publication date: 29-Aug-2022
https://dl.acm.org/doi/10.1145/3545008.3545094
Romero EStewart CLi AHale KMorris NBellavista PZhang KGherbi ABagchi SPatiño MDi Modica GGascon-Samson J(2022)BoltProceedings of the 23rd ACM/IFIP International Middleware Conference10.1145/3528535.3531519(94-106)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3528535.3531519
Liu JZhang SWang QWei J(2022)Coordinating Fast Concurrency Adapting With Autoscaling for SLO-Oriented Web ApplicationsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2022.315151233:12(3349-3362)Online publication date: 1-Dec-2022
https://doi.org/10.1109/TPDS.2022.3151512
Samreen FBlair GElkhatib Y(2022)Transferable Knowledge for Low-Cost Decision Making in Cloud EnvironmentsIEEE Transactions on Cloud Computing10.1109/TCC.2020.298938110:3(2190-2203)Online publication date: 1-Jul-2022
https://doi.org/10.1109/TCC.2020.2989381
Kim IHwang JWang WHumphrey M(2022)Guaranteeing Performance SLAs of Cloud Applications Under Resource StormsIEEE Transactions on Cloud Computing10.1109/TCC.2020.298537210:2(1329-1343)Online publication date: 1-Apr-2022
https://doi.org/10.1109/TCC.2020.2985372
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