research-article

Public Access

Performance Monitoring and Root Cause Analysis for Cloud-hosted Web Applications

Authors:

Hiranya Jayathilaka,

Chandra Krintz,

Rich WolskiAuthors Info & Claims

WWW '17: Proceedings of the 26th International Conference on World Wide Web

Pages 469 - 478

https://doi.org/10.1145/3038912.3052649

Published: 03 April 2017 Publication History

Abstract

In this paper, we describe Roots - a system for automatically identifying the "root cause" of performance anomalies in web applications deployed in Platform-as-a-Service (PaaS) clouds. Roots does not require application-level instrumentation. Instead, it tracks events within the PaaS cloud that are triggered by application requests using a combination of metadata injection and platform-level instrumentation.

We describe the extensible architecture of Roots, a prototype implementation of the system, and a statistical methodology for performance anomaly detection and diagnosis. We evaluate the efficacy of Roots using a set of PaaS-hosted web applications, and detail the performance overhead and scalability of the implementation.

References

[1]

M. K. Aguilera, J. C. Mogul, J. L. Wiener, P. Reynolds, and A. Muthitacharoen. Performance debugging for distributed systems of black boxes. In Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles, 2003.

Digital Library

[2]

N. Antonopoulos and L. Gillam. Cloud Computing: Principles, Systems and Applications. Springer Publishing Company, Incorporated, 1st edition, 2010.

Digital Library

[3]

M. Attariyan, M. Chow, and J. Flinn. X-ray: Automating root-cause diagnosis of performance anomalies in production software. In Proceedings of the 10th USENIX Conference on Operating Systems Design and Implementation, 2012.

Digital Library

[4]

V. Chandola, A. Banerjee, and V. Kumar. Anomaly detection: A survey. ACM Comput. Surv., 41(3), 2009.

Digital Library

[5]

C. Chen and L.-M. Liu. Joint estimation of model parameters and outlier effects in time series. Journal of the American Statistical Association, 88(421):284--297, 1993.

[6]

M. Y. Chen, E. Kiciman, E. Fratkin, A. Fox, and E. Brewer. Pinpoint: Problem determination in large, dynamic internet services. In Proceedings of the 2002 International Conference on Dependable Systems and Networks, 2002.

Digital Library

[7]

Amazon cloud watch, 2016. https://aws.amazon.com/cloudwatch {Accessed Sep 2016}.

[8]

G. Da Cunha Rodrigues, R. N. Calheiros, V. T. Guimaraes, G. L. d. Santos, M. B. de Carvalho, L. Z. Granville, L. M. R. Tarouco, and R. Buyya. Monitoring of cloud computing environments: Concepts, solutions, trends, and future directions. In Proceedings of the 31st Annual ACM Symposium on Applied Computing, 2016.

Digital Library

[9]

Datadog: Cloud monitoring as a service, 2016. https://www.datadoghq.com {Accessed Sep 2016}.

[10]

D. J. Dean, H. Nguyen, P. Wang, and X. Gu. Perfcompass: Toward runtime performance anomaly fault localization for infrastructure-as-a-service clouds. In Proceedings of the 6th USENIX Conference on Hot Topics in Cloud Computing, 2014.

Digital Library

[11]

Dynatrace: Digital performance management and application performance monitoring, 2016. https://www.dynatrace.com {Accessed Sep 2016}.

[12]

R. Fonseca, G. Porter, R. H. Katz, S. Shenker, and I. Stoica. X-trace: A pervasive network tracing framework. In Proceedings of the 4th USENIX Conference on Networked Systems Design #38; Implementation, 2007.

Digital Library

[13]

App Engine - Run your applications on a fully managed PaaS, 2015. "https://cloud.google.com/appengine" {Accessed March 2015}.

[14]

Google Cloud SDK Service Quotas, 2015. https://cloud.google.com/appengine/docs/quotas {Accessed March 2015}.

[15]

U. Groemping. Relative importance for linear regression in r: The package relaimpo. Journal of Statistical Software, 17(1), 2006.

[16]

Q. Guan, Z. Zhang, and S. Fu. Proactive failure management by integrated unsupervised and semi-supervised learning for dependable cloud systems. In Availability, Reliability and Security (ARES), 2011 Sixth International Conference on, 2011.

Digital Library

[17]

O. Ibidunmoye, F. Hernández-Rodriguez, and E. Elmroth. Performance anomaly detection and bottleneck identification. ACM Comput. Surv., 48(1), July 2015.

Digital Library

[18]

H. Jayathilaka, C. Krintz, and R. Wolski. Response time service level agreements for cloud-hosted web applications. In Proceedings of the Sixth ACM Symposium on Cloud Computing, 2015.

Digital Library

[19]

A. Keller and H. Ludwig. The WSLA Framework: Specifying and Monitoring Service Level Agreements for Web Services. J. Netw. Syst. Manage., 11(1), Mar. 2003.

Digital Library

[20]

R. Killick, P. Fearnhead, and I. A. Eckley. Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association, 107(500):1590--1598, 2012.

[21]

O. Kononenko, O. Baysal, R. Holmes, and M. W. Godfrey. Mining modern repositories with elasticsearch. In Proceedings of the 11th Working Conference on Mining Software Repositories, 2014.

Digital Library

[22]

C. Krintz. The appscale cloud platform: Enabling portable, scalable web application deployment. IEEE Internet Computing, 17(2), 2013.

Digital Library

[23]

Latency is Everywhere and it Costs Your Sales, 2009. http://highscalability.com/latency-everywhere-and-it-costs-you-sales-how-crush-it {Accessed Sep 2016}.

[24]

G. R. Lindeman R.H., Merenda P.F. Introduction to Bivariate and Multivariate Analysis. Scott, Foresman, Glenview, IL, 1980.

[25]

J. a. P. Magalhaes and L. M. Silva. Root-cause analysis of performance anomalies in web-based applications. In Proceedings of the 2011 ACM Symposium on Applied Computing, 2011.

Digital Library

[26]

J. P. Magalhaes and L. M. Silva. Detection of performance anomalies in web-based applications. In Proceedings of the 2010 Ninth IEEE International Symposium on Network Computing and Applications, 2010.

Digital Library

[27]

Microsoft Azure Cloud SDK Service Quotas, 2015. http://azure.microsoft.com/en-us/documentation/articles/azure-subscription-service-limits {Accessed March 2015}.

[28]

M. Natu, R. K. Ghosh, R. K. Shyamsundar, and R. Ranjan. Holistic performance monitoring of hybrid clouds: Complexities and future directions. IEEE Cloud Computing, 3(1), Jan 2016.

[29]

New relic: Application performance management and monitoring, 2016. https://newrelic.com {Accessed Sep 2016}.

[30]

H. Nguyen, Y. Tan, and X. Gu. Pal: Propagation-aware anomaly localization for cloud hosted distributed applications. In Managing Large-scale Systems via the Analysis of System Logs and the Application of Machine Learning Techniques, 2011.

Digital Library

[31]

D. Nurmi, R. Wolski, C. Grzegorczyk, G. Obertelli, S. Soman, L. Youseff, and D. Zagorodnov. The Eucalyptus open-source cloud-computing system. In IEEE/ACM International Symposium on Cluster Computing and the Grid, 2009.

Digital Library

[32]

P. Pinheiro, M. Aparicio, and C. Costa. Adoption of cloud computing systems. In Proceedings of the International Conference on Information Systems and Design of Communication, 2014.

Digital Library

[33]

M. Soni. Cloud computing basics--platform as a service (paas). Linux J., 2014(238), 2014.

Digital Library

Cited By

Wong ABucci PBeschastnikh IFedorova A(2024)Making Sense of Multi-threaded Application Performance at Scale with NonSequiturProceedings of the ACM on Programming Languages10.1145/36897938:OOPSLA2(2325-2354)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3689793
Baluta ARouf YMukherjee JJiang ZLitoiu MBalsamo SKnottenbelt WAbad CShang W(2024)Disambiguating Performance Anomalies from Workload Changes in Cloud-Native ApplicationsProceedings of the 15th ACM/SPEC International Conference on Performance Engineering10.1145/3629526.3645046(286-297)Online publication date: 7-May-2024
https://dl.acm.org/doi/10.1145/3629526.3645046
Zhang CDong ZPeng XZhang BChen MRoychoudhury APaiva AAbreu RStorey M(2024)Trace-based Multi-Dimensional Root Cause Localization of Performance Issues in Microservice SystemsProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639088(1-12)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3639088
Show More Cited By

Index Terms

Performance Monitoring and Root Cause Analysis for Cloud-hosted Web Applications

Recommendations

Root Cause Analysis Using Sequence Alignment and Latent Semantic Indexing
ASWEC '08: Proceedings of the 19th Australian Conference on Software Engineering

Automatic identification of software faults has enormous practical significance. This requires characterizing program execution behavior. Equally important is the aspect of diagnosing (finding root-cause of) faults encountered. In this article, we ...
Empirical study of root cause analysis of software failure

Root Cause Analysis (RCA) is the process of identifying project issues, correcting them and taking preventive actions to avoid occurrences of such issues in the future. Issues could be variance in schedule, effort, cost, productivity, expected results ...
Clustering intrusion detection alarms to support root cause analysis

It is a well-known problem that intrusion detection systems overload their human operators by triggering thousands of alarms per day. This paper presents a new approach for handling intrusion detection alarms more efficiently. Central to this approach ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '17: Proceedings of the 26th International Conference on World Wide Web

April 2017

1678 pages

ISBN:9781450349130

General Chairs:
Rick Barrett
W3Events
,
Rick Cummings
Murdoch University
,
Program Chairs:
Eugene Agichtein
Emory University
,
Evgeniy Gabrilovich
Google Research

Copyright © 2017 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Sponsors

IW3C2: International World Wide Web Conference Committee

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 03 April 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

NIH
ONR NEEC
Huawei Techologies
NSF
California Energy Commission

Conference

WWW '17

Sponsor:

IW3C2

WWW '17: 26th International World Wide Web Conference

April 3 - 7, 2017

Perth, Australia

Acceptance Rates

WWW '17 Paper Acceptance Rate 164 of 966 submissions, 17%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

61
Total Citations
View Citations
1,627
Total Downloads

Downloads (Last 12 months)262
Downloads (Last 6 weeks)31

Reflects downloads up to 10 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wong ABucci PBeschastnikh IFedorova A(2024)Making Sense of Multi-threaded Application Performance at Scale with NonSequiturProceedings of the ACM on Programming Languages10.1145/36897938:OOPSLA2(2325-2354)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3689793
Baluta ARouf YMukherjee JJiang ZLitoiu MBalsamo SKnottenbelt WAbad CShang W(2024)Disambiguating Performance Anomalies from Workload Changes in Cloud-Native ApplicationsProceedings of the 15th ACM/SPEC International Conference on Performance Engineering10.1145/3629526.3645046(286-297)Online publication date: 7-May-2024
https://dl.acm.org/doi/10.1145/3629526.3645046
Zhang CDong ZPeng XZhang BChen MRoychoudhury APaiva AAbreu RStorey M(2024)Trace-based Multi-Dimensional Root Cause Localization of Performance Issues in Microservice SystemsProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639088(1-12)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3639088
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
Govindarajan CNaik PGovindarajan KGoel SKodeswaran PSen SJavachandran P(2024)Syscall Analysis for Resource Stress Identification for Container Network Functions2024 IEEE 17th International Conference on Cloud Computing (CLOUD)10.1109/CLOUD62652.2024.00037(256-266)Online publication date: 7-Jul-2024
https://doi.org/10.1109/CLOUD62652.2024.00037
Mukherjee JMukhopadhyay SLitoiu MOnut IShirani POnut IOnut IBranco P(2023)Detecting Software Anomalies Using Spectrograms and Convolutional Neural NetworkProceedings of the 33rd Annual International Conference on Computer Science and Software Engineering10.5555/3615924.3615929(44-53)Online publication date: 11-Sep-2023
https://dl.acm.org/doi/10.5555/3615924.3615929
Kim SKim JIn H(2023)Multitier Web System Reliability: Identifying Causative Metrics and Analyzing Performance Anomaly Using a Regression ModelSensors10.3390/s2304191923:4(1919)Online publication date: 8-Feb-2023
https://doi.org/10.3390/s23041919
Yang FYin WWang LLi TZhao PLiu BWang PQiao BLiu YBjörkman MRajmohan SLin QZhang DChandra SBlincoe KTonella P(2023)Diffusion-Based Time Series Data Imputation for Cloud Failure Prediction at Microsoft 365Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3611643.3613866(2050-2055)Online publication date: 30-Nov-2023
https://dl.acm.org/doi/10.1145/3611643.3613866
Rong GWang HGu SXu YSun JShao DZhang H(2023)Locating Anomaly Clues for Atypical Anomalous Services: An Industrial ExplorationIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.318114320:4(2746-2761)Online publication date: 1-Jul-2023
https://doi.org/10.1109/TDSC.2022.3181143
Li LZhang XHe SKang YZhang HMa MDang YXu ZRajmohan SLin QZhang D(2023)CONAN: Diagnosing Batch Failures for Cloud Systems2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)10.1109/ICSE-SEIP58684.2023.00018(138-149)Online publication date: May-2023
https://doi.org/10.1109/ICSE-SEIP58684.2023.00018
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

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 Publication

Media

Figures

Other

Tables

View Table of Contents