short-paper

Performance Modeling for Cloud Microservice Applications

Authors:

Vladimir Podolskiy,

Michael GerndtAuthors Info & Claims

ICPE '19: Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering

Pages 25 - 32

https://doi.org/10.1145/3297663.3310309

Published: 04 April 2019 Publication History

Abstract

Microservices enable a fine-grained control over the cloud applications that they constitute and thus became widely-used in the industry. Each microservice implements its own functionality and communicates with other microservices through language- and platform-agnostic API. The resources usage of microservices varies depending on the implemented functionality and the workload. Continuously increasing load or a sudden load spike may yield a violation of a service level objective (SLO). To characterize the behavior of a microservice application which is appropriate for the user, we define a MicroService Capacity (MSC) as a maximal rate of requests that can be served without violating SLO.

The paper addresses the challenge of identifying MSC individually for each microservice. Finding individual capacities of microservices ensures the flexibility of the capacity planning for an application. This challenge is addressed by sandboxing a microservice and building its performance model. This approach was implemented in a tool Terminus. The tool estimates the capacity of a microservice on different deployment configurations by conducting a limited set of load tests followed by fitting an appropriate regression model to the acquired performance data. The evaluation of the microservice performance models on microservices of four different applications shown relatively accurate predictions with mean absolute percentage error (MAPE) less than 10%.

The results of the proposed performance modeling for individual microservices are deemed as a major input for the microservice application performance modeling.

References

[1]

Peter Arijs. 2018. How to use resource requests and limits to manage resource usage of your Kubernetes cluster. https://jaxenter.com/manage-container-resource-kubernetes-141977.html Retrieved 2-October-2018 from

[2]

André Bauer, Nikolas Herbst, and Samuel Kounev. 2017. Design and Evaluation of a Proactive, Application-Aware Auto-Scaler: Tutorial Paper. In Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering (ICPE '17). ACM, New York, NY, USA, 425--428.

Digital Library

[3]

Betsy Beyer, Niall Richard Murphy, David K. Rensin, Kent Kawahara, and Stephen Thorne. 2018. The Site Reliability Workbook: Practical Ways to Implement SRE 1st ed.). O'Reilly Media, Inc., Farnham, UK.

Digital Library

[4]

Cloudmonix. 2018. CloudMonix. http://www.cloudmonix.com/ Retrieved 9-May-2018 from

[5]

Danny Yuan Daniel Jacobson and Neeraj Joshi. 2013. Scryer: Netflix's Predictive Auto Scaling Engine. https://medium.com/netflix-techblog/scryer-netflixs-predictive-auto-scaling-engine-a3f8fc922270 Retrieved 29-September-2018 from

[6]

Elastic. 2018. Elasticsearch. https://www.elastic.co/products/elasticsearch Retrieved 25-September-2018 from

[7]

R. Fernandes and S. G. Leblanc. 2005. Parametric (modified least squares) and non-parametric (Theil-Sen) linear regressions for predicting biophysical parameters in the presence of measurement errors. Remote Sensing of Environment, Vol. 95 (April 2005), 303--316.

[8]

Heapster. 2018. Heapster. https://github.com/kubernetes/heapster Retrieved 25-September-2018 from

[9]

Influxdata. 2018. InfluxDB. https://www.influxdata.com/time-series-platform/influxdb/ Retrieved 25-September-2018 from

[10]

D. Jaramillo, D. V. Nguyen, and R. Smart. 2016. Leveraging microservices architecture by using Docker technology. In Southeast Con 2016. 1--5.

[11]

Hiranya Jayathilaka, Chandra Krintz, and Rich Wolski. 2017. Performance Monitoring and Root Cause Analysis for Cloud-hosted Web Applications. In Proceedings of the 26th International Conference on World Wide Web (WWW '17). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 469--478.

Digital Library

[12]

K6. 2018. K6. https://docs.k6.io/docs Retrieved 25-September-2018 from https://docs.k6.io/docs

[13]

Nane Kratzke. 2017. About Microservices, Containers and their Underestimated Impact on Network Performance. CoRR, Vol. abs/1710.04049 (2017). arxiv: 1710.04049 http://arxiv.org/abs/1710.04049

[14]

Kubernetes. 2018a. Kubernetes Operations. https://github.com/kubernetes/kops Retrieved 25-September-2018 from

[15]

Kubernetes. 2018b. Managing Compute Resources for Containers. https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/ Retrieved 2-October-2018 from

[16]

kubernetes.io. 2018. What is Kubernetes. https://kubernetes.io/docs/concepts/overview/what-is-kubernetes/ Retrieved 3-May-2018 from

[17]

Frank Leymann, Uwe Breitenbücher, Sebastian Wagner, and Johannes Wettinger. 2017. Native Cloud Applications: Why Monolithic Virtualization Is Not Their Foundation. In Cloud Computing and Services Science, Markus Helfert, Donald Ferguson, Victor Méndez Mu n oz, and Jorge Cardoso (Eds.). Springer International Publishing, Cham, 16--40.

[18]

J. Mukherjee, M. Wang, and D. Krishnamurthy. 2014. Performance Testing Web Applications on the Cloud. In 2014 IEEE Seventh International Conference on Software Testing, Verification and Validation Workshops. 363--369.

Digital Library

[19]

V. Podolskiy, A. Jindal, M. Gerndt, and Y. Oleynik. 2018. Forecasting Models for Self-Adaptive Cloud Applications: A Comparative Study. In 2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO).

[20]

Vassilis Prevelakis and Diomidis Spinellis. 2001. Sandboxing Applications. In Proceedings of the FREENIX Track: 2001 USENIX Annual Technical Conference. USENIX Association, Berkeley, CA, USA, 119--126. http://dl.acm.org/citation.cfm?id=647054.715767

Digital Library

[21]

Chris Richardson. 2015. Introduction to Microservices. https://www.nginx.com/blog/introduction-to-microservices/ Retrieved 25-May-2018 from

[22]

F. Samreen, Y. Elkhatib, M. Rowe, and G. S. Blair. 2016. Daleel: Simplifying cloud instance selection using machine learning. In NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium. 557--563.

[23]

Amazon Web Services. 2018a. Amazon EC2 Instance Types. https://aws.amazon.com/ec2/instance-types/ Retrieved 25-September-2018 from

[24]

Amazon Web Services. 2018b. Simple and Step Scaling Policies for Amazon EC2 Auto Scaling. https://docs.aws.amazon.com/autoscaling/ec2/userguide/as-scaling-simple-step.html Retrieved 29-September-2018 from

[25]

B. Urgaonkar, P. Shenoy, A. Chandra, and P. Goyal. 2005. Dynamic Provisioning of Multi-tier Internet Applications. In Second International Conference on Autonomic Computing (ICAC'05). 217--228.

Digital Library

[26]

Q. Wang, Y. Kanemasa, J. Li, D. Jayasinghe, T. Shimizu, M. Matsubara, M. Kawaba, and C. Pu. 2013. Detecting Transient Bottlenecks in n-Tier Applications through Fine-Grained Analysis. In 2013 IEEE 33rd International Conference on Distributed Computing Systems. 31--40.

Digital Library

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Index Terms

Performance Modeling for Cloud Microservice Applications

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cover image ACM Conferences

ICPE '19: Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering

April 2019

348 pages

ISBN:9781450362399

DOI:10.1145/3297663

General Chairs:
Varsha Apte
IIT Bombay, India
,
Antinisca Di Marco
University of L'Aquila, Italy
,
Program Chairs:
Marin Litoiu
York University, Canada
,
José Merseguer
Universidad de Zaragoza, Spain

Copyright © 2019 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 the author(s) 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: 04 April 2019

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ICPE '19

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ICPE '19: Tenth ACM/SPEC International Conference on Performance Engineering

April 7 - 11, 2019

Mumbai, India

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ICPE '19 Paper Acceptance Rate 13 of 71 submissions, 18%;

Overall Acceptance Rate 252 of 851 submissions, 30%

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https://doi.org/10.3390/electronics13142885
Dakić P(2024)Software compliance in various industries using CI/CD, dynamic microservices, and containersOpen Computer Science10.1515/comp-2024-001314:1Online publication date: 12-Jul-2024
https://doi.org/10.1515/comp-2024-0013
Pavlenko ACahoon JZhu YKroth BNelson MCarter ALiao DWright TCamacho-Rodríguez JSaur KBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)Vertically Autoscaling Monolithic Applications with CaaSPER: Scalable Container-as-a-Service Performance Enhanced Resizing Algorithm for the CloudCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3653378(241-254)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3653378
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