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Feasibility of container orchestration for adaptive performance isolation in multi-tenant SaaS applications

Authors:

Emad Heydari Beni,

Wouter JoosenAuthors Info & Claims

SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing

Pages 162 - 169

https://doi.org/10.1145/3341105.3374034

Published: 30 March 2020 Publication History

Abstract

SaaS application instances typically serve multiple tenants to improve cost-efficiency. This results in the need for adaptive performance isolation between tenants in order to guarantee custom service level objectives (SLOs) about request latency or throughput. Current solutions, which are based on request scheduling algorithms, suffer from SLO instability under globally varying workloads. This means that the configuration for an SLO has to be recalibrated when total workload patterns change such as an increase or decrease in the number of subscribed tenants, or the application becomes co-located with other types of resource-intensive applications. Lately container technology such as Docker and container orchestration frameworks like Kubernetes have been used to increase cost-efficiency, multi-tenancy and elasticity. This paper investigates if the problem of adaptive performance isolation can be mapped to resource management concepts of Kubernetes through a series of experiments. These experiments show that Kubernetes provides good support for QoS differentiation and adaptive resource allocation by grouping tenants according to their SLO class (e.g gold vs bronze) in different containers. Moreover, SLO instability does not occur when co-locating these containers with other container-based applications provided that a few interferences between CPU-, memory- and disk-io intensive applications are taken into account. However SLO instability does occur when the number of subscribed tenants changes. This latter problem is not caused by the replication and auto-scaling concepts of Kubernetes, but by a non-linear resource scaling phenomenon that is inherent when the goal is to meet multiple custom SLOs in a cost-optimal way.

References

[1]

Brendan Burns, Brian Grant, David Oppenheimer, Eric Brewer, and John Wilkes. 2016. Borg, Omega, and Kubernetes. Commun. ACM 59, 5 (2016), 50--57.

Digital Library

[2]

Jeff Dean. 2017. Latency Numbers Every Programmer Should Know. https://gist.github.com/jboner/2841832, last checked on 2017-06-08.

[3]

Wes Felter, Alexandre Ferreira, RamRajamony, and Juan Rubio. 2015. An updated performance comparison of virtual machines and Linux containers. 2015 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) (2015). http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?amumber=7095802

[4]

Cloud Native Computing Foundation. [n.d.]. In-place Update of Pod Resources. https://github.com/kubernetes/enhancements/blob/29a22b61241b35bb280de83edc0aee40d1bd87bf/keps/sig-autoscaling/20181106-in-place-update-of-pod-resources.md. Accessed: 2019-09-09.

[5]

Cloud Native Computing Foundation. [n.d.]. What is a Pod? https://kubernetes.io/docs/concepts/workloads/pods/pod/. Accessed: 2019-03-08.

[6]

The Linux Foundation. [n.d.]. Horizontal Pod Autoscaler. https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/. Accessed: 2019-07-29.

[7]

Google.com. [n.d.]. Kubernetes best practices: Resource requests and limits. https://cloud.google.com/blog/products/gcp/kubernetes-best-practices-resource-requests-and-limits. Accessed: 2019-03-08.

[8]

Neil J. Gunther. 2008. A general theory of computational scalability based on rational functions. arXiv preprint arXiv:0808.1431 (2008).

[9]

Dean Jacobs and Stefan Aulbach. 2007. Ruminations on Multi-Tenant Databases. BTW Proceedings 103 (2007).

[10]

Alexey Kopytov. 2017. sysbench github. https://github.com/akopytov/sysbench, last checked on 2017-04-11.

[11]

Rouven Krebs, Christof Momm, and Samuel Kounev. 2012. Architectural Concerns in Multi-tenant SaaS Applications. In The 2nd International Conference on Cloud Computing and Services Science (CLOSER 2012). ScitePress, 426--431.

[12]

Rouven Krebs, Christof Momm, and Samuel Kounev. 2014. Metrics and techniques for quantifying performance isolation in cloud environments. Science of Computer Programming 90 (2014), 116--134.

[13]

Kubernetes. 2018. Evicting end-user pods. URL: https://github.com/kubernetes/website/blob/release-1.11/content/en/docs/tasks/administer-cluster/out-of-resource.md#evicting-end-user-pods, accessed 2018-09-18.

[14]

Kubernetes. 2018. Production-Grade Container Orchestration. URL: https://kubernetes.io/, accessed 2018-01-23.

[15]

Willis Lang, Srinath Shankar, Jignesh M. Patel, and Ajay Kalhan. 2014. Towards multi-tenant performance SLOs. IEEE Transactions on Knowledge and Data Engineering 26, 6 (2014), 1447--1463.

Digital Library

[16]

Jacob Leverich and Christos Kozyrakis. 2014. Reconciling High Server Utilization and Sub-millisecond Quality-of-service. In Proceedings of the Ninth European Conference on Computer Systems (EuroSys '14). ACM.

Digital Library

[17]

Hailue Lin, Kai Sun, Shuan Zhao, and Yanbo Han. 2009. Feedback-control-based performance regulation for multi-tenant applications. In Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS.

Digital Library

[18]

John DC Little and Stephen C Graves. 2008. Little's law. In Building intuition. Springer, 81--100.

[19]

Jeanna Neefe Matthews, Wenjin Hu, Madhujith Hapuarachchi, Todd Deshane, Demetrios Dimatos, Gary Hamilton, Michael McCabe, and James Owens. 2007. Quantifying the Performance Isolation Properties of Virtualization Systems. Proceedings of the 2007 Workshop on Experimental Computer Science (ExpCS 2007) (2007), 6.

Digital Library

[20]

Larry McVoy and Carl Staelin. 1996. Lmbench: Portable Tools for Performance Analysis. In Proceedings of the 1996 Annual Conference on USENIX Annual Technical Conference (ATEC '96). USENIX Association, Berkeley, CA, USA, 23--23. http://dl.acm.org/citation.cfm?id=1268299.1268322

Digital Library

[21]

Laud Charles Ochei, Julian M. Bass, and Andrei Petrovski. 2015. Evaluating Degrees of Multitenancy Isolation: A Case Study of Cloud-Hosted GSD Tools. Proceedings - 2015 International Conference on Cloud and Autonomic Computing, ICCAC 2015 (2015).

Digital Library

[22]

M.M. Teixeira, M.J. Santana, and R.H.C. Santana. 2004. Using adaptive priority scheduling for service differentiation QoS-aware Web servers. Performance, Computing, and Communications, 2004 IEEE International Conference on (2004), 279--285. http://ieeexplore.ieee.org/xpls/abs{_}all.jsp?arnumber=1395003

[23]

Abhishek Verma, Luis Pedrosa, Madhukar Korupolu, David Oppenheimer, Eric Tune, and John Wilkes. 2015. Large-scale cluster management at Google with Borg. Eurosys (2015).

[24]

Ananya Kumar Vishnu Kannan. 2019. How Pods with resource requests are scheduled. https://github.com/kubernetes/website/blob/release-1.14/content/en/docs/concepts/configuration/manage-compute-resources-container.md#how-pods-with-resource-limits-are-run. Accessed: 2019-06-13.

[25]

Stefan Walraven, Tanguy Monheim, Eddy Truyen, and Wouter Joosen. 2012. Towards performance isolation in multi-tenant SaaS applications. Proceedings of the 7th Workshop on Middleware for Next Generation Internet Computing - MW4NG '12 (2012), 1--6.

Digital Library

[26]

Stefan Walraven, Eddy Truyen, and Wouter Joosen. 2011. A Middleware Layer for Flexible and Cost-Efficient Multi-tenant Applications. Middleware 2011 7049, i (2011), 370--389.

[27]

Miguel Gomes Xavier, Marcelo Veiga Neves, and Cesar Augusto Fonticielha De Rose. 2014. A Performance Comparison of Container-Based Virtualization Systems for MapReduce Clusters. 2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (2014), 299--306.

Digital Library

[28]

L. Zhao, S. Sakr, and A. Liu. 2015. A Framework for Consumer-Centric SLA Management of Cloud-Hosted Databases. IEEE Transactions on Services Computing 8, 4 (July 2015).

Cited By

Olabanji DFitch TMatthew O(2023)Multi-tenancy in Cloud-native Architecture: A Systematic Mapping StudyWSEAS TRANSACTIONS ON COMPUTERS10.37394/23205.2023.22.422(25-43)Online publication date: 7-Mar-2023
https://doi.org/10.37394/23205.2023.22.4
Budigiri G(2023)Secure and Scalable Policy Management in Cloud Native NetworkingProceedings of the 24th International Middleware Conference: Demos, Posters and Doctoral Symposium10.1145/3626564.3629092(11-14)Online publication date: 11-Dec-2023
https://dl.acm.org/doi/10.1145/3626564.3629092
Budigiri GBaumann CTruyen EMühlberg JJoosen W(2023)Zero-Cost In-Depth Enforcement of Network Policies for Low-Latency Cloud-Native Systems2023 IEEE 16th International Conference on Cloud Computing (CLOUD)10.1109/CLOUD60044.2023.00036(249-261)Online publication date: Jul-2023
https://doi.org/10.1109/CLOUD60044.2023.00036
Show More Cited By

Index Terms

Feasibility of container orchestration for adaptive performance isolation in multi-tenant SaaS applications
1. Software and its engineering
  1. Software notations and tools
    1. Development frameworks and environments
      1. Software as a service orchestration system
  2. Software organization and properties
    1. Extra-functional properties
      1. Software performance
    2. Software system structures
      1. Distributed systems organizing principles
        Cloud computing

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Published In

cover image ACM Conferences

SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing

March 2020

2348 pages

ISBN:9781450368667

DOI:10.1145/3341105

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University
,
Tomas Cerny
Baylor University
,
Program Chairs:
Dongwan Shin
New Mexico Tech
,
Alessio Bechini
University of Pisa, Italy

Copyright © 2020 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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Published: 30 March 2020

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Agentschap voor Innovatie door Wetenschap en Technologie

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SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing

March 30 - April 3, 2020

Brno, Czech Republic

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Olabanji DFitch TMatthew O(2023)Multi-tenancy in Cloud-native Architecture: A Systematic Mapping StudyWSEAS TRANSACTIONS ON COMPUTERS10.37394/23205.2023.22.422(25-43)Online publication date: 7-Mar-2023
https://doi.org/10.37394/23205.2023.22.4
Budigiri G(2023)Secure and Scalable Policy Management in Cloud Native NetworkingProceedings of the 24th International Middleware Conference: Demos, Posters and Doctoral Symposium10.1145/3626564.3629092(11-14)Online publication date: 11-Dec-2023
https://dl.acm.org/doi/10.1145/3626564.3629092
Budigiri GBaumann CTruyen EMühlberg JJoosen W(2023)Zero-Cost In-Depth Enforcement of Network Policies for Low-Latency Cloud-Native Systems2023 IEEE 16th International Conference on Cloud Computing (CLOUD)10.1109/CLOUD60044.2023.00036(249-261)Online publication date: Jul-2023
https://doi.org/10.1109/CLOUD60044.2023.00036
Ugwuanyi SAsif RIrvine J(2020)Network Virtualization: Proof of Concept for Remote Management of Multi-Tenant Infrastructure2020 IEEE 6th International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application (DependSys)10.1109/DependSys51298.2020.00023(98-105)Online publication date: Dec-2020
https://doi.org/10.1109/DependSys51298.2020.00023

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