Skip to main content
Log in

Intent-Driven Orchestration: Enforcing Service Level Objectives for Cloud Native Deployments

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

The introduction of microservices and functions using serverless deployment styles for cloud-native applications will trigger a shift in the orchestration paradigm towards an intent-driven model. In this model we shift from imperatively declaring an object’s state to the declaration of a set of desired intents. Intent-driven orchestration (IDO) enables the management of applications through their service level objectives (SLOs) while minimizing service owner and administrator overhead. By enabling service owners to express the desired target key performance indicator (KPI) objectives for their service components instead of declaratively defining the required state and resources, we enable ease of use and abstraction from underlying platforms. By adding a planning component to a Kubernetes-based orchestration stack, the feasibility of translating service objectives into actionable decisions is demonstrated. As this new architecture component introduces more autonomy in the control plane, a means to evaluate the results of planning is defined.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. https://edc.intel.com/content/www/us/en/products/performance/benchmarks/3rd-generation-intel-xeon-scalable-processors/.

  2. https://kubernetes.io.

  3. https://github.com/intel/intent-driven-orchestration.

  4. https://kubernetes.io/docs/tasks/extend-kubernetes/custom-resources/custom-resource-definitions/.

  5. https://kubernetes.io/docs/concepts/scheduling-eviction/kube-scheduler/.

  6. https://linkerd.io/.

  7. https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/.

References

  1. Veitch P, McGrath MJ, Bayon V. An instrumentation and analytics framework for optimal and robust NFV deployment. IEEE Commun Mag. 2015;53(2):126–33. https://doi.org/10.1109/MCOM.2015.7045400.

    Article  Google Scholar 

  2. Jackson K. Post mortem: Kubernetes Node OOM 2019. https://www.bluematador.com/blog/post-mortem-kubernetes-node-oom.

  3. Newman S. Building microservices. Sebastopol: O’Reilly Media Inc; 2015.

    Google Scholar 

  4. Jonas E, Schleier-Smith J, Sreekanti V, Tsai C, Khandelwal A, Pu Q, Shankar V, Carreira J, Krauth K, Yadwadkar NJ, Gonzalez JE, Popa RA, Stoica I, Patterson DA. Cloud programming simplified: a Berkeley view on serverless computing. CoRR abs/1902.03383, 2019;1–33. arXiv:1902.03383.

  5. Lango J. Toward software-defined slas: enterprise computing in the public cloud. Queue. 2013;11(11):20–31. https://doi.org/10.1145/2557963.2560948.

    Article  Google Scholar 

  6. Gog I, Schwarzkopf M, Gleave A, Watson RNM, Hand S. Firmament: fast, centralized cluster scheduling at scale. In: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), pp. 99–115. USENIX Association, Savannah, GA; 2016. https://www.usenix.org/conference/osdi16/technical-sessions/presentation/gog.

  7. Loomba R, Metsch T, Feehan L, Butler J. Utility-driven deployment decision making. In: Proceedings of The10th International Conference on Utility and Cloud Computing. UCC ’17, pp. 207–208. Association for Computing Machinery, New York, NY, USA; 2017. doi: https://doi.org/10.1145/3147213.3149375.

  8. Hart PE, Nilsson NJ, Raphael B. A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybern. 1968;4(2):100–7. https://doi.org/10.1109/TSSC.1968.300136.

    Article  Google Scholar 

  9. Metsch T, Ibidunmoye O, Bayon-Molino V, Butler J, Hernández-Rodriguez F, Elmroth E. Apex lake: a framework for enabling smart orchestration. In: Proceedings of the Industrial Track of the 16th International Middleware Conference. Middleware Industry ’15. Association for Computing Machinery, New York, NY, USA; 2015. https://doi.org/10.1145/2830013.2830016.

  10. Herdrich A, Verplanke E, Autee P, Illikkal R, Gianos C, Singhal R, Iyer R. Cache qos: From concept to reality in the intel® xeon® processor e5-2600 v3 product family. In: 2016 IEEE International Symposium on High Performance Computer Architecture (HPCA), pp. 657–668. IEEE Computer Society, Los Alamitos, CA, USA; 2016. doi: https://doi.org/10.1109/HPCA.2016.7446102.

  11. Iyer R. Cqos: a framework for enabling qos in shared caches of cmp platforms. In: Proceedings of the 18th Annual International Conference on Supercomputing. ICS ’04, pp. 257–266. Association for Computing Machinery, New York, NY USA; 2004. https://doi.org/10.1145/1006209.1006246.

  12. Guim F, Metsch T, Moustafa H, Verrall T, Carrera D, Cadenelli N, Chen J, Doria D, Ghadie C, Prats RG. Autonomous lifecycle management for resource-efficient workload orchestration for green edge computing. IEEE Trans Green Commun Netw. 2022;6(1):571–82. https://doi.org/10.1109/TGCN.2021.3127531.

    Article  Google Scholar 

  13. Li Q, Li B, Mercati P, Illikkal R, Tai C, Kishinevsky M, Kozyrakis C. Rambo: resource allocation for microservices using Bayesian optimization. IEEE Comput Archit Lett. 2021;20(1):46–9. https://doi.org/10.1109/LCA.2021.3066142.

    Article  Google Scholar 

  14. Roy N, Dubey A, Gokhale A. Efficient autoscaling in the cloud using predictive models for workload forecasting. In: 2011 IEEE 4th International Conference on Cloud Computing, 2011;pp. 500–507 . https://doi.org/10.1109/CLOUD.2011.42.

  15. Fernandez H, Pierre G, Kielmann T. Autoscaling web applications in heterogeneous cloud infrastructures. In: 2014 IEEE International Conference on Cloud Engineering, 2014;p. 195–204. https://doi.org/10.1109/IC2E.2014.25.

  16. Bobroff N, Kochut A, Beaty K. Dynamic placement of virtual machines for managing sla violations. In: 2007 10th IFIP/IEEE International Symposium on Integrated Network Management, 2007;p. 119–128. https://doi.org/10.1109/INM.2007.374776.

  17. Van HN, Tran FD, Menaud J-M. Sla-aware virtual resource management for cloud infrastructures. In: 2009 Ninth IEEE International Conference on Computer and Information Technology, vol. 1, 2009;p. 357–362. https://doi.org/10.1109/CIT.2009.109.

  18. Padala P, Hou K-Y, Shin KG. Zhu X, Uysal M, Wang Z, Singhal S, Merchant A. Automated control of multiple virtualized resources. In: Proceedings of the 4th ACM European Conference on Computer Systems. EuroSys ’09. Association for Computing Machinery, New York, NY, USA. 2009;pp. 13–26. https://doi.org/10.1145/1519065.1519068.

  19. Lakew EB, Klein C, Hernandez-Rodriguez F, Elmroth E. Performance-based service differentiation in clouds. In: 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 2015;p. 505–514. https://doi.org/10.1109/CCGrid.2015.145.

  20. Tomás L, Tordsson J. Improving cloud infrastructure utilization through overbooking. In: Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference. CAC ’13. Association for Computing Machinery, New York, NY, USA; 2013. doi: https://doi.org/10.1145/2494621.2494627.

  21. Caglar F, Gokhale A. ioverbook: intelligent resource-overbooking to support soft real-time applications in the cloud. In: 2014 IEEE 7th International Conference on Cloud Computing, 2014;p. 538–545. https://doi.org/10.1109/CLOUD.2014.78.

  22. Sedaghat M, Hernández F, Elmroth E. Unifying cloud management: Towards overall governance of business level objectives. In: 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 2011;p. 591–597. https://doi.org/10.1109/CCGrid.2011.65.

  23. Alipourfard O, Liu HH, Chen J, Venkataraman S, Yu M, Zhang M. Cherrypick: adaptively unearthing the best cloud configurations for big data analytics. In: 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17). USENIX Association, Boston, MA; 2017;pp. 469–482. https://www.usenix.org/conference/nsdi17/technical-sessions/presentation/alipourfard.

  24. Venkataraman S, Yang Z, Franklin M, Recht B, Stoica I. Ernest: efficient performance prediction for large-scale advanced analytics. In: 13th USENIX Symposium on Networked Systems Design and Implementation (NSDI 16). USENIX Association, Santa Clara, CA; 2016;pp. 363–378. https://www.usenix.org/conference/nsdi16/technical-sessions/presentation/venkataraman.

Download references

Funding

Financial support has been provided in part by the Knut and Alice Wallenberg Foundation (Grant No. KAW 2019.0352) and by the eSSENCE Programme under the Swedish Government’s Strategic Research Initiative.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Thijs Metsch.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Advances on Cloud Computing and Services Science” guest edited by Donald F. Ferguson, Claus Pahl and Maarten van Steen.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Metsch, T., Viktorsson, M., Hoban, A. et al. Intent-Driven Orchestration: Enforcing Service Level Objectives for Cloud Native Deployments. SN COMPUT. SCI. 4, 268 (2023). https://doi.org/10.1007/s42979-023-01698-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-023-01698-0

Keywords

Navigation