The Salmon Algorithm Spawning with Kubernetes
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The document discusses the application of the salmon algorithm, a metaheuristic for combinatorial optimization inspired by genetic algorithms and ant colony optimization, within a Kubernetes environment. It outlines various practical considerations, implementation steps, experimental results, and future work related to using Kubernetes as a research platform, particularly with Compute Canada. The document also highlights the advantages and challenges of Kubernetes for researchers, emphasizing its potential for automating reproducible workflows despite a steep learning curve.
![DNA Fragment Assembly Problem to TSP
The layout phase of FAP can be translated into a TSP problem[19]
(Mallen-Fullerton,Fernandez-Anaya) with the cities representing fragments and
the distances representing overlap scores.
Two minor changes:
Negate Total: Minimization to Maximization
Remove distance to last city since FAP is not circular (insertion of dummy city is
one solution)](https://image.slidesharecdn.com/lindseytulloch-thesalmonalgorithmspawningwithkubernetes-190909212207/85/The-Salmon-Algorithm-Spawning-with-Kubernetes-8-320.jpg)
![References
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http://www.inderscienceonline.com/doi/abs/10.1504/IJBIC.2013.058912
[20] R. M. Karp, “Reducibility among combinatorial problems,” in Complexity of Computer Computations: Proc. of a Symp. on the
Complexity of Computer Computations, ser. The IBM Research Symposia Series, R. E. Miller and J. W. Thatcher, Ed. New York,
NY: Plenum Press, 1972, pp. 85–103. [Online]. Available: http://cgi.di.uoa.gr/ ∼ sgk/teaching/grad/handouts/karp.pdf
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[25] J. Hughes, “A Study of Ordered Gene Problems Featuring DNA Error Correction and DNA Fragment Assembly with a Variety
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Available: http://www.dr.library.brocku.ca/handle/10464/5600
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onyiny-ang/4f90-kubernetes,” Jul. 2019. [Online]. Available: https://github.com/onyiny-ang/4F90-kubernetes](https://image.slidesharecdn.com/lindseytulloch-thesalmonalgorithmspawningwithkubernetes-190909212207/85/The-Salmon-Algorithm-Spawning-with-Kubernetes-44-320.jpg)
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The Salmon Algorithm Spawning with Kubernetes
- 1. The Salmon Algorithm Spawning with Kubernetes Lindsey Tulloch
- 2. Lindsey Tulloch Also likes: books, cats, coffee, bicycles, adventures https://github.com/kubernetes-sigs/federation-v2 - Software Engineering Intern @ Red Hat - Multicluster May 2017 - Aug 2018 - Tekton Extension May 2019 - Thursday - Kubernetes Release Team - 1.12, 1.14, 1.15, 1.16 Release Notes - Masters Student @ University of Waterloo (as of next week!)
- 3. Spawning with Kubernetes ● Introduction and rationale ● Salmon ● Practical Considerations ● Containerization ● Compute Canada ● Implementation ● Experimental results ● Conclusion and Future Work
- 5. Salmon
- 6. The Salmon Algorithm ● John Orth, 2012 ● metaheuristic algorithm for combinatorial optimization problems inspired by: ○ Genetic Algorithms (population, generations, parent and child memories) ○ Ant Colony optimization (flow of water attracts salmon) ○ Salmon spawning behaviour* Each salmon contains two lists: - tabu list: vertices in the current path under construction - memory list: copy of the parent's completed tabu list. *The behaviour of the software salmon is idealized. We make no claim that real salmon behave in exactly this fashion.
- 7. The Salmon Algorithm Previous work: - Orth (2012): Error Correcting Codes, Code Clique Equivalence, TSP - Orth, Houghten,Tulloch (2017) and Tulloch, Houghten (2017): Param-ILS to tune Parameters, TSP, DNA Fragment Assembly - Shows promising results for TSP - might be useful on other combinatorial optimization problems.
- 8. DNA Fragment Assembly Problem to TSP The layout phase of FAP can be translated into a TSP problem[19] (Mallen-Fullerton,Fernandez-Anaya) with the cities representing fragments and the distances representing overlap scores. Two minor changes: Negate Total: Minimization to Maximization Remove distance to last city since FAP is not circular (insertion of dummy city is one solution)
- 9. Why Kube? (if it ain’t broke. . .) - Increased use of containers (including Singularity, Charliecloud, Docker). - Research moving away from strict “job” style workflows. - Adoption of data-streaming and in-flight processing. - Greater use of interactive Science Gateways. - Dependence on other more persistent services--not aligned with HPC systems - Increasing demand for reproducibility. (if it ain’t broke. . .) optimal, fix it!
- 10. 10 Advantages: - Same API across ALL cloud resources with predictable results - Promise of gitops: abstract away the running and processing of research jobs - Very popular, lots of talented engineers working on it, thriving community - Long-term outlook looks good Does Kubernetes make sense as a research platform?
- 12. Compute Canada Regional and Government Partners
- 14. Compute Canada ● Not-for-profit corporation ● Membership includes most of Canada’s major research universities ● All Canadian faculty members have access to Compute Canada systems and can sponsor others: - students - postdocs - external collaborators ● No fee for Canadian university faculty ● Reduced fee for federal laboratories and not-for-profit orgs
- 15. Compute Canada ● Compute and storage resources, data centres ● Team of ~200 experts in utilization of advanced research computing ● 100s of research software packages ● Cloud compute and storage (openstack, owncloud) ● 5-10 Data Centres ● 300,000 cores ● 12 Pflops, 50+ PB
- 16. Compute Canada Researchers drive innovation ● The CC user base is broadening, bringing a broader set of needs. ● Tremendous interest in services enabling Research Data Management (RDM)
- 17. Containerization
- 18. Docker This is Docker This is Docker on HPC
- 19. Alternatives
- 20. 20 Advantages: - Same API across ALL cloud resources with predictable results - GitOps - Very popular, Disadvantages: - Complete overhaul of existing systems (Kubernetes and HPC are not compatible at present) - Huge learning curve - + yaml, docker, github, argo, kubeflow - Desert of Expertise in academia - Concern that corporate open source may not be as friendly for academia as we’d like to think Does Kubernetes make sense as a research platform?
- 21. Implementation
- 22. Salmon on Kubernetes ● Arbutus Cloud Project Access ○ Openstack ○ Maximum Resource Allocation ■ 5 Instances, 16 VCPUs, 36GB RAM, 5 Volumes, 70GB Volume Storage ■ 5 Floating IPs, 6 Security Groups ● Deploy Kubernetes with Kubespray, Terraform and Ansible ● Containerize the Salmon Algorithm ● Create Argo workflow Salmon on Kubernetes
- 25. Salmon on KubernetesQuay Container Registry
- 26. Salmon on KubernetesContainer Logic
- 27. Salmon on KubernetesWorkflow Logic YAML YAML
- 28. Salmon runs with Argouns
- 31. Ease of Deployment For a 12 month project. . . August - Started preliminary brainstorming ahead of time September - Official project start October - Meetings with Compute Canada staff interested in Kubernetes November - Request RAS - End of November--actual RAS allocation
- 32. Ease of Deployment For a 12 month project. . . December - Kubecon Seattle, meet Compute Canada staff working on Kubernetes F2F - Ryan Taylor (@ Compute Canada) and Bob Killen (@ U of Michigan) offer support January - Figuring out how to get Kubernetes up and running on Openstack February - Need more resources than originally requested - Still working through oddities in the Kubernetes deployment
- 33. Ease of Deployment For a 12 month project. . . March - Kubernetes deployed!!! 🙌🙌🙌🙌 April - Containerizing the salmon algorithm for TSP - Figuring out and deploying Argo workflows May - Argo workflows successful - DNA Fragment Assembly containers - Salmon container git repo with CI June - Cluster Flakes due to maintenance - Paper writing
- 34. Ease of Deployment For a 12 month project. . . July - Paper writing and submissions - Presentation - Graduate. . .? NO KUBERNETES FOR 7/11 MONTHS
- 35. Conclusions & Future Work
- 36. Future of Kubernetes at CC ○ Learning curve is steep and time is precious (installing Kubernetes on bare metal just to run your workflow is not worth it) ○ Lack of expertise with essential tools (yaml, docker, github) ○ Given an existing Kubernetes cluster, with a knowledgeable admin that can assist you--Kubernetes offers a lot of benefits worth exploring ■ Completely automated, reproducible research workflows ■ Tools optimized for containerized, persistent services ■ Dependency hell eliminated ■ Roll back of Kubernetes versions can be fairly painless To Kube or not to Kube?
- 37. Providers ● Offer Kubernetes for people to consume ● Get involved with the Kube community ● Learn as much as you can ● Provide outreach to researchers and anyone that might need to be ramped up
- 38. Researchers ● Engage with research institutions ● Get involved with the Kube community ● Learn as much as you can ● Share your findings widely!
- 39. Future Research - Performance Comparisons - Git ops - Syslabs.io’s “Slurm operator” - Best practices for deploying Kubernetes on prem
- 40. Thank You!
- 41. Useful Links ● CNCF Research User Group ● CNCF Academic Mailing List ● CNCF Academic Slack (#academia) ● Batch Jobs Channel (#kubernetes-batch-jobs) ● Kubernetes Big Data User Group ● Kubernetes Machine Learning Working Group
- 42. References [1] G. M. Kurtzer, V. Sochat, and M. W. Bauer, “Singularity: Scientific containers for mobility of compute,” PLOS ONE, vol. 12, no. 5, p. e0177459, May 2017. [Online]. Available: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0177459 [2] R. Priedhorsky and T. Randles, “Charliecloud: Unprivileged containers for user-defined software stacks in HPC,” p. 12. [3] B. Burns, “The History of Kubernetes & the Community Behind It,” 2018. [Online]. Available: https://kubernetes.io/blog/2018/07/20/ the-history-of-kubernetes--the-community-behind-it/ [4] B. Burns, B. Grant, D. Oppenheimer, E. Brewer, and J. Wilkes, “Borg, Omega, and Kubernetes,” p. 24. [5] “CERN Case Study,” jun 2019. [Online]. Available: https://kubernetes.io/case-studies/cern/ [6] K. Sheets and S. Telfer, “Kubernetes, HPC and MPI,” Nov. 2018. [Online]. Available: https://www.stackhpc.com/k8s-mpi.html [7] A. Ingersoll, “The full-time job of keeping up with Kubernetes,” https://gravitational.com/blog/kubernetes-release-cycle/. [Online]. Available: https://gravitational.com/blog/kubernetes-release-cycle/ [8] J. Orth, S. Houghten, and L. Tulloch, “Evaluation of the salmon algorithm,” in 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Aug. 2017, pp. 1–8. [9] J. Orth, “The Salmon Algorithm-A New Population Based Search Metaheuristic,” Master’s Thesis, Brock University, 2012. [Online]. Available: https://dr.library.brocku.ca/handle/10464/3929 [10] C. Pahl, A. Brogi, J. Soldani, and P. Jamshidi, “Cloud Container Technologies: a State-of-the-Art Review,” IEEE Transactions on Cloud Computing, vol. PP, pp. 1–1, May 2017. [11] “Open Container Initiative, About,” https://www.opencontainers.org/faq, url = https://www.opencontainers.org/faq, language = en-US, urldate = 2019-07-01, author = The Linux Foundation, journal = Open Containers Initiative, file = Snap- shot:/home/banana/.zotero/zotero/7pdexnl0.default/zotero/storage/CJNUMT6R/faq.html:text/html.
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