Serverless Workflows for Containerised Applications in the Cloud Continuum

[EN] This paper introduces an open-source platform to support serverless computing for scientific data-processing workflow-based applications across the Cloud continuum (i.e. simultaneously involving both on-premises and public Cloud platforms to process data captured at the edge). This is achieved via dynamic resource provisioning for FaaS platforms compatible with scale-to-zero approaches that minimise resource usage and cost for dynamic workloads with different elasticity requirements. The platform combines the usage of dynamically deployed auto-scaled Kubernetes clusters on on-premises Clouds and automated Cloud bursting into AWS Lambda to achieve higher levels of elasticity. A use case in public health for smart cities is used to assess the platform, in charge of detecting people not wearing face masks from captured videos. Faces are blurred for enhanced anonymity in the on-premises Cloud and detection via Deep Learning models is performed in AWS Lambda for this data-driven containerised workflow. The results indicate that hybrid workflows across the Cloud continuum can efficiently perform local data processing for enhanced regulations compliance and perform Cloud bursting for increased levels of elasticity.The authors would like to thank the European Union for the project "Artificial Intelligence in Secure PRIvacy-preserving computing coNTinuum" (AI-SPRINT), with code 101016577, funded under the H2020 Framework Programme and also the regional government of the Comunitat Valenciana (Spain) for the project IDIFEDER/2018/032 (High-Performance Algorithms for the Modeling, Simulation and early Detection of diseases in Personalized Medicine), co-funded by the European Union ERDF funds (European Regional Development Fund) of the Comunitat Valenciana 2014-2020.Risco, S.; Moltó, G.; Naranjo-Delgado, DM.; Blanquer Espert, I. (2021). Serverless Workflows for Containerised Applications in the Cloud Continuum. Journal of Grid Computing. 19(3):1-18. https://doi.org/10.1007/s10723-021-09570-2118193Agache, A., Brooker, M., Iordache, A., Liguori, A., Neugebauer, R., Piwonka, P., Popa, D.M.: Firecracker: lightweight virtualization for serverless applications. In: 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI 20), pp. 419–434. USENIX Association, Santa Clara, CA. https://www.usenix.org/conference/nsdi20/presentation/agache (2020)Al Nuaimi, E., Al Neyadi, H., Mohamed, N., Al-Jaroodi, J.: Applications of big data to smart cities. Journal of Internet Services and Applications 6(1), 25 (2015). https://doi.org/10.1186/s13174-015-0041-5.de Alfonso, C., Caballer, M., Calatrava, A., Moltó, G., Blanquer, I.: Multi-elastic Datacenters: auto-scaled virtual clusters on energy-aware physical infrastructures. Journal of Grid Computing 17(1), 191–204 (2019). https://doi.org/10.1007/s10723-018-9449-z.Amazon Web Services: Amazon EC2. https://aws.amazon.com/ec2/Amazon Web Services: AWS Batch — Easy and Efficient Batch Computing Capabilities. https://aws.amazon.com/batch/Amazon Web Services: AWS Lambda. https://aws.amazon.com/lambdaApache: OpenWhisk - Open Source Serverless Cloud Platform. https://openwhisk.apache.org/Argo: Workflows & Pipelines. https://argoproj.github.io/projects/argo/Baldini, I., Castro, P., Chang, K., Cheng, P., Fink, S., Ishakian, V., Mitchell, N., Muthusamy, V., Rabbah, R., Slominski, A., Suter, P.: Serverless computing: Current trends and open problems. In: Research Advances in Cloud Computing., pp 1–20. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-5026-8_1Baldini, I., Cheng, P., Fink, S.J., Mitchell, N., Muthusamy, V., Rabbah, R., Suter, P., Tardieu, O.: The serverless trilemma: function composition for serverless computing. In: Proceedings of the 2017 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software - Onward! 2017, pp 89–103. ACM Press, New York (2017). https://doi.org/10.1145/3133850.3133855. http://dl.acm.org/citation.cfm?doid=3133850.3133855Balouek-Thomert, D., Renart, E.G., Zamani, A.R., Simonet, A., Parashar, M.: Towards a computing continuum: Enabling edge-to-cloud integration for data-driven workflows. International Journal of High Performance Computing Applications 33(6), 1159–1174 (2019). https://doi.org/10.1177/1094342019877383.Baresi, L., Mendonça, D.F., Garriga, M., Guinea, S., Quattrocchi, G.: A unified model for the mobile-edge-cloud continuum. ACM Transactions on Internet Technology 19(2), 1–21 (2019). https://doi.org/10.1145/3226644Beckman, P., Dongarra, J., Ferrier, N., Fox, G., Moore, T., Reed, D., Beck, M.: Harnessing the computing continuum for programming our world. In: Fog Computing., pp 215–230. Wiley (2020). https://doi.org/10.1002/9781119551713.ch7Bello, J.P., Mydlarz, C., Salamon, J.: Sound analysis in smart cities. In: Computational Analysis of Sound Scenes and Events, pp 373–397. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-319-63450-0_13Brewer, E.A.: Kubernetes and the path to cloud native. In: Proceedings of the Sixth ACM Symposium on Cloud Computing - SoCC ’15, pp 167–167. Association for Computing Machinery (ACM), New York (2015). https://doi.org/10.1145/2806777.2809955. http://dl.acm.org/citation.cfm?doid=2806777.2809955Caballer, M., Blanquer, I., Moltó, G., de Alfonso, C.: Dynamic management of virtual infrastructures. Journal of Grid Computing 13(1), 53–70 (2015). https://doi.org/10.1007/s10723-014-9296-5Calatrava, A., Romero, E., Moltó, G., Caballer, M., Alonso, J.M.: Self-managed cost-efficient virtual elastic clusters on hybrid Cloud infrastructures. Future Generation Computer Systems 61, 13–25 (2016). https://doi.org/10.1016/j.future.2016.01.018Camero, A., Alba, E.: Smart City and information technology: A review. Cities 93, 84–94 (2019). https://doi.org/10.1016/j.cities.2019.04.014Casalboni, A.: AWS Lambda Power Tuning. https://github.com/alexcasalboni/aws-lambda-power-tuningChard, R., Babuji, Y., Li, Z., Skluzacek, T., Woodard, A., Blaiszik, B., Foster, I., Chard, K.: funcX: a federated function serving fabric for science. In: Proceedings of the 29th International symposium on high-performance parallel and distributed computing, pp 65–76. ACM, New York (2020). https://doi.org/10.1145/3369583.3392683Chen, C.H., Favre, J., Kurillo, G., Andriacchi, T.P., Bajcsy, R., Chellappa, R.: Camera networks for healthcare, teleimmersion, and surveillance. Computer 47(5), 26–36 (2014). https://doi.org/10.1109/MC.2014.112. http://ieeexplore.ieee.org/document/6818909/Chen, Q., Wang, W., Wu, F., De, S., Wang, R., Zhang, B., Huang, X.: A survey on an emerging area: deep learning for smart city data. IEEE Trans. Emerg. Topics Comput. Intell. 3(5), 392–410 (2019). https://doi.org/10.1109/TETCI.2019.2907718. https://ieeexplore.ieee.org/document/8704334/Christidis, A., Davies, R., Moschoyiannis, S.: Serving machine learning workloads in resource constrained environments: A serverless deployment example. In: Proceedings - 2019 IEEE 12th Conference on Service-Oriented Computing and Applications, SOCA 2019, pp. 55–63. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/SOCA.2019.00016 (2019)Christidis, A., Moschoyiannis, S., Hsu, C. H., Davies, R.: Enabling Serverless Deployment of Large-Scale AI Workloads. IEEE Access 8, 70150–70161 (2020). https://doi.org/10.1109/ACCESS.2020.2985282CNCF: Serverless Workflow: A specification for defining declarative workflow models that orchestrate Event-driven, Serverless applications. https://serverlessworkflow.ioCouturier, R.: Designing scientific applications on GPUs. Chapman and Hall/CRC. https://doi.org/10.1201/b16051. https://www.taylorfrancis.com/books/designing-scientific-applications-gpus-raphael-couturier/e/10.1201/b16051 (2013)Docker: Enterprise Container Platform. https://www.docker.com/Docker: Docker hub. https://hub.docker.com/ (2019)Dutka, Ł., Wrzeszcz, M., Lichoń, T., Słota, R., Zemek, K., Trzepla, K., Opioła, Ł., Słota, R., Kitowski, J.: Onedata - A step forward towards globalization of data access for computing infrastructures, vol. 51, pp 2843–2847 (2015). https://doi.org/10.1016/j.procs.2015.05.445. https://www.sciencedirect.com/science/article/pii/S1877050915012533Fouladi, S., Romero, F., Iter, D., Li, Q., Chatterjee, S., Kozyrakis, C., Zaharia, M., Winstein, K.: From laptop to Lambda: Outsourcing everyday jobs to thousands of transient functional containers. In: Proceedings of the 2019 USENIX Annual Technical Conference, USENIX ATC 2019, pp 475–488 (2019). https://dl.acm.org/doi/10.5555/3358807.3358848Giménez-Alventosa, V., Moltó, G., Caballer, M.: A framework and a performance assessment for serverless MapReduce on AWS Lambda. Future Generation Computer Systems 97, 259–274 (2019). https://doi.org/10.1016/j.future.2019.02.057Gimėnez-Alventosa, V., Moltȯ, G., Segrelles, J. D.: RUPER-LB: Load balancing embarrasingly parallel applications in unpredictable cloud environments. In: International Symposium on Cloud Computing and Services for High Performance Computing Systems (as part of the 18th International Conference on High Performance Computing & Simulation (HPCS 2020) (2020)GRyCAP: minicon: minimization containers. https://github.com/grycap/miniconHeath, M.T.: Scientific computing: : an introductory survey, revised second edition. Society for Industrial and Applied Mathematics, Philadelphia, PA. https://doi.org/10.1137/1.9781611975581. (2018)Ishakian, V., Muthusamy, V., Slominski, A.: Serving deep learning models in a serverless platform. In: Proceedings - 2018 IEEE International Conference on Cloud Engineering, IC2E 2018, pp. 257–262. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/IC2E.2018.00052 (2018)Ivie, P., Thain, D.: Reproducibility in scientific computing. https://doi.org/10.1145/3186266 (2018)Jonas, E., Pu, Q., Venkataraman, S., Stoica, I., Recht, B.: Occupy the cloud. In: Proceedings of the 2017 Symposium on Cloud Computing, pp 445–451. ACM, New York (2017). https://doi.org/10.1145/3127479.3128601. arXiv:1702.04024Knative: Kubernetes-based platform to deploy and manage modern serverless workloads. https://knative.dev/Linux Containers: LXC. https://linuxcontainers.org/lxc/introduction/Malawski, M., Gajek, A., Zima, A., Balis, B., Figiela, K.: Serverless execution of scientific workflows: Experiments with HyperFlow, AWS Lambda and Google Cloud functions. Future Generation Computer Systems 110, 502–514 (2020). https://doi.org/10.1016/j.future.2017.10.029. https://linkinghub.elsevier.com/retrieve/pii/167739X1730047XMcCallister, E., Grance, T., Kent, K.: Guide to protecting the confidentiality of personally identifiable information (PII). Special Publication 800-122 Guide pp. 1–59. https://doi.org/10.5555/2206206 (2010)Microsoft Azure: Azure Functions—Develop Faster With Serverless Compute. https://azure.microsoft.com/en-us/services/functions/MinIO: High Performance, Kubernetes Native Object Storage. https://min.io/Mirkhan, A.: BlurryFaces: A tool to blur faces or other regions in photos and videos. https://github.com/asmaamirkhan/BlurryFacesMorris, K.: Infrastructure as code: managing servers in the cloud. O’Reilly Media, Inc. https://www.oreilly.com/library/view/infrastructure-as-code/9781491924334/ (2016)OASIS: TOSCA simple profile in YAML version 1.3. https://docs.oasis-open.org/tosca/TOSCA-Simple-Profile-YAML/v1.3/TOSCA-Simple-Profile-YAML-v1.3.htmlOpenFaaS: Serverless functions made simple. https://www.openfaas.com/OpenStack: Open Source Cloud Computing Infrastructure. https://www.openstack.orgPavlovic, M., Etsion, Y., Ramirez, A.: On the memory system requirements of future scientific applications: Four case-studies. In: Proceedings - 2011 IEEE International Symposium on Workload Characterization, IISWC - 2011, pp 159–170 (2011). https://doi.org/10.1109/IISWC.2011.6114176Pérez, A., Moltó, G., Caballer, M., Calatrava, A.: Serverless computing for container-based architectures. Future Generation Computer Systems 83, 50–59 (2018). https://doi.org/10.1016/j.future.2018.01.022. http://linkinghub.elsevier.com/retrieve/pii/S0167739X17316485Pérez, A., Moltó, G., Caballer, M., Calatrava, A.: Serverless computing for container-based architectures. Future Generation Computer Systems 83, 50–59 (2018). https://doi.org/10.1016/j.future.2018.01.022. http://www.sciencedirect.com/science/article/pii/S0167739X17316485Pérez, A., Moltó, G., Caballer, M., Calatrava, A.: A programming model and middleware for high throughput serverless computing applications. In: Proceedings of the 34th ACM/SIGAPP symposium on applied Computing - SAC ’19, pp 106–113. ACM Press, New York (2019). https://doi.org/10.1145/3297280.3297292Perez, A., Risco, S., Naranjo, D.M., Caballer, M., Molto, G.: On-premises serverless computing for event-driven data processing applications. In: 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), pp. 414–421. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/cloud.2019.00073. https://ieeexplore.ieee.org/document/8814513 (2019)Purohit, A.: face-mask-detector: Real-Time Face mask detection using deep learning with Alert system. https://github.com/adityap27/face-mask-detector/Reisslein, M., Rinner, B., Roy-Chowdhury, A.: Smart Camera Networks [Guest editors’ introduction]. Computer 47(5), 23–25 (2014). https://doi.org/10.1109/MC.2014.134Risco, S., Moltó, G.: GPU-enabled serverless workflows for efficient multimedia processing. Applied Sciences 11(4), 1438 (2021). https://doi.org/10.3390/app11041438. https://www.mdpi.com/2076-3417/11/4/1438Ristov, S., Pedratscher, S., Fahringer, T.: AFCL: An abstract function choreography language for serverless workflow specification. Future Generation Computer Systems 114, 368–382 (2021). https://doi.org/10.1016/j.future.2020.08.012. https://linkinghub.elsevier.com/retrieve/pii/S0167739X20302648Sengupta, S.: faas-flow: Function Composition for OpenFaaS. https://github.com/s8sg/faas-flowSewak, M., Singh, S.: Winning in the era of serverless computing and function as a service. In: 2018 3rd International Conference for Convergence in Technology, I2CT 2018. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/I2CT.2018.8529465 (2018)Shields, M.: Control-versus data-driven workflows. In: Workflows for e-Science, pp 167–173. Springer , London (2007). https://link.springer.com/chapter/10.1007/978-1-84628-757-2_11Spadini, T., Silva, D.L.d.O., Suyama, R.: Sound event recognition in a smart city surveillance context. arXiv:1910.12369 (2019)Vecchiola, C., Pandey, S., Buyya, R.: High-performance cloud computing: A view of scientific applications. In: I-SPAN 2009 - The 10th International Symposium on Pervasive Systems, Algorithms, and Networks, pp 4–16 (2009). https://doi.org/10.1109/I-SPAN.2009.15