- -

Serverless Workflows for Containerised Applications in the Cloud Continuum

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Serverless Workflows for Containerised Applications in the Cloud Continuum

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: RiscoMoltoNaranjo ...
Tamaño: 1.774Mb
Formato: PDF
Descripción: Versión editorial
Abrir
dc.contributor.author Risco, Sebastián es_ES
dc.contributor.author Moltó, Germán es_ES
dc.contributor.author Naranjo-Delgado, Diana María es_ES
dc.contributor.author Blanquer Espert, Ignacio es_ES
dc.date.accessioned 2022-01-28T07:40:55Z
dc.date.available 2022-01-28T07:40:55Z
dc.date.issued 2021-09 es_ES
dc.identifier.issn 1570-7873 es_ES
dc.identifier.uri http://hdl.handle.net/10251/180321
dc.description.abstract [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. es_ES
dc.description.sponsorship 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. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Journal of Grid Computing es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Cloud computing es_ES
dc.subject Serverless computing es_ES
dc.subject Workflow es_ES
dc.subject Containers es_ES
dc.subject.classification CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL es_ES
dc.title Serverless Workflows for Containerised Applications in the Cloud Continuum es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s10723-021-09570-2 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/101016577/EU/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EDUC.INVEST.CULT.DEP//IDIFEDER%2F2018%2F032//PLATAFORMA DE COMPUTACION INTENSIVA MEDIANTE ACELERADORES GRAFICOS (GPUS) PARA SU APLICACION EN MEDICINA PERSONALIZADA/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto de Instrumentación para Imagen Molecular - Institut d'Instrumentació per a Imatge Molecular es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation 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/s10723-021-09570-2 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s10723-021-09570-2 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 18 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 19 es_ES
dc.description.issue 3 es_ES
dc.identifier.pmid 34276264 es_ES
dc.identifier.pmcid PMC8276028 es_ES
dc.relation.pasarela S\442474 es_ES
dc.contributor.funder GENERALITAT VALENCIANA es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder COMISION DE LAS COMUNIDADES EUROPEA es_ES
dc.description.references Agache, 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) es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references Amazon Web Services: Amazon EC2. https://aws.amazon.com/ec2/ es_ES
dc.description.references Amazon Web Services: AWS Batch — Easy and Efficient Batch Computing Capabilities. https://aws.amazon.com/batch/ es_ES
dc.description.references Amazon Web Services: AWS Lambda. https://aws.amazon.com/lambda es_ES
dc.description.references Apache: OpenWhisk - Open Source Serverless Cloud Platform. https://openwhisk.apache.org/ es_ES
dc.description.references Argo: Workflows & Pipelines. https://argoproj.github.io/projects/argo/ es_ES
dc.description.references 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_1 es_ES
dc.description.references Baldini, 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=3133850.3133855 es_ES
dc.description.references Balouek-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. es_ES
dc.description.references 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/10.1145/3226644 es_ES
dc.description.references Beckman, 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.ch7 es_ES
dc.description.references Bello, 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_13 es_ES
dc.description.references Brewer, 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=2806777.2809955 es_ES
dc.description.references Caballer, 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/s10723-014-9296-5 es_ES
dc.description.references Calatrava, 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.2016.01.018 es_ES
dc.description.references Camero, A., Alba, E.: Smart City and information technology: A review. Cities 93, 84–94 (2019). https://doi.org/10.1016/j.cities.2019.04.014 es_ES
dc.description.references Casalboni, A.: AWS Lambda Power Tuning. https://github.com/alexcasalboni/aws-lambda-power-tuning es_ES
dc.description.references Chard, 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/10.1145/3369583.3392683 es_ES
dc.description.references Chen, 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/ es_ES
dc.description.references 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/ es_ES
dc.description.references 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) es_ES
dc.description.references 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.2020.2985282 es_ES
dc.description.references CNCF: Serverless Workflow: A specification for defining declarative workflow models that orchestrate Event-driven, Serverless applications. https://serverlessworkflow.io es_ES
dc.description.references Couturier, 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) es_ES
dc.description.references Docker: Enterprise Container Platform. https://www.docker.com/ es_ES
dc.description.references Docker: Docker hub. https://hub.docker.com/ (2019) es_ES
dc.description.references 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/S1877050915012533 es_ES
dc.description.references Fouladi, 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/10.5555/3358807.3358848 es_ES
dc.description.references Giménez-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.2019.02.057 es_ES
dc.description.references Gimėnez-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) es_ES
dc.description.references GRyCAP: minicon: minimization containers. https://github.com/grycap/minicon es_ES
dc.description.references Heath, 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) es_ES
dc.description.references 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) es_ES
dc.description.references Ivie, P., Thain, D.: Reproducibility in scientific computing. https://doi.org/10.1145/3186266 (2018) es_ES
dc.description.references 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:1702.04024 es_ES
dc.description.references Knative: Kubernetes-based platform to deploy and manage modern serverless workloads. https://knative.dev/ es_ES
dc.description.references Linux Containers: LXC. https://linuxcontainers.org/lxc/introduction/ es_ES
dc.description.references 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/167739X1730047X es_ES
dc.description.references McCallister, 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) es_ES
dc.description.references Microsoft Azure: Azure Functions—Develop Faster With Serverless Compute. https://azure.microsoft.com/en-us/services/functions/ es_ES
dc.description.references MinIO: High Performance, Kubernetes Native Object Storage. https://min.io/ es_ES
dc.description.references Mirkhan, A.: BlurryFaces: A tool to blur faces or other regions in photos and videos. https://github.com/asmaamirkhan/BlurryFaces es_ES
dc.description.references Morris, K.: Infrastructure as code: managing servers in the cloud. O’Reilly Media, Inc. https://www.oreilly.com/library/view/infrastructure-as-code/9781491924334/ (2016) es_ES
dc.description.references 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.html es_ES
dc.description.references OpenFaaS: Serverless functions made simple. https://www.openfaas.com/ es_ES
dc.description.references OpenStack: Open Source Cloud Computing Infrastructure. https://www.openstack.org es_ES
dc.description.references Pavlovic, 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.2011.6114176 es_ES
dc.description.references Pérez, 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/S0167739X17316485 es_ES
dc.description.references Pérez, 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/S0167739X17316485 es_ES
dc.description.references Pérez, 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/10.1145/3297280.3297292 es_ES
dc.description.references Perez, 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) es_ES
dc.description.references Purohit, A.: face-mask-detector: Real-Time Face mask detection using deep learning with Alert system. https://github.com/adityap27/face-mask-detector/ es_ES
dc.description.references 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.2014.134 es_ES
dc.description.references Risco, 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/2076-3417/11/4/1438 es_ES
dc.description.references Ristov, 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/S0167739X20302648 es_ES
dc.description.references Sengupta, S.: faas-flow: Function Composition for OpenFaaS. https://github.com/s8sg/faas-flow es_ES
dc.description.references Sewak, 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) es_ES
dc.description.references 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_11 es_ES
dc.description.references Spadini, T., Silva, D.L.d.O., Suyama, R.: Sound event recognition in a smart city surveillance context. arXiv:1910.12369 (2019) es_ES
dc.description.references 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.2009.150 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem