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Real-time resource scaling platform for Big Data workloads on serverless environments
dc.contributor.author | Enes, Jonatan | |
dc.contributor.author | Expósito, Roberto R. | |
dc.contributor.author | Touriño, Juan | |
dc.date.accessioned | 2023-11-29T20:07:28Z | |
dc.date.available | 2023-11-29T20:07:28Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Enes, J., Expósito, R. R., & Touriño, J. (2020). 'Real-time resource scaling platform for Big Data workloads on serverless environment. Future Generation Computer Systems, 105, 361–379. https://doi.org/10.1016/j.future.2019.11.037. | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/34382 | |
dc.description | Versión final aceptada de: https://doi.org/10.1016/j.future.2019.11.037 | es_ES |
dc.description | This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-ncnd/ 4.0/. This version of the article: Enes, J., Expósito, R. R., & Touriño, J. (2020). 'Real-time resource scaling platform for Big Data workloads on serverless environments', has been accepted for publication in.: Future Generation Computer Systems, 105, 361–379. The Version of Record is available online at: https://doi.org/10.1016/j.future.2019.11.037. | es_ES |
dc.description.abstract | The serverless execution paradigm is becoming an increasingly popular option when workloads are to be deployed in an abstracted way, more specifically, without specifying any infrastructure requirements. Currently, such workloads are typically comprised of small programs or even a series of single functions used as event triggers or to process a data stream. Other applications that may also fit on a serverless scenario are stateless services that may need to seamlessly scale in terms of resources, such as a web server. Although several commercial serverless services are available (e.g., Amazon Lambda), their use cases are mostly limited to the execution of functions or scripts that can be adapted to predefined templates or specifications. However, current research efforts point out that it is interesting for the serverless paradigm to evolve from single functions and support more flexible infrastructure units such as operating-system-level virtualization in the form of containers. In this paper we present a novel platform to automatically scale container resources in real time, while they are running, and without any need for reboots. This platform is evaluated using Big Data workloads, both batch and streaming, as representative examples of applications that could be initially regarded as unsuitable for the serverless paradigm considering the currently available services. The results show how our serverless platform can improve the CPU utilization by up to 77% with an execution time overhead of only 6%, while remaining scalable when using a 32-container cluster. | es_ES |
dc.description.sponsorship | This work was supported by the Ministry of Economy, Industry and Competitiveness of Spain and FEDER funds of the European Union (project TIN2016-75845-P, AEI/FEDER/EU), the FPU Program of the Ministry of Education, Spain (grant FPU15/03381) and by Xunta de Galicia, Spain (Centro Singular de Investigación de Galicia accreditation 2016–2019, ref. ED431G/01). We also gratefully acknowledge CESGA for providing access to the Big Data infrastructure, and also sincerely thank Dr. Javier López Cacheiro for his technical support to perform some of the experiments. Other experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several universities as well as other organizations. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2016-75845-P/ES/NUEVOS DESAFIOS EN COMPUTACION DE ALTAS PRESTACIONES: DESDE ARQUITECTURAS HASTA APLICACIONES (II)/ | es_ES |
dc.relation | info:eu-repo/grantAgreement/MECD/Programa Estatal de Promoción del Talento y su Empleabilidad/FPU15/03381/ES/ | es_ES |
dc.relation.isversionof | https://doi.org/10.1016/j.future.2019.11.037 | |
dc.relation.uri | https://doi.org/10.1016/j.future.2019.11.037 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC-BY-NC-ND 4.0) | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Serverless computing | es_ES |
dc.subject | Big Data | es_ES |
dc.subject | Resource scaling | es_ES |
dc.subject | Operating-system-level virtualization | es_ES |
dc.subject | Container cluster | es_ES |
dc.title | Real-time resource scaling platform for Big Data workloads on serverless environments | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
dc.identifier.doi | 10.1016/j.future.2019.11.037 |
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