Real-time resource scaling platform for Big Data workloads on serverless environments
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Real-time resource scaling platform for Big Data workloads on serverless environmentsData
2020Cita bibliográfica
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.
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https://doi.org/10.1016/j.future.2019.11.037
Resumo
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.
Palabras chave
Serverless computing
Big Data
Resource scaling
Operating-system-level virtualization
Container cluster
Big Data
Resource scaling
Operating-system-level virtualization
Container cluster
Descrición
Versión final aceptada de: https://doi.org/10.1016/j.future.2019.11.037 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.
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC-BY-NC-ND 4.0)