Performance evaluation of data-centric workloads in serverless environments
| Autor: | David Carrera, Claudia Misale, Jordà Polo, Anna Maria Nestorov, Alaa Youssef |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Barcelona Supercomputing Center, Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Information storage and retrieval systems
Informàtica::Arquitectura de computadors::Arquitectures distribuïdes [Àrees temàtiques de la UPC] Computació en núvol Computer science business.industry Distributed computing Serverless Storage Provisioning Cloud computing Workload Database-centric architecture Scheduling (computing) Memory management Resource (project management) Tekton Informació -- Sistemes d'emmagatzematge i recuperació Performance model Scalability Data_FILES Kubernetes business Knative |
| Zdroj: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) CLOUD |
| Popis: | Serverless computing is a cloud-based execution paradigm that allows provisioning resources on-demand, freeing developers from infrastructure management and operational concerns. It typically involves deploying workloads as stateless functions that take no resources when not in use, and is meant to scale transparently. To make serverless effective, providers impose limits on a per-function level, such as maximum duration, fixed amount of memory, and no persistent local storage. These constraints make it challenging for data-intensive workloads to take advantage of serverless because they lead to sharing significant amounts of data through remote storage. In this paper, we build a performance model for serverless workloads that considers how data is shared between functions, including the amount of data and the underlying technology that is being used. The model's accuracy is assessed by running a real workload in a cluster using Knative, a state-of-the-art serverless environment, showing a relative error of 5.52%. With the proposed model, we evaluate the performance of data-intensive workloads in serverless, analyzing parallelism, scalability, resource requirements, and scheduling policies. We also explore possible solutions for the data-sharing problem, like using local memory and storage. Our results show that the performance of data-intensive workloads in serverless can be up to 4.32= faster depending on how these are deployed. This work was partially supported by the Ministry of Economy of Spain under contract TIN2015-65316-P, the Ministry of Science under contract PID2019-107255GB-C21/AEI/10.13039/501100011033, and the Generalitat de Catalunya under contract 2014SGR1051. |
| Databáze: | OpenAIRE |
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