Container-Based Microservice Scheduling Using Reinforcement Learning in Distributed Cloud Computing

Autor(es)

Marques Matos, Gabriel Henrique ; Carvalho, Marcos ; Macedo, Daniel F.

• Tipo de documento: Artigo Científico
• Fonte: 2024 IEEE LATIN-AMERICAN CONFERENCE ON COMMUNICATIONS, LATINCOM; v. N/A, p. 6-pg., 2024-01-01.

Resumo

Containers provide significant advantages for application deployment in cloud computing environments. Applications are now divided into multiple small services (microservices) that communicate among themselves to ensure effective application operation. However, this paradigm shift introduces challenges in distributed cloud computing, where microservices are spread across different zones that may be hundreds of kilometers apart, resulting in latency issues in microservice communication. This paper proposes microservice scheduling based on Reinforcement Learning (RL) by considering microservice interdependencies to optimize their placement. Experimental results indicate that our RL-based scheduler reduces average latency by 22.51% compared to the default Kubernetes scheduler. (AU)

• Processo FAPESP: 20/05182-3 - PORVIR-5G: programabilidade, orquestração e virtualização em redes 5G
• Beneficiário: José Marcos Silva Nogueira
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 18/23097-3 - SFI2: fatiamento de infraestruturas de internet do futuro
• Beneficiário: Tereza Cristina Melo de Brito Carvalho
• Modalidade de apoio: Auxílio à Pesquisa - Temático