Details

Autor(en) / Beteiligte

• Choochotkaew, Sunyanan
• Chiba, Tatsuhiro
• Trent, Scott
• Amaral, Marcelo

Titel

Run Wild: Resource Management System with Generalized Modeling for Microservices on Cloud

Ist Teil von

• 2021 IEEE 14th International Conference on Cloud Computing (CLOUD), 2021, p.609-618

Ort / Verlag

IEEE

Erscheinungsjahr

2021

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• Microservice architecture competes with the traditional monolithic design by offering benefits of agility, flexibility, reusability resilience, and ease of use. Nevertheless, due to the increase in internal communication complexity, care must be taken for resource-usage scaling in harmony with placement scheduling, and request balancing to prevent cascading performance degradation across microservices. We prototype Run Wild, a resource management system that controls all mechanisms in the microservice-deployment process covering scaling, scheduling, and balancing to optimize for desirable performance on the dynamic cloud driven by an automatic, united, and consistent deployment plan. In this paper, we also highlight the significance of co-location aware metrics on predicting the resource usage and computing the deployment plan. We conducted experiments with an actual cluster on the IBM Cloud platform. RunWild reduced the 90th percentile response time by 11% and increased average throughput by 10% with more than 30% lower resource usage for widely used autoscaling benchmarks on Kubernetes clusters.