About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
CLOUD 2021
Conference paper
RunWild: Resource Management System with Generalized Modeling for Microservices on Cloud
Abstract
Microservice architecture competes a traditional monolithic design with benefits of agility, flexibility, reusability resilience, and ease of use. Nevertheless, due to the raise of internal communication complexity, care must be taken consistently with resource-usage scaling, placement scheduling, and load balancing to prevent cascading performance degradation across microservices. In this paper, we highlight findings on co-location aware metrics to predict and minimize resource usage for automatically scaling, scheduling, and balancing arbitrary microservices on a dynamic cloud environment with a united and consistent plan. We prototype RunWild, a resource management system that controls all mechanisms in the microservice-deployment process to optimize for desirable performance. We conducted experiments with an actual cluster on the IBM Cloud platform for evaluation. 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.