A framework for black-box SLO tuning of multi-tenant applications in Kubernetes

Resource management concepts of container orchestration platforms such as Kubernetes can be used to achieve multi-tenancy with quality of service differentiation between tenants. However, to support cost-effective enforcement of Service Level Objectives (SLOs) about response time or throughput, an automated resource optimization approach is needed for mapping custom SLOs of different tenants to cost-efficient resource allocation policies. We propose a versatile tool for cost-effective SLO tuning, named k8-resource-optimizer, that relies on black-box performance tuning algorithms. We illustrate and validate the tool for optimizing different resource configuration properties of a simple job processing application. Our experiments showed that k8-resource-optimizer can find near-optimal configurations for different multi-tenant deployment settings and different types of resource parameters. However an open research challenge is that, when the number of parameters increases, the total tuning cost may also increase beyond what is acceptable for contemporary cloud-native applications. We shortly discuss three possible complementary solutions to tackle this challenge.