Refining Micro Services Placement over Multiple Kubernetes-orchestrated Clusters employing Resource Monitoring

In the cloud field, there is an increasing demand for globalized services and corresponding execution environments that overcome local limitations and selectively utilize optimal resources. Utilizing multi-cloud deployments and operations rather than using a single cloud is an effective way to satisfy the increasing demand. In particular, we need to provide cloud-native environment to organically support services based on a microservices architecture. In this paper, we propose a cloud-native workload profiling system with Kubernetes-orchestrated multi-cluster configuration. The contributions of this paper are as follows. (i) We design the operating software over multiple cloud-native cluster to select optimal resources by monitoring. (ii) For operating the multiple clusters through the design, we define and design specific general service workloads. Also, we implement the workloads in application software (iii) To seek optimal resources, we deployed the general workloads and monitored resource usage repeatedly in detail. We calculate resource variation in comparison with initial resource usage and average resource usage after deploying the service workloads. Also, we analyze the resource monitoring result. We expect this methodology can find proper resources for service workload types.