QoS-Aware and Resource Efficient Microservice Deployment in Cloud-Edge Continuum

User-facing services are now evolving towards the microservice architecture where a service is built by connecting multiple microservice stages. While an entire service is heavy, the microservice architecture shows the opportunity to only offload some microservice stages to the edge devices that are close to the end users. However, emerging techniques often result in the violation of Quality-of-Service (QoS) of microservice-based services in cloud-edge continuum, as they do not consider the communication overhead or the resource contention between microservices. We propose Nautilus, a runtime system that effectively deploys microservice-based user-facing services in cloud-edge continuum. It ensures the QoS of microservice-based user-facing services while minimizing the required computational resources. Nautilus is comprised of a communication-aware microservice mapper, a contention-aware resource manager and a load-aware microservice scheduler. The mapper divides the microservice graph into multiple partitions based on the communication overhead and maps the partitions to the nodes. On each node, the resource manager determines the optimal resource allocation for its microservices based on reinforcement learning that may capture the complex contention behaviors. The microservice scheduler monitors the QoS of the entire service, and migrates microservices from busy nodes to idle ones at runtime. Our experimental results show that Nautilus reduces the computational resource usage by 23.9% and the network bandwidth usage by 53.4%, while achieving the required 99%-ile latency.