The paper presents a self-adaptive Kubernetes cloud controller for scheduling time-sensitive applications. The controller allows services to specify timing requirements (response time or throughput) and schedules services on shared cloud resources so as to meet the requirements. The controller builds and continuously updates an internal performance model of each service and uses it to determine the kind of resources needed by a service, as well as predict potential contention on shared resources, and (re-)deploys services accordingly. The controller is integrated with our highly-customizable data processing and visualization platform IVIS, which provides a web-based front-end for service deployment and visualization of results. The controller implementation is open-source and is intended to provide an easy-to-use testbed for experiments focusing on various aspects of adaptive scheduling and deployment in the cloud.