Orchestrating and Scheduling System for Workflows in Heterogeneous and Dynamic Environment

Many Orchestrating and scheduling systems and algorithms have been proposed or deployed in Cloud computing and Edge computing scenarios, where computing resources are heterogeneous and dynamic due to rapid growth of mobile devices and Internet-of-Things scenarios. And intelligent services can be abstracted or deployed as computing workflows. When data privacy and service latency should be considered, how to orchestrate and schedule these intelligent services (i.e. training or inference or compound computing workflows in heterogeneous and dynamic scenarios) is becoming more important. Previous proposals are not taking these features into enough consideration. We propose Network Artificial Intelligence Management and Orchestration architecture (NAMO) and NAMO-RL Reinforcement Learning algorithm for this heterogeneous and dynamic scenario, which involves all cloud-edge-user computing devices, and has high dynamics in wireless channel.