수퍼인텔리전스연구실

Multi-access Edge Computing (MEC) is a paradigm which moves the capabilities of the central cloud closer to users. In this paradigm, miniaturized and distributed clouds are deployed at the network edge to accelerate conventional services such as live video streaming and healthcare, and also to offer innovative services such as online mobile gaming. Challenges regarding MEC management include computation offloading, allocation of computing resource, and mobility management. We employ the reinforcement learning technique for the MEC management since it tolerates noisy inputs and works well under diverse conditions of not only users geographic distribution but also their varying requirements. The general idea of reinforcement learning is that by gradually interaction with the environment, agent will be able to make better decisions using the past experience. At the initial state, the agent rarely has the idea about how to take action or even what the task is. Then according to agent performance of this task, it will obtain a reward as the feedback. Theoretically, agent could make the optimal decisions if it can explore whole states and obtain corresponding values of its action. Network softwarization is an approach in which network control, management, and functions are decoupled from the data plane (i.e., network elements) and softwarized in a central control plane. Software-defined Networking (SDN) and Network Function Virtualization (NFV) technologies together achieves fully softwarized networks. SDN decouples the control plane of underlying network elements and centralized it in a controller. The controller in SDN decides how traffic is controlled in a network, and to do so it requires some method to communicate with the data plane. One such mechanism is OpenFlow, whic