Despite placing services and computing resources at the edge of the network for ultra-low latency, we still face the challenge of centralized scheduling costs, including delays from additional request forwarding and resource selection. To address this challenge, we propose SmartBuoy, a new computing paradigm. Our approach starts with a service coverage concept that assumes users within the coverage have high access availability. To enable users to perceive service status, we design a distributed metric table that synchronizes service status periodically and distributively. We propose coverage indicator updating principles to make the updating process more effective. We then implement two distributed methods, SmartBuoy-Time and SmartBuoy-Reliability, that enable users to perceive service capability directly and immediately. To determine the metric table update window size, we provide an analysis method based on user access patterns and offer a theoretical upper bound in a dynamic environment, making SmartBuoy easy to use. Finally, we implement the proposed methods distributively on an open-source edge computing simulator. Experiments on a real-world network topology dataset demonstrate the efficiency of SmartBuoy in reducing delays and improving the success rate.