An autonomous energy-aware resource scheduling mechanism in serverless edge computing

In edge computing environments using a serverless approach, properly managing resources is essential to reduce energy usage and maintain continuous service for IoT devices. Because the energy availability of edge nodes fluctuates over time, scheduling tasks in real-time becomes complex, requiring the system to allocate resources dynamically while still responding promptly to incoming requests. In this paper, we propose autonomous energy-aware scheduler mechanism (AEASM), a novel scheduling framework that selects and executes the most suitable scheduler based on the predicted energy levels of active nodes. AEASM leverages a MAPE-K control loop to continuously monitor energy states, analyze workload requirements, plan scheduling decisions, and execute the chosen scheduler. Additionally, AEASM enhances network availability by dynamically adapting to energy drops in active nodes. Our evaluation across four workload distribution models demonstrates that AEASM optimizes energy consumption, ensures sufficient resource allocation for incoming requests, increases network availability for over one hour, and improves overall quality of service through fault tolerance and faster response times.