PROBA: Enhancing Serverless Edge Computing via Adaptive Task Scheduling and Probabilistic Resource Sharing

Serverless edge computing improves performance by processing data closer to its source, reducing operational costs, and increasing server utilization. Despite these benefits, edge servers face scalability challenges and queuing delays due to limited resources. Horizontal offloading can alleviate excessive workloads by efficiently distributing tasks across edge servers. However, it introduces higher waiting time, cold starts latency, and missed task deadlines at the receiving edge servers. To address these challenges, we introduce PROBA, which utilizes a Double Dueling Deep Q-learning algorithm and In-node scheduling that optimize the task offloading between the edge servers and improve task scheduling within the edge server. The approach uses probabilistic resource sharing, where edge servers share their real-time availability to a central cloud system. The cloud analyzes these performance metrics based on user-specified rewards to determine optimal scheduling decisions, which the edge servers execute to maintain balanced and responsive work-load distribution. We evaluated PROBA in a serverless edge computing simulator that focuses on horizontal offloading and in-node scheduling. In the evaluation using real-world trace data from Alibaba, our PROBA technique decreased the average wait time from 3.1 s to below 0.69 $s$. PROBA also gave 1.37 % better task completion time than competitors.