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Event-Driven Double Dueling Deep Q-Learning for Efficient Serverless Edge Function Offloading and Scheduling
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DOI：10.1109/tcc.2026.3703387.png)
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Abstract
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Serverless edge computing combines the scalability and cost-efficiency of serverless platforms with the low-latency benefits of edge systems. However, the limited computational capacity of individual edge nodes and frequent cold starts often lead to long waiting times and missed deadlines. Although horizontal offloading can alleviate local overload, conventional heuristics rely on static metrics and neglect critical conditions such as queue buildup, warm containers, and memory allocation, which frequently shift bottlenecks to neighboring nodes. This paper proposes a cooperative serverless edge architecture that integrates intelligent horizontal offloading with an adaptive in-node scheduling layer. A Double Dueling Deep Q-Learning (D3QL) agent guides task placement based on real-time feedback, while the scheduler at the chosen node prioritizes requests to increase warm-start reuse and protect deadline-sensitive workloads. The scheduler’s decisions flow back into the agent, forming a continuous improvement loop between global and local control. Evaluations using production Azure and Alibaba traces show that our method consistently outperforms baseline approaches, achieving higher task completion and guarantee rates, reduced memory consumption, and improved warm-start ratios. Using a one-hour Azure invocation trace on OpenWhisk, our method achieves 67.46% higher task completion than local execution.
Keywords:
Serverless computing
edge computing
reinforcement learning
horizontal offloading
task scheduling
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