Abstract
Serverless edge computing provides a lightweight and easily scalable new paradigm for edge computing, which is widespread in many fields. However, its characteristics of fine-grained tasks, short startup times, and fast execution speed bring new challenges in task offloading and latency reduction. In this paper, we consider the task offloading problem of serverless functions in a multi-edge-to-cloud environment. A new hybrid offloading algorithm, Average latency constrained Independent task Hybrid Offloading (AIHO), is proposed aimed at reducing latency so as to enhance serverless computing in edge environment. AIHO integrates a serverless-based three-layer system framework, enabling strategic deployment of serverless functions closer to end devices, and includes four critical components: offloading decision, task sorting, path selection, and function replacement. The proposed algorithm is evaluated by comparing it to other three baselines for similar problems on the same datasets. By focusing more on horizontal collaboration among edge nodes based on the multi-hop communication mechanism, AIHO exhibits higher performance than other baselines. Experimental results demonstrate that it can significantly reduce average latency, optimize resource usage, and enhance overall resilience and efficiency of edge computing systems, marking a substantial advancement in serverless and edge computing integration.
Published Version
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