Online Deployment of Dynamic Edge DAG Serverless Functions Toward Fast Startup

Serverless computing converts services into multiple containerized functions, greatly enhancing the flexibility of edge applications. To guarantee Service Level Objectives (SLOs), it is common to pre-warm containers for all functions. However, we have observed that requests with a Directed Acyclic Graph (DAG) structure commonly involve only a subset of functions. Pre-warming all containers may lead to over-provisioning, and the fixed optimization strategy for pre-warming becomes ineffective in scenarios where the calling probabilities of functions dynamically change. To overcome the above challenges, we constructed a dynamic DAG model with the consideration of function calling probability, aiming to minimize the request execution time. Based on the model, we proposed an Expectation-based Rounding Optimization (EBRO) algorithm to progressively find the offline optimal container perwarm and deployment strategy with theoretical performance guarantee. Then, we implemented an Online Pre-warming and Tasks Scheduling (OPTS) algorithm to adjust pre-warming and deployment locations based on real-time edge resources and container states. Finally, extensive experiments on a edge cluster show that, compared with baselines, EBRO has the lowest average request execution time and cold start time, with reductions reaching 78.4% and 88.5% respectively. OPTS can potentially reduce the request execution time by up to 25.2%, and reduce the cold start time by a maximum of 80.1 %.