Serverless computing, with its operational simplicity and on-demand scalability, has become a preferred paradigm for deploying workflow applications. However, resource allocation for workflows, particularly those with branching structures, is complicated by cold starts and network delays between dependent functions, significantly degrading execution efficiency and response times. In this paper, we propose the Invocation Concurrency Prediction-Based Scaling (ICPS) algorithm to address these challenges. ICPS employs Long Short-Term Memory (LSTM) networks to predict function concurrency, dynamically pre-warming function instances, and an affinity-based deployment strategy to co-locate dependent functions on the same worker node, minimizing network latency. The experimental results demonstrate that ICPS consistently outperforms existing approaches in diverse scenarios. The results confirm ICPS as a robust and scalable solution for optimizing serverless workflow execution.
@article{arxiv.2504.06512,
title = {ICPS: Real-Time Resource Configuration for Cloud Serverless Functions Considering Affinity},
author = {Long Chen and Xinshuai Hua and Jinquan Zhang and Wenshuai Li and Xiaoping Li and Shijie Guo},
journal= {arXiv preprint arXiv:2504.06512},
year = {2025}
}