RIA: Return on Investment Auto-scaler for Serverless Edge Functions
Huadong Li, Hui Liu, Aoqi Chen, Xirui Ma, Qiaoqiao Liu, Junzhao Du · 2024
Serverless introduces a lightweight, function-based execution model that is significant in addressing challenges such as heterogeneity in Internet of Things (IoT) edge applications, high dynamics in user requests, and unpredictability in workloads. Firstly, in this paper, we propose a novel scaling algorithm evaluation metric, the economic model Return on Investment (ROI). This metric encompasses elements such as response latency, latency stability, function invocation, and resource usage to assess the Quality of Service (QoS) per unit of monetary cost for application providers. Secondly, we introduce RIA, a serverless edge framework that serves as a high ROI auto-scaler for edge functions. RIA incorporates the SHAP-based DQN (SDQN) algorithm, integrating threshold-based reactive methods with prediction-based proactive approaches to make optimal scaling decisions based on the current state of the edge environment. It includes a SHAP-based space optimization algorithm, effectively addressing the issue of state space explosion caused by high volatility and numerous concurrent functions in traditional reinforcement learning in edge environments. Finally, we conduct extensive experiments based on Azure traces to evaluate the effectiveness and performance of RIA. We compare RIA with five state-of-the-art technologies. The experimental results demonstrate a reduction in QoS violations by 25.66%-83.08%, an increase in ROI by 0.86 to 6.45 times, and the second-lowest monetary cost.