SimuScale: Optimizing Parameters for Autoscaling of Serverless Edge Functions Through Co-Simulation

Abstract

Serverless Edge Computing is growing in popularity, and while commercial providers are starting to offer edge-oriented products, much research is still being done on orches-trating functions (e.g., autoscaling). These approaches range from threshold- to AI-based strategies and support various Service Level Objectives (SLOs), such as Round-Trip-Time (RTT) and re-source usage. However, the Quality of Service (QoS) continuously deteriorates due to the dynamic edge-cloud continuum and static parameterization of orchestration strategy parameters. Platforms must adapt the orchestration parameters during runtime to counteract this drift that causes SLO violations. To this end, we introduce the Orchestration Parameter Optimization Prob-lem (OPOP), which aims to find parameters for orchestration strategies to minimize SLO violations. We propose a novel self-adaptive Simulation-based Scaling (SimuScale) approach that uses co-simulation to solve OPOP for autoscalers during runtime. SimuScale uses live monitoring data to feed the simulation and perform parameter optimization. Our Proof of Concept is inte-grated with Kubernetes and evaluated on a real-world edge-cloud testbed. While this work focuses on a threshold-based autoscaler, it can be extended to optimize other orchestration components (e.g., schedulers). Our experimental results show that SimuScale finds parameters that decrease RTT SLO violations between 15% and 40%. SimuScale also can reduce resource usage by 29.87% while maintaining the target 95th RTT percentile. Moreover, it can reduce variance caused by different request patterns, making orchestration strategies more resilient in realistic scenarios.