SynScale: Spatiotemporal Collaborative Autoscaling for Microservices in Edge-Clouds

Edge-cloud environments constitute a heterogeneous computing paradigm that integrates resource-constrained edge servers with high-performance cloud servers. While microservices have revolutionized large-scale applications development by enhancing scalability and flexibility, achieving efficient microservice autoscaling—the dynamic adjustment of instances to maintain quality of service (QoS) and meet service-level agreements (SLA) targets—remains challenging in such environments. Most existing autoscaling techniques rely on metric forecasting and centralized or per-node control, causing them to overlook temporal evolution and server relationships and resulting in unstable and weakly coordinated scaling. To address these challenges, we present SynScale, a distributed collaborative autoscaling framework that strengthens both temporal sensitivity and structural coordination. SynScale introduces a spatiotemporal representation module that couples a temporal attention network with a multi graph convolutional model. The temporal component distills behavioral trends from recent observations to ensure coherent responses to time-varying dynamics, while the spatial component performs relational reasoning over explicitly modeled inter server correlations to yield structure-aware representations that support effective cross-node coordination. These spatiotemporal embeddings are then used within a multi-agent reinforcement learning paradigm, enabling distributed agents to generate context-aware scaling decisions that align local adaptability with system-wide efficiency. Experimental evaluations against state of-the-art autoscaling techniques show that SynScale reduces average response time by 59.32%, SLA violations by 85.71%, and P95 latency by 73.2%, while also lowering resource cost. It further improves scaling stability—reflected by lower instance time and longer instance lifetimes—and maintains lightweight, scale-stable runtime overhead, ensuring practical deployability in heterogeneous edge-cloud environments.