🤖 AI Summary
Existing auto-scaling frameworks suffer from prediction inaccuracy, response latency, and structural fragmentation between proactive and reactive components under highly volatile cloud workloads. To address these issues, this paper proposes OptScaler—the first unified optimization framework that synergistically integrates predictive and reactive decision-making. Its core innovation is a centralized optimization orchestrator that jointly embeds time-series forecasting, real-time self-tuning estimators, and model predictive control (MPC) with chance constraints, enabling co-optimization of resource utilization and service-level objective (SLO) compliance. This architecture overcomes the module incompatibility and insufficient robustness inherent in conventional hybrid scaling approaches. Experimental evaluations demonstrate that OptScaler reduces SLO violation rates by over 36%. Deployed at scale in Alipay’s production environment, it effectively supports elastic scaling for long-running, high-concurrency, multi-tenant applications.
📝 Abstract
Autoscaling is a critical mechanism in cloud computing, enabling the autonomous adjustment of computing resources in response to dynamic workloads. This is particularly valuable for co-located, long-running applications with diverse workload patterns. The primary objective of autoscaling is to regulate resource utilization at a desired level, effectively balancing the need for resource optimization with the fulfillment of Service Level Objectives (SLOs). Many existing proactive autoscaling frameworks may encounter prediction deviations arising from the frequent fluctuations of cloud workloads. Reactive frameworks, on the other hand, rely on realtime system feedback, but their hysteretic nature could lead to violations of stringent SLOs. Hybrid frameworks, while prevalent, often feature independently functioning proactive and reactive modules, potentially leading to incompatibility and undermining the overall decision-making efficacy. In addressing these challenges, we propose OptScaler, a collaborative autoscaling framework that integrates proactive and reactive modules through an optimization module. The proactive module delivers reliable future workload predictions to the optimization module, while the reactive module offers a self-tuning estimator for real-time updates. By embedding a Model Predictive Control (MPC) mechanism and chance constraints into the optimization module, we further enhance its robustness. Numerical results have demonstrated the superiority of our workload prediction model and the collaborative framework, leading to over a 36% reduction in SLO violations compared to prevalent reactive, proactive, or hybrid autoscalers. Notably, OptScaler has been successfully deployed at Alipay, providing autoscaling support for the world-leading payment platform.