🤖 AI Summary
To address the challenges of SLA violation and delayed elasticity in cloud-based distributed services, this paper proposes FLAS—a lightweight elastic scaling architecture integrating proactive prediction and reactive response. FLAS makes three key contributions: (1) a high-order time-series trend forecasting model enabling accurate long-horizon prediction of workload and performance metrics; (2) a low-overhead, resource-utilization–based reactive mechanism for rapid failover under sudden load spikes; and (3) boundary-value–driven scaling decisions tailored to event-driven scenarios such as content publish-subscribe. Integrated into the E-SilboPS middleware, FLAS enables non-intrusive monitoring and fully automated scaling execution. Experimental evaluation demonstrates that FLAS consistently achieves ≥99% SLA compliance across diverse workloads, significantly improving elasticity responsiveness and resource efficiency under both normal and extreme conditions.
📝 Abstract
Cloud computing has established itself as the support for the vast majority of emerging technologies, mainly due to the characteristic of elasticity it offers. Auto-scalers are the systems that enable this elasticity by acquiring and releasing resources on demand to ensure an agreed service level. In this article we present FLAS (Forecasted Load Auto-Scaling), an auto-scaler for distributed services that combines the advantages of proactive and reactive approaches according to the situation to decide the optimal scaling actions in every moment. The main novelties introduced by FLAS are (i) a predictive model of the high-level metrics trend which allows to anticipate changes in the relevant SLA parameters (e.g. performance metrics such as response time or throughput) and (ii) a reactive contingency system based on the estimation of high-level metrics from resource use metrics, reducing the necessary instrumentation (less invasive) and allowing it to be adapted agnostically to different applications. We provide a FLAS implementation for the use case of a content-based publish-subscribe middleware (E-SilboPS) that is the cornerstone of an event-driven architecture. To the best of our knowledge, this is the first auto-scaling system for content-based publish-subscribe distributed systems (although it is generic enough to fit any distributed service). Through an evaluation based on several test cases recreating not only the expected contexts of use, but also the worst possible scenarios (following the Boundary-Value Analysis or BVA test methodology), we have validated our approach and demonstrated the effectiveness of our solution by ensuring compliance with performance requirements over 99% of the time.