Graph Neural Network-Based SLO-Aware Proactive Resource Autoscaling Framework for Microservices
Jinwoo Park, Byung‐Kwon Choi, Chunghan Lee, Dongsu Han
Abstract
Microservice is an architectural style widely adopted in various latency-sensitive cloud applications. Similar to the monolith, autoscaling has attracted the attention of operators for managing the resource utilization of microservices. However, it is still challenging to optimize resources in terms of latency service-level-objective (SLO) without human intervention. In this paper, we present GRAF, a graph neural network-based SLO-aware proactive resource autoscaling framework for minimizing total CPU resources while satisfying latency SLO. GRAF leverages front-end workload, distributed tracing data, and machine learning approaches to (a) observe/estimate the impact of traffic change (b) find optimal resource combinations (c) make proactive resource allocation. Experiments using various open-source benchmarks demonstrate that GRAF successfully targets latency SLO while saving up to 19% of total CPU resources compared to the fine-tuned autoscaler. GRAF also handles a traffic surge with 36% fewer resources while achieving up to 2.6x faster tail latency convergence compared to the Kubernetes autoscaler. Moreover, we verify the scalability of GRAF on large-scale deployments, where GRAF saves 21.6% and 25.4% for CPU resources and memory resources, respectively.