A GNN-based proactive resource allocation framework for SLO-oriented microservices.

GRAF: Design Overview


Microservice is an architectural style that has been widely adopted in various latency-sensitive applications. Similar to the monolith, autoscaling has attracted the attention of operators for managing 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 proactive resource allocation 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 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. Moreover, GRAF handles traffic surge with 36% fewer resources while achieving up to 2.6x faster tail latency convergence compared to the Kubernetes autoscaler.


  1. CoNEXT
    GRAF: A Graph Neural Network Based Proactive Resource Allocation Framework for SLO-Oriented Microservices
    Jinwoo ParkByungkwon Choi, Chunghan Lee, and Dongsu Han
    In Proceedings of the 17th International Conference on Emerging Networking EXperiments and Technologies 2021
  2. APNet
    PHPA: A Proactive Autoscaling Framework for Microservice Chain
    Byungkwon ChoiJinwoo Park, Chunghan Lee, and Dongsu Han
    In 5th Asia-Pacific Workshop on Networking (APNet 2021) 2021



15 minute talk.