Jul 2026· 2026 8th International Conference on Electronics and Communication, Network and Computer Technology (ECNCT)· pp. 441-446· 2 citations· 52 references
TL;DR
SAGF (SLO-Aware Graph Forecasting), an integrated prediction-and-control framework for proactive autoscaling, encodes six per-service metrics on a spatio-temporal call graph, predicts per-service SLO risk via GCN with temporal gating, and feeds these scores to a constrained RL controller for dependency-aware scaling.
Abstract
Autoscaling microservice backends is challenging due to complex inter-service dependencies, bursty workloads, and cascading latency amplification along call chains. Existing approaches either react to threshold breaches after SLO violations occur, or predict demand per service in isolation. We propose SAGF (SLO-Aware Graph Forecasting), an integrated prediction-and-control framework for proactive autoscaling. SAGF encodes six per-service metrics on a spatio-temporal call graph, predicts per-service SLO risk via GCN with temporal gating, and feeds these scores to a constrained RL controller for dependency-aware scaling. On the Online Boutique benchmark with Kubernetes and Istio, SAGF reduces SLO violations by 41% and excess overprovision by 48% on average compared to the strongest baseline across four workload patterns.
SLO-Scaler is proposed, an uncertainty-aware autoscaling framework that predicts short-horizon request rates, tail latency, and SLO violation probability using a Bayesian LSTM model, and is evaluated on the DeathStarBench Social Network benchmark deployed on Kubernetes under periodic, bursty, and long-tail traffic patt...
Shuo Wang, Xiao-Xuan Sun, Shao-yu Huang et al.· 1 citation
Kubernetes Horizontal Pod Autoscaler(HPA) and other existing auto-scaling solutions that respond reactively to demand experience significant delays in provisioning and inefficiencies when responding to sudden workload spikes. This paper proposes a new Real-Time Workload Monitoring-Based Intelligent Auto-Scaling Framewo...
Nikita Singh, Meenu Gupta, Rakesh Kumar et al.· International Conference on...· 0 citations
CAPA is presented, a confidence-aware predictive autoscaler that couples a lightweight rolling ridge forecaster with an online one-sided residual-quantile bound and an SLO-debt feedback signal and reduces the estimated SLO violation rate relative to a reactive HPA-style baseline.
AgileSphere is presented, a predictive-reactive cluster autoscaling framework that combines ScalePlanner, an iTransformer-based capacity forecaster trained on the Alibaba 2022 cluster trace, and AgileServer, a monotone-max reconciler that arbitrates between the two desired-state sources to identify a stability advantag...
Pooyan Habibi, Sushil Rawat, Alberto Leon-Garcia· IEEE Access· 1 citation
Migrating microservices across virtual machines (VMs) without knowledge of their runtime communication patterns risks creating cross-VM hotspots and latency spikes that are difficult to predict from static analysis alone. We use extended Berkeley Packet Filter (eBPF) kernel-level network tracing to automatically discov...
The proposed PCGAT-Stab, an explainable Temporal Graph Attention Network for PCIe protocol behavior modeling and system stability prediction in cloud-native microservices, confirms the effectiveness of topology-aware temporal graph attention for reliable system stability prediction in cloud-native microservices.
Cheng-De Xu, Ji-Xiang Ding· Fundamental Scientific Repor...· 0 citations
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