Predictive Analytics-Driven Performance Tuning in Large-Scale Computing Infrastructures
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V4I1P102Keywords:
Predictive Analytics, Performance Tuning, Tail Latency, SLO/SLA Compliance, Aiops, Time-Series Forecasting, Bayesian Optimization, Safe Reinforcement Learning, Multi-Objective Optimization, Digital Twin, Canary Rollout, Causal Inference, Autoscaling, Kubernetes, Microservices Observability, Cost and Energy EfficiencyAbstract
Large-scale computing infrastructures spanning cloud, edge, and hybrid deployments face volatile workloads, resource contention, and non-linear performance-cost trade-offs. This paper presents a predictive analytics–driven framework that closes the loop between observability and action for automatic performance tuning under service-level objectives (SLOs). The approach ingests multimodal telemetry (traces, metrics, logs, and events) and builds horizon-aware forecasts of demand and tail latency using a model ensemble (gradient boosting for short-horizon bursts, sequence models for diurnal patterns, and causal attribution for change impact). A policy layer blends constrained Bayesian optimization with safe reinforcement learning to recommend tunables e.g., autoscaling targets, concurrency limits, cache sizes, I/O quotas, and NUMA/affinity hints subject to SLO, budget, and energy constraints. To mitigate risk, the system employs digital twins and canary experiments with counterfactual evaluation before progressive rollout. We formalize tuning as a multi-objective optimization that minimizes p95/p99 latency and error budget burn while bounding cost and energy. In evaluation on Kubernetes-based microservices and data pipelines under realistic workload mixes, the framework consistently improved tail latency and throughput while reducing over-provisioning, ablation studies show the value of causal features and safety filters in preventing regressions. The result is an adaptive, explainable, and portable tuning stack that turns noisy observability data into reliable, cost-aware control actions for heterogeneous compute estates
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