Optimizing Cloud Resource Allocation with Predictive Big Data Models

Cloud platforms increasingly host mixtures of latency-sensitive microservices and data-intensive analytics whose workloads vary at time scales comparable to autoscaler actuation delays. Reactive scaling policies therefore operate with inherent lag, leading to transient service-level objective (SLO) violations during demand surges and unnecessary overprovisioning when demand drops. Predictive autoscaling can reduce this lag, but many deployed and published approaches either (i) rely on point forecasts integrated through heuristic rules, or (ii) solve cost minimization without explicit treatment of forecast uncertainty and SLO risk. This paper presents Predictive Big-Data Model Resource Allocation (PBDMRA), a cloud-native control stack that couples large-scale telemetry processing with probabilistic forecasting, lightweight uncertainty calibration, and risk-bounded allocation. PBDMRA ingests metrics and traces (and can incorporate structured logs when available), constructs features using stream processing, produces multi-horizon predictive quantiles, calibrates empirical coverage via rolling residual adjustments, and computes scaling actions by solving a constrained optimization problem that trades cost against SLO risk and scaling churn. Trace-driven replay across microservice, streaming, and batch workloads indicates that PBDMRA reduces SLO violation minutes by 33-55relative to Kubernetes HPA and a point-forecast baseline, while also reducing scaling actions.