Evaluating Kubernetes Progressive Delivery in Constrained Environments Flagger vs. Argo Rollouts - Gagah Syuja Saka Abdullah; Rama Aria Megantara (Politeknik Ganesha Medan, 2026)
Cloud‑native progressive delivery orchestrators reduce deployment risk by automating canary deployment rollback procedures, dramatically reducing mean time to recover from deployment failures. However, existing research predominantly evaluates these tools in hyperscale environments, masking the transient computational overhead they introduce in resource‑constrained edge deployments. This study empirically evaluates and compares the automated incident mitigation latency and computational resource volatility of Flagger and Argo Rollouts within a strictly resource‑constrained Kubernetes environment. A low virtual central processing unit Kubernetes testbed was provisioned using Talos Linux with strict hypervisor‑level central processing unit pinning, simulating edge computing conditions. Deterministic fault injection spanning four fault classes, two workload runtimes, and two network topology configurations was executed across thirty trials. A Shapiro-Wilk normality assessment, Welch t-test, Mann-Whitney U test, Cohen's d, and 95% confidence intervals were applied to compare temporal and computational metrics. Memory utilization remained statically bounded, averaging 24.01 megabytes for Flagger and 35.47 megabytes for Argo Rollouts. Under standard fault conditions, neither orchestrator demonstrated a consistent temporal advantage. However, under memory exhaustion progressing to CrashLoopBackOff, Argo Rollouts recovered in a mean of 29.67 seconds against Flagger's 166.79 seconds, a statistically significant 5.6-fold degradation with a large effect size. Argo Rollouts sustained transient central processing unit surges of 159 to 168 millicpu against Flagger's bounded ceiling of 17 to 18 millicpu. Progressive delivery automation introduces non‑negligible and fault-type-dependent computational overhead in resource‑constrained environments. Flagger is recommended for strict resource predictability in threshold-breach environments, while Argo Rollouts is recommended where broader fault-type resilience is operationally critical.