🤖 AI Summary
This study addresses how cloud infrastructure failures distort performance metrics, thereby misleading autoscaling systems and leading to resource misallocation, increased costs, or degraded service reliability. Through controlled simulations, the authors systematically evaluate the impact of four common failure types—including storage and network routing issues—on both vertical and horizontal scaling strategies across varying instance configurations and SLO thresholds. The work presents the first quantitative analysis of how such failures bias scaling decisions, revealing that horizontal scaling is particularly sensitive to transient anomalies. It further proposes design principles to distinguish genuine workload changes from failure-induced artifacts. Experimental results demonstrate that storage failures can incur up to $258 in additional monthly costs under horizontal scaling, while routing anomalies consistently cause resource under-provisioning, offering empirical foundations for building fault-tolerant autoscaling mechanisms.
📝 Abstract
Resource autoscaling mechanisms in cloud environments depend on accurate performance metrics to make optimal provisioning decisions. When infrastructure faults including hardware malfunctions, network disruptions, and software anomalies corrupt these metrics, autoscalers may systematically over- or under-provision resources, resulting in elevated operational expenses or degraded service reliability. This paper conducts controlled simulation experiments to measure how four prevalent fault categories affect both vertical and horizontal autoscaling behaviors across multiple instance configurations and service level objective (SLO) thresholds. Experimental findings demonstrate that storage-related faults generate the largest cost overhead, adding up to $258 monthly under horizontal scaling policies, whereas routing anomalies consistently bias autoscalers toward insufficient resource allocation. The sensitivity to fault-induced metric distortions differs markedly between scaling strategies: horizontal autoscaling exhibits greater susceptibility to transient anomalies, particularly near threshold boundaries. These empirically-grounded insights offer actionable recommendations for designing fault-tolerant autoscaling policies that distinguish genuine workload fluctuations from failure artifacts.