"Kubernetes Horizontal Pod Autoscaler (HPA)'s delayed reactions might impact edge performance, while creating a custom autoscaler could achieve more stable scale-up and scale-down behavior based on domain-specific metrics and multiple signal evaluations. Startup time of pods should be included in the autoscaling logic because reacting only when CPU spiking occurs delays the increase in scale and reduces performance. Safe scale-down policies and a cooldown window are necessary to prevent replica oscillations, especially when high-frequency metric signals are being used."
"Engineers should maintain CPU headroom when autoscaling edge workloads to absorb unpredictable bursts without latency impact. Latency SLOs (p95 or p99) are powerful early indicators of overload and should be incorporated into autoscaling decisions alongside CPU. Edge computing involves running applications on devices or servers located close to where data is generated, rather than in a centralized cloud. Applications running at the edge must meet extremely low-latency requirements, be highly elastic, and perform predictably when subjected to large and unpredictable spikes in workload volume."
Kubernetes HPA can react slowly, which negatively impacts performance for latency-sensitive edge workloads. Custom autoscalers that evaluate multiple signals and domain-specific metrics provide more stable scale-up and scale-down behavior. Autoscaling must account for pod startup time because responding only to CPU spikes delays scaling and reduces performance. Safe scale-down policies and a cooldown window prevent replica oscillations when using high-frequency metrics. Engineers should maintain CPU headroom to absorb unpredictable bursts without latency impact. Incorporating latency SLOs such as p95 or p99 alongside CPU metrics gives early overload indicators for better autoscaling decisions.
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