Executive Summary

Modern cloud-native stacks increasingly run stateful services (search, analytics, feature stores) that use mmap and lazy loading, and when many containers fault pages simultaneously it creates coordinated page-fault storms that effectively DDoS the cluster coordinator—a pain platform engineers and SREs face but struggle to diagnose and prevent. This manifests as hard-to-predict outages and noisy telemetry that existing OOM/observability tools don’t reliably prevent. You could build a kube-scheduler plugin plus lightweight node agent that implements mmap-aware memory accounting, admission control and paced mmap scheduling, with observability integrations that surface actionable policies and automated throttling to prevent coordinated faults. The product would be opinionated, policy-driven, and designed to plug into existing orchestration and monitoring stacks for quick piloting. The market looks attractive now: roughly 12,000 relevant enterprises paying ~$500K ACV gives a $6.0B TAM, and trends—rising stateful workloads, shift-left SRE buying opinionated reliability tools, and observability vendors moving into orchestration—make customers receptive today (Market Score 90/100, Revenue Potential 86/100). You’d differentiate by offering kernel-aware mmap accounting and scheduler-level enforcement tailored to mmap patterns—capabilities general observability or CNI vendors typically don’t provide—while shipping integrations that lower adoption friction. The main challenges are cross-kernel portability and proving safety in production, but given the clear ROI for platform reliability and medium competition, this is worth piloting with a few large customers.

Market Opportunity

Avoid coordinator DDOS by mmap-aware scheduling and memory accounting targets a $6.0B = 12,000 relevant enterprises × $500K ACV (platform reliability + tooling across data platforms and SRE teams) total addressable market with medium saturation and a year-over-year growth rate of 12% YoY (IDC/Forrester forecasts for APM, observability, and platform engineering tooling combined, 2024-2026).

Key trends driving demand: Cloud-native stateful workloads are increasing — more companies run search, analytics, and feature-store services that rely on mmap and lazy loading, creating demand for specialized reliability tooling.; Shift-left SRE and platform engineering teams are buying opinionated tools that proactively prevent outages rather than only surfacing telemetry.; Observability vendors are integrating with orchestration layers, which opens an opportunity for focused scheduler-aware products to plug into that stack.; Rising cloud costs and outage SLAs are pushing engineering orgs to invest in tools that reduce incident frequency and mean time to repair, creating willingness to pay for high-impact reliability solutions..

Key competitors include Datadog, Honeycomb, Kubernetes custom scheduler ecosystem (open-source + vendors).

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Avoid coordinator DDOS by mmap-aware scheduling and memory accounting

Executive Summary

Market Validation

Market Opportunity

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