Executive Summary

Many platform and SRE teams running stateful cloud-native workloads (vector DBs, search, analytics) today suffer from unpredictable performance and noisy neighbors because schedulers and accounting systems treat mmap/file-backed memory poorly, leading to hard-to-diagnose OOMs, inefficient packing, and expensive overprovisioning. This pain is acute for teams responsible for SLA-driven services and for cloud operators who face escalations and rising infrastructure costs. You could build a scheduler plugin and control-plane service that uses kernel-level observability (eBPF/low-overhead tracing) to deliver precise mmap-aware memory accounting and then enforce policies via admission control, throttling, eviction, and smarter placement decisions. Delivered as a Kubernetes operator or managed control plane, it would expose actionable metrics, policy APIs, and automated remediation instead of just visibility. The market looks attractive now: an addressable market of roughly $3.6B (12,000 potential customers at ~$300K ACV) with a market score of 88/100 and revenue potential 92/100, driven by the growth of stateful workloads and mature kernel tracing toolchains. Operations teams are also shifting from reactive alerting to automated control-plane actions, increasing willingness to pay for enforcement capabilities that reduce toil and cost. This idea can stand out by combining truly accurate mmap-aware accounting with enforcement primitives (not merely dashboards) and keeping telemetry overhead minimal through eBPF, but it faces nontrivial challenges: kernel compatibility, portability across clouds, and the need to prove clear ROI versus extending existing tools or native cloud features. If you can demonstrate measurable cost savings and reliability improvements for a few early enterprise customers, this is a viable and differentiated product to pursue.

Market Opportunity

Schedule stateful nodes with mmap-aware memory accounting and backpressure targets a $3.6B = 12,000 businesses × $300K ACV total addressable market with medium saturation and a year-over-year growth rate of 12% annual growth — based on cloud-native infrastructure and observability market CAGR estimates (Gartner/IDC blended analysis).

Key trends driving demand: Trend — stateful cloud-native workloads (vector DBs, search, analytics) are increasing, creating demand for scheduling that understands file-backed memory behavior.; Trend — kernel-level observability (eBPF) and low-overhead tracing tools are production-ready, enabling collection of precise memory metrics previously unavailable.; Trend — operations teams are shifting from reactive alerting to automated control-plane actions, creating demand for enforcement (admission control, throttling) rather than just visibility..

Key competitors include Kubernetes default scheduler + kubelet, HashiCorp Nomad, Datadog (observability + APM).

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Schedule stateful nodes with mmap-aware memory accounting and backpressure

Executive Summary

Market Validation

Market Opportunity

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