Executive Summary

Many stateful, mmap-heavy services—vector search, columnar OLAP, and media indexing—suffer performance variability and OOMs because schedulers lack accurate visibility into real memory (RSS/working set). Platform engineers and SREs at enterprise customers end up over-provisioning or doing manual placement to avoid production incidents, wasting capacity and ops time. You could build a memory-aware scheduling layer that uses in-kernel eBPF and optional sidecar telemetry to measure resident sets and working sets with low overhead, then surface those signals via a scheduler plugin, CSI/node driver, or daemonset API to influence placement and autoscaling. Keep the surface intentionally thin: non-invasive probes, a scoring API for control planes, and operator-friendly install paths for rapid adoption. This is a timely market: kernel-level observability is maturing and mmap-heavy workloads are growing, giving a $3.6B TAM (36,000 enterprises × $100K ACV) and high market/revenue scores (88/100). Cloud and platform teams favor operator-friendly integrations, so a focused product can win despite a medium competition landscape. You can differentiate by delivering accurate, low-overhead memory signals and tight scheduler integration that demonstrably reduces memory waste and incidents, while offering minimal-privilege deployment modes and clear ROI metrics. The main challenges are cross-kernel portability, security/privilege concerns, and proving value in pilot deployments, but the technical and commercial signals suggest this is worth exploring.

Market Opportunity

Memory-aware scheduling for mmap-backed stateful nodes targets a $3.6B = 36,000 enterprises × $100K ACV total addressable market with medium saturation and a year-over-year growth rate of 12% YoY — based on Gartner/IDC reporting on observability and cloud infrastructure software growth 2023-2025.

Key trends driving demand: Kernel-level observability is maturing — eBPF and sidecar telemetry make resident-set measurement feasible and low-overhead, creating product opportunities for memory-aware scheduling.; Stateful, mmap-heavy workloads (vector search, columnar OLAP, media indexing) are growing, increasing demand for specialized operational tooling.; Cloud providers and platform teams prefer operator-friendly integrations (daemonsets, CSI drivers, scheduler plugins), enabling a thin-surface product to attach to existing control planes.; SRE and platform engineering headcount is constrained, so teams favor automated mitigations and prescriptive policy over raw dashboards..

Key competitors include Datadog, Grafana / Prometheus ecosystem, Kubernetes scheduler ecosystem (default scheduler, Volcano, Karpenter integrations).

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Memory-aware scheduling for mmap-backed stateful nodes

Executive Summary

Market Validation

Market Opportunity

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