Celluster™ Scheduling & Scaling

Reflex-native runtime vs. controller-era orchestration
Scheduling • Scaling • Runtime Behavior
From “schedule once” to always-on reflexes.

Most stacks treat scheduling as a one-time event: pick a node, launch a pod, hope the heuristics age well. Celluster Reflex keeps intent alive at runtime, so scheduling and scaling become continuous reflexes, not controller rituals.

Pods stand in line. Cells adapt in the moment.

See Runtime Reflex Model Skip to Scaling & Decay
Cartoon of a stressed Drivers sweating, yelling, confused as traffic light cpntolelr/scheduler , next to a relaxed Cell smiling with simple intent-based fields.
Controller-era scheduling: fair share, preemption, pause/resume… and a very tired driver.
Illustration of a neural-like network of Cells scaling through long, branching nerves across an endless data center.
Reflex-native scaling: Cells act like nerves — local reflexes, endless reach, no central choke point.

Scheduling at Runtime, Not Just at Launch

Celluster’s reflex-native approach is one step ahead.

Where others stop

Traditional GPU scheduling frameworks typically:

  • Scrape GPU telemetry (load, temp, power) from exporters.
  • Feed it into controller logic inside Kubernetes.
  • Use it for initial placement and pricing / quota decisions.
  • After launch, rely on preemption, fair-share, pause/resume on pods fighting over GPU slices.
In that model, telemetry mostly informs “where to place the next pod”, not “how the live workload should evolve.”
Where Celluster goes further

In Celluster, intent stays alive at runtime:

  • Thresholds and reflex policies are declared in the manifest, not buried in controllers.
  • The Reflex engine continuously evaluates telemetry against those thresholds.
  • Reflex actions : clone, reroute, decay, migrate — apply to running Cells, not only new ones.

Coordination is mesh-native:

  • Cells with the same intent share policies and can rebalance GPU/NIC/RoCE load across the fabric.
  • No central controller, decisions are distributed and intent-scoped.

And control is user-driven, not vendor-locked:

  • The manifest encodes what to do when thresholds are crossed (clone, reroute, decay...).
  • Reflex honors those instructions live runtime choreography, not opaque heuristics.

Example — Same Signal, Different World

What happens when GPUs get hot?

Imagine a model serving Cell under pressure:

Run.ai-style controller logic:
if GPU_load > 80% then schedule_next_pod_on_other_node()
→ Good for queueing the next job, less expressive for the one already running.

Celluster Reflex-style manifest logic:
if GPU_load > 80% for 5s in zone "prod" then
  clone 1 more Cell with same intent;
  reroute new requests to the clone;
  decay original Cell capacity by 50%;
end
→ The workload itself evolves: it grows, shifts traffic, and cools down gracefully.

That’s the difference between “schedule next pod” and “reflexively reshape the running system.”

Scheduling & Scaling — Controllers vs Reflex Cells

Aspect GPU Schedulers (Run.ai / NVIDIA / Lambda) Celluster Reflex™ Net Winner
Use of Telemetry Scraped metrics drive controller loops and autoscaling decisions, mostly at launch time and for pricing/quotas. Telemetry is live input to reflexes — directly bound to manifest thresholds and runtime actions on Cells. Winner: Cells
More expressive, closer to intent.
Scheduling Moment Focus on initial placement, with runtime tweaks via preemption and fair-share. Continuous scheduling: Cells can clone, move, and decay while serving traffic. Winner: Cells
True runtime behavior, not just a queue decision.
Coordination Model Central controllers reconcile cluster state, pods stay passive; scaling is gated by control-plane capacity. Mesh-native, local reflexes; Cells coordinate without a global brain. Winner: Cells
Fewer global bottlenecks, more local intelligence.
Scaling Ceiling Practical limits from controller bottlenecks, global maps, and reconciliation lag. No shared global maps; designed for 10M+ Cells across fabrics as data centers expand. Winner: Cells
Infinite in practice, if hardware is.
User Control Policies expressed as YAML hints; actual logic lives in vendor-specific controller code. Manifest declares exact reflex behavior when thresholds trip (“clone, reroute, decay”). Winner: Cells
User-defined choreography instead of black-box heuristics.
Operational Feel Tuning controllers, chasing over/under-provisioning, debugging queues and priorities. Tuning intent and thresholds; the fabric reacts like a nervous system. Winner: Cells
Operate semantics, not machinery.

Numbers and behavior derived from public descriptions of GPU schedulers + Celluster’s own claims. Controllers work — they just carry more baggage. Cells travel light. Choose your journey.

Scaling — Local Scope, No Contention, Natural Decay

Infinite, as far as your data center goes.

Traditional systems hit scaling walls because too much lives in one place: global controllers, shared maps, central routing decisions.

Celluster is designed around three simple principles:

  • Local scoping: reflex decisions stay local to the intent and zone, not the entire cluster.
  • No shared contention point: no single control loop has to “bless” every change.
  • Reflex decay: excess capacity winds down automatically when pressure drops.

Think of a nervous system:

  • Reflexes happen locally (touch a hot surface → hand moves) without consulting the entire brain.
  • Signals still roll up into higher-order behavior, but that doesn’t slow the reflex.

As long as you can keep adding racks and GPUs, Celluster’s reflex graph can keep stretching with them, without discovering a new global bottleneck.

This page describes the behavioral model, not Celluster’s internal wiring.