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Scheduler Architecture ​

Intelligent Global Work Distribution Across Heterogeneous Compute Nodes ​

The Scheduler is responsible for assigning compute shards to the most suitable Agents in the planetary compute fabric. It optimizes for speed, reliability, fairness, cost, and determinism.

The Scheduler directly influences:

  • job completion latency
  • scalability and throughput
  • fairness across providers
  • reproducibility under varying system load
  • robustness to unreliable or slow Agents

1. Scheduler Responsibilities ​

The Scheduler performs six core tasks:

1. Capability Matching ​

Determines which Agents can execute which workloads (CPU/GPU, memory footprint, codec support, instruction sets).

2. Performance Scoring ​

Evaluates Agent suitability based on:

  • latency
  • bandwidth
  • historical reliability
  • shard completion speed
  • consistency of results

This formula is illustrative; actual scoring is workload-specific and adaptive.

3. Shard Assignment ​

Maps work units (shards) to Agents:

  • Monte Carlo → iteration blocks
  • BLAS → matrix tiles
  • PCA → ensemble members
  • FFmpeg → media segments
  • CAT → perturbation sets

4. Dynamic Rebalancing ​

Reassigns shards in real time when:

  • Agents slow down
  • Agents disconnect
  • verification fails
  • quota changes
  • new Agents join the pool

5. Fairness Enforcement ​

Ensures compute providers earn credits fairly:

  • prevents resource hogging
  • prevents cherry-picking easy shards
  • rewards high-quality Agents

6. Verification Integration ​

Schedules redundant verification shards to detect:

  • incorrect results
  • malicious Agents
  • numerical instability
  • non-deterministic kernels

2. Scheduling Model ​

The Scheduler operates in rolling cycles:


1. Snapshot system state
2. Score Agents
3. Assign shards
4. Monitor progress
5. Rebalance if needed
6. Repeat

Each cycle is short-lived (5–50 ms), allowing continuous adaptation.

3. Agent Scoring Algorithm ​

Each Agent receives a composite performance score, combining:

FactorWeightDescription
LatencyHighLower latency = higher score
ReliabilityHighHistorical success rate
Compute SpeedHighMeasured iterations/sec
BandwidthMediumAffects Blob/VMem streaming
HardwareMediumCPU features, GPU presence
Verification HistoryHighPast correctness
AvailabilityMediumOnline consistency
FairnessMediumEnsures shared opportunities

Scores are normalized and updated continuously.

Example scoring formula (simplified): ​


score =
w1 * latency_score +
w2 * reliability_score +
w3 * throughput_score +
w4 * verification_score +
w5 * fairness_score

The actual formula is adaptive and model-specific.

4. Shard Assignment Strategies ​

4.1 Greedy Fastest-Agent Strategy ​

Used for urgent, latency-sensitive workloads (ETA, real-time dashboards).

4.2 Balanced Throughput Strategy ​

Used for long-running analytical jobs (CAT, climate ensembles, finance risk).

4.3 GPU-Aware Strategy ​

Used for:

  • media transcoding
  • matrix multiplication
  • CUDA-enabled workloads

4.4 Fairness-Aware Strategy ​

Ensures compute providers are compensated proportionally to their contribution.

4.5 Redundant Verification Strategy ​

Certain shards are duplicated and routed to multiple Agents to validate correctness.

5. Fault Handling & Recovery ​

The Scheduler treats Agents as unreliable by default.
This assumption is a core design principle enabling robustness.

Recovery mechanisms: ​

  • Shard timeouts
  • Automatic requeue
  • Reassignment to higher-scoring Agents
  • Quarantine of misbehaving Agents
  • Verification-triggered reschedules

Even when 10–30% of Agents fail mid-job, workloads complete reliably.

6. Adaptive Load Control ​

The Scheduler avoids overloading high-performing Agents by enforcing:

  • credit-based fairness
  • concurrency limits
  • CPU/GPU saturation monitoring
  • bandwidth estimation
  • cooling periods

This keeps the system stable under extreme parallelism.

7. Multi-Modal Scheduling ​

Different adapters require different scheduling behaviors:

• Monte Carlo → embarrassingly parallel ​

➡ massive sharding, linear scaling

• BLAS → tile dependencies ​

➡ maintain block boundaries

• FFmpeg → segment stitching ​

➡ assign segments sequentially

• Climate ensembles → independent member sets ​

➡ no shard interdependency

• CAT modeling → multi-dimensional perturbations ​

➡ dynamic range weighting

Each workload type plugs into a unified scheduling pipeline.

8. Deterministic Reproducibility ​

The Scheduler ensures reproducibility by:

  • deterministic shard partitioning
  • fixed seed offsets for random streams
  • consistent ordering of reduction
  • stable algorithmic behavior across runs

This is essential for:

  • regulated industries
  • insurance compliance
  • financial reporting
  • reproducible science

9. Observability ​

The Scheduler publishes:

  • agent score evolution
  • shard lifecycle events
  • dispatch/compute timing
  • rebalance statistics
  • verification failures
  • job-level performance metrics

Operators can visualize scheduler behavior through Hub dashboards.

Related Documentation ​