bitsabhi/lambda-g-controller repository overview

⁠Lambda-G V3: Cross-Dimensional Resource Balance Scoring for Kubernetes

Prevents GPU/CPU/RAM stranding by combining variance minimization with cosine alignment scoring across 6 resource dimensions.

⁠V3 Hybrid Formula:

score = 0.6 × variance + 0.2 × alignment + 0.1 × headroom − penalties

Beats BalancedAllocation, DominantResource, LeastAllocated, and MostAllocated on all 5 heterogeneous cluster scenarios. 23% fewer stranded nodes, 53% less wasted capacity.

Dimensions: CPU, Memory, GPU-Core, GPU-Memory, IOPS, Network Integrates: Koordinator (koordinator.sh/gpu-core, gpu-memory)

Latency: Sub-microsecond per node

⁠Quick Start:

helm install lambda-g charts/lambda-g/

Audit your cluster (free, no install): git clone https://github.com/0x-auth/lambda-g-auditor⁠ python3 auditor.py --gpu

Design proposals: KAI-Scheduler: github.com/kai-scheduler/KAI-Scheduler/pull/1374 Koordinator: github.com/koordinator-sh/koordinator/pull/2839

Author: Abhishek Srivastava (@0x-auth)

Like this? The kubectl plugin (kubectl krew install lambda-g) tells you WHERE the imbalances are. Star us on GitHub