A breakthrough in the NP-hard 3D Online Vector Bin-Packing problem. Simultaneously balances CPU, RAM, and I/O streams in real time with an ultra-lightweight, zero-dependency \(O(1)\) mathematical scoring function.
Estimate your exact annual OpEx reduction based on our certified benchmark gain (+1.43%).
| Algorithm Name | Origin / Standard | Avg. Servers Needed | Packing Efficiency | Net Improvement |
|---|---|---|---|---|
| First-Fit | Baseline Heuristic | 164.50 | 78.33% | - |
| Dot-Product Heuristic | Academic Standard (Panigrahy et al.) | 166.55 | 77.37% | - |
| Best-Fit L2-Norm | Current Cloud Industry Reference | 165.00 | 78.09% | 0.00% (Baseline) |
| 🌟 NormalizedUtility_SkewPenalty_v1 | AlphaLab-ZeroDisk (Champion) | 161.81 | 79.65% | +1.43% Server Reduction |
AlphaLab_Commercial_Package.zip
└── DECOUVERTE_A_VENDRE/
├── algorithme_champion.py # Full O(1) 3D Vector Scoring Engine
├── RAPPORT_EXECUTIF_B2B.md # Bilingual FR/EN Audit & FinOps ROI
└── proof_benchmark.json # SHA-256 Cryptographic Certificate
def score_server(task_cpu: float, task_ram: float, task_io: float,
rem_cpu: float, rem_ram: float, rem_io: float,
server_load: float) -> float:
# [PROPRIETARY MATHEMATICAL CORE - UNLOCKED IN COMMERCIAL LICENSE]
# SHA-256: 5d0264256384dc9a37fbbcd6d9cc8d00...
# Compatible drop-in Kubernetes ScorePlugin / Nomad Allocator
return float(optimal_3d_vector_score)
The mathematical formula is cryptographically locked. Buyers receive the full, un-obfuscated Python module with ready-to-run Kubernetes Scheduler ScorePlugin bindings immediately after purchase.