Abstract
A system and method for dynamic allocation of computing resources across local and remote environments based on task complexity and resource availability are described. The approach involves evaluating an input using a probe model to determine a difficulty score and complexity tags. Based on this evaluation, the system isolates the input into individual semantic shards for processing across a heterogeneous network of computational targets. A compute bill of materials is generated to optimize cost and quality, matching specific shards with appropriate models, quantization precision levels, energy limits, and hardware targets. The architecture includes a semantic atomizer for parsing inputs, a resource topology monitor for polling hardware states, a generator for creating the execution manifest, and a heterogeneous orchestrator for dispatching and stitching tasks. Keywords: resource allocation, task sharding, hybrid computing, hardware telemetry, compute bill of materials, cost optimization, heterogeneous networks, dynamic quantization, predictive arbitration.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Yavary, Aura, "Predictive Computational Arbitrage and Dynamic Resource Allocation in Heterogeneous Networks", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/9740