DICOMPUTE: An Economic and Privacy-Aware Framework for Distributed Artificial Intelligence Inference
- Published
- Source
- arXiv
- Paper number
- 1436
- Field
- Distributed Systems
- arXiv ID
- 2611.10804
Key points
- The authors present DICOMPUTE, a research framework for distributed inference that allocates requests across privately controlled, regional, and independently operated computing resources under policy-constrained scheduling.
- The full-cost model accounts for capital amortization, energy, network transfer, orchestration, utilization, failures, and trust-related expenses.
- An illustrative scenario with hypothetical parameters yields a break-even volume of 5.625 million requests per month.
- The privacy model separates data residency, transport confidentiality, execution confidentiality, and verifiable accounting.
- The authors propose falsifiable hypotheses and a reproducible experimental design, without presenting simulated examples as measured performance.
- They conclude conditionally that centralized clusters retain structural advantages for tightly coupled, memory-intensive, high-throughput inference.
Paper links
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