# Cloud abstractions for AI workloads

arXiv: https://arxiv.org/abs/2501.09562 · HTML: https://mlsanity.com/paper/2501.09562/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2025.

## Built on by (most cited first, 1 of 1)

- [NetKV: Network-Aware Decode Instance Selection for Disaggregated LLM Inference](https://mlsanity.com/paper/2606.03910/) (2026)

## Abstract (arXiv)

AI workloads, often hosted in multi-tenant cloud environments, require vast computational resources but suffer inefficiencies due to limited tenant-provider coordination. Tenants lack infrastructure insights, while providers lack workload details to optimize tasks like partitioning, scheduling, and fault tolerance. We propose HarmonAIze to redefine cloud abstractions, enabling cooperative optimization for improved performance, efficiency, resiliency, and sustainability. We outline key opportunities and challenges this vision faces.