Cloud abstractions for AI workloads
Marco Canini, Theophilus A. Benson, Ricardo Bianchini, \'I\~nigo Goiri, Dejan Kosti\'c, Peter Pietzuch, Simon Peter
TL;DR
This paper proposes HarmonAIze, a new cloud abstraction framework designed to improve AI workload performance, efficiency, and resilience through cooperative optimization between tenants and providers.
Contribution
It introduces HarmonAIze, a novel cloud abstraction approach that facilitates better coordination and optimization for AI workloads in multi-tenant cloud environments.
Findings
Identifies key opportunities for cooperative optimization
Highlights challenges in implementing HarmonAIze
Suggests potential improvements in performance and sustainability
Abstract
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCloud Computing and Resource Management · IoT and Edge/Fog Computing · Data Stream Mining Techniques