Green Distributed AI Training: Orchestrating Compute Across Renewable-Powered Micro Datacenters
Giuseppe Tomei, Andrea Mayer, Giuseppe Alcini, Stefano Salsano

TL;DR
This paper proposes a formal model and orchestration framework for migrating AI workloads across renewable-powered micro datacenters, aiming to enhance energy efficiency and performance stability by aligning computation with green energy availability.
Contribution
It introduces a feasibility-domain model linking migration factors and develops a control mechanism for renewable-aware AI workload orchestration.
Findings
Migration is energetically justified in most cases.
Feasibility is primarily constrained by time, not energy.
Orchestration reduces non-renewable energy use and improves stability.
Abstract
The accelerating expansion of AI workloads is colliding with an energy landscape increasingly dominated by intermittent renewable generation. While vast quantities of zero-carbon energy are routinely curtailed, today's centralized datacenter architectures remain poorly matched to this reality in both energy proportionality and geographic flexibility. This work envisions a shift toward a distributed fabric of renewable-powered micro-datacenters that dynamically follow the availability of surplus green energy through live workload migration. At the core of this vision lies a formal feasibility-domain model that delineates when migratory AI computation is practically achievable. By explicitly linking checkpoint size, wide-area bandwidth, and renewable-window duration, the model reveals that migration is almost always energetically justified, and that time-not energy-is the dominant…
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Taxonomy
TopicsCloud Computing and Resource Management · Parallel Computing and Optimization Techniques · Software-Defined Networks and 5G
