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 constraint shaping feasibility. This insight enables the design of a feasibility-aware orchestration framework that transforms migration from a best-effort heuristic into a principled control mechanism. Trace-driven evaluation shows that such orchestration can simultaneously reduce non-renewable energy use and improve performance stability, overcoming the tradeoffs of purely energy-driven strategies. Beyond the immediate feasibility analysis, the extended version explores the architectural horizon of renewable-aware AI infrastructures. It examines the role of emerging ultra-efficient GPU-enabled edge platforms, anticipates integration with grid-level control and demand-response ecosystems, and outlines paths toward supporting partially migratable and distributed workloads. The work positions feasibility-aware migration as a foundational building block for a future computing paradigm in which AI execution becomes fluid, geographically adaptive, and aligned with renewable energy availability.

翻译：AI工作负载的加速扩张正与日益以间歇性可再生能源为主导的能源格局产生碰撞。尽管大量零碳能源常规化地被弃用，当今集中式数据中心架构在能源比例性与地理灵活性方面仍与这一现实严重脱节。本研究构想向由可再生能源供电的微型数据中心分布式网络转型——通过实时工作负载迁移动态追踪富余绿色能源的可获取性。该愿景的核心是一个形式化可行性域模型，用于界定可迁移AI计算实际可行性的边界条件。通过明确关联检查点规模、广域网带宽与可再生能源窗口时长，该模型揭示：迁移几乎总能实现能源收益，而时间——而非能源——才是主导可行性的关键约束。这一洞见催生了可行性感知编排框架的设计，它将迁移从尽力而为的启发式方法转变为原理性控制机制。基于轨迹驱动的评估表明，此类编排可同步减少非可再生能源消耗并提升性能稳定性，从而突破纯能源驱动策略的权衡局限。在即时可行性分析之外，扩展版本深入探讨了可再生能源感知型AI基础设施的架构前景：审视新兴超高效GPU边缘平台的作用，预判与电网级控制及需求响应生态系统的集成，并勾勒支持部分可迁移与分布式工作负载的演进路径。本研究将可行性感知迁移定位为未来计算范式的基石——在此范式中，AI执行将具备流动性、地理适应性，并与可再生能源可用性保持协同。