Biblio

Computing researchers have long focused on improving energy-efficiency under the implicit assumption that all energy is created equal. Yet, this assumption is actually incorrect: energy's cost and carbon footprint vary substantially over time. As a result, consuming energy inefficiently when it is cheap and clean may sometimes be preferable to consuming it efficiently when it is expensive and dirty. Green datacenters adapt their energy usage to optimize for such variations, as reflected in changing electricity prices or renewable energy output. Thus, we introduce energy-agility as a new metric to evaluate green datacenter applications. To illustrate fundamental tradeoffs in energy-agile design, we develop GreenSort, a distributed sorting system optimized for energy-agility. GreenSort is representative of the long-running, massively-parallel, data-intensive tasks that are common in datacenters and amenable to delays from power variations. Our results demonstrate the importance of energy-agile design when considering the benefits of using variable power. For example, we show that GreenSort requires 31% more time and energy to complete when power varies based on real-time electricity prices versus when it is constant. Thus, in this case, real-time prices should be at least 31% lower than fixed prices to warrant using them.