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Content Delivery Networks (CDNs) are Internet-scale systems that deliver streaming and web content to users from many geographically distributed edge data centers. Since large CDNs can comprise hundreds of thousands of servers deployed in thousands of global data centers, they can consume a large amount of energy for their operations and thus are responsible for large amounts of Green House Gas (GHG) emissions. As these networks scale to cope with increased demand for bandwidth-intensive content, their emissions are expected to rise further, making sustainable design and operation an important goal for the future. Since different geographic regions vary in the carbon intensity and cost of their electricity supply, in this paper, we consider spatial shifting as a key technique to jointly optimize the carbon emissions and energy costs of a CDN. We present two forms of shifting: spatial load shifting, which operates within the time scale of minutes, and VM capacity shifting, which operates at a coarse time scale of days or weeks. The proposed techniques jointly reduce carbon and electricity costs while considering the performance impact of increased request latency from such optimizations. Using real-world traces from a large CDN and carbon intensity and energy prices data from electric grids in different regions, we show that increasing the latency by 60ms can reduce carbon emissions by up to 35.5%, 78.6%, and 61.7% across the US, Europe, and worldwide, respectively. In addition, we show that capacity shifting can increase carbon savings by up to 61.2%. Finally, we analyze the benefits of spatial shifting and show that it increases carbon savings from added solar energy by 68% and 130% in the US and Europe, respectively. 

As solar electricity has become cheaper than the retail electricity price, residential consumers are trying to reduce costs by meeting more demand using solar energy. One way to achieve this is to invest in the solar infrastructure collaboratively. When houses form a coalition, houses with high solar potential or surplus roof capacity can install more panels and share the generated solar energy with others, lowering the total cost. Fair sharing of the resulting cost savings across the houses is crucial to prevent the coalition from breaking. However, estimating the fair share of each house is complex as houses contribute different amounts of generation and demand in the coalition, and rooftop solar generation across houses with similar roof capacities can vary widely. In this paper, we present HeliosFair, a system that minimizes the total electricity costs of a community that shares solar energy and then uses Shapley values to fairly distribute the cost savings thus obtained. Using real-world data, we show that the joint CapEx and OpEx electricity costs of a community sharing solar can be reduced by 12.7% on average (11.3% on average with roof capacity constraints) over houses installing solar energy individually. Our Shapley-value-based approach can fairly distribute these savings across houses based on their contributions towards cost reduction, while commonly used ad hoc approaches are unfair under many scenarios. HeliosFair is also the first work to consider practical constraints such as the difference in solar potential across houses, rooftop capacity and weight of solar panels, making it deployable in practice.