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To mitigate climate change, we must reduce carbon emissions from hyperscale cloud computing. We find that cloud compute servers cause the majority of emissions in a general-purpose cloud. Thus, we motivate designing carbon-efficient compute server SKUs, or GreenSKUs, using recently-available low-carbon server components. To this end, we design and build three GreenSKUs using low-carbon components, such as energy-efficient CPUs, reused old DRAM via CXL, and reused old SSDs. We detail several challenges that limit GreenSKUs, carbon savings at scale and may prevent their adoption by cloud providers. To address these challenges, we develop a novel methodology and associated framework, GSF (GreenSKU Framework), that enables a cloud provider to systematically evaluate a GreenSKU’s carbon savings at scale. We implement GSF within Microsoft Azure’s production constraints to evaluate our three GreenSKUs’ carbon savings. Using GSF, we show that our most carbon-efficient GreenSKU reduces emissions per core by 28% compared to currently-deployed cloud servers. When designing GreenSKUs to meet applications’ performance requirements, we reduce emissions by 15%. When incorporating overall data center overheads, our GreenSKU reduces Azure’s net cloud emissions by 8%. 

If a trader could predict price changes in the stock market better than other traders, she would make a fortune. Similarly in the electricity market, a trader that could predict changes in the electricity load, and thus electricity prices, would be able to make large profits. Predicting price changes in the electricity market better than other market participants is hard, but in this paper, we show that attackers can manipulate the electricity prices in small but predictable ways, giving them a competitive advantage in the market. Our attack is possible when the adversary controls a botnet of high wattage devices such as air conditioning units, which are able to abruptly change the total demand of the power grid. Such attacks are called Manipulation of Demand via IoT (MaDIoT) attacks. In this paper, we present a new variant of MaDIoT and name it Manipulation of Market via IoT (MaMIoT). MaMIoT is the first energy market manipulation cyberattack that leverages high wattage IoT botnets to slightly change the total demand of the power grid with the aim of affecting the electricity prices in the favor of specific market players. Using real-world data obtained from two major energy markets, we show that MaMIoT can significantly increase the profit of particular market players or financially damage a group of players depending on the motivation of the attacker.