Optimizing Energy Consumed by Analytics in the Cloud

Given the climate change crisis, there is a worldwide growing concern on energy production, energy consumption and pollution. Cloud computing represents a small fraction of global energy consumption, but trends indicate it will continue to grow, driven by Big Data and AI. AI analytics are pushing computing resources, especially CPUs and GPUs, to their limits. However, powerful CPUs and GPUs, consume tons of energy and require cooling appliances, which results into higher operating cost and as an indirect consequence, higher pollution and global warming. Based on these issues, we present a survey on measuring and reducing energy, especially when processing analytic workloads. We discuss tradeoffs between high performance (low latency to get results) and low energy (less power consumed over time). Our focus is on identifying modern hardware components which have a significant impact on energy consumption and then examining how software optimizations can manage hardware to reduce energy in a cloud data center. We conclude with a tentative research agenda, based on the state of the art of research at the intersection of big data analytics, high performance computing, electrical energy and cloud computing.