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.

Key-value stores are a well-established technology for big data management, with many leveraging the Log-Structured Merge (LSM) tree for its high write throughput and efficient primary key lookups. However, searching for non-key values in LSM trees is slow, as it typically requires scanning all LSM tree files. Secondary indexes are a common solution, but they typically require rebuilding the entire LSM tree and involve a challenging selection of indexing attribute(s). To overcome these limitations, we propose a Bloom filter hierarchy to accelerate searching for non-key values in LSM trees. In a nutshell, a Bloom filter is built for each data file in the LSM tree, and then a hierarchy (another tree) of these Bloom filters is created. Experiments show our new indexing mechanism outperforms existing LSM methods by 80% with a small space overhead.

Analytic processing systems have been traditionally designed to optimize time performance, leaving energy as a secondary aspect. More recently, during the past decade, there has been a growing interest in addressing the energy efficiency of analytics and in particular query processing (QP), our focus in this article. Numerous solutions, spanning both software and hardware approaches, have been proposed in database systems, but they have important limitations: (i) They were designed for old QP architectures, (ii) they do not consider emerging QP AI trends, such as learned query plans and hybrid QP, and (iii) they lack a well-defined framework with clear steps, which can be applied in a modern data science ecosystem. With such reasons in mind, we introduce a general framework that will help researchers and industry practitioners in addressing energy-efficiency challenges. Our framework, named ${SATM}^{2}V$, integrates five major steps: (1) assessing public sentiment and alerting analysts on the real impact of data science on decarbonization, (2) conducting “under the hood” energy consumption audits to identify energy-hungry components, (3) turning on/off and tuning parameters of components to understand their contribution to energy savings. (4) developing models and measurement techniques for quantifying energy consumption, (5) developing and executing tactics for energy saving. We then turn our attention to database systems and identify relational join processing as a representative energy consumption example. QP becomes particularly difficult when dealing with queries involving multiple join operations since join ordering is known to be an NP-hard problem, whose optimal solution remains an open problem. We apply our solution framework to the specific case of hybrid QPs, studying the impact of various join ordering optimization techniques on energy efficiency. Extensive experiments are conducted using the well-known Join Order Benchmark dataset to evaluate the effectiveness and tradeoffs of several query optimization techniques on time and energy consumption.