The carbon footprint of scientific computing is substantial, but environmentally sustainable computational science (ESCS) is a nascent field with many opportunities to thrive. To realize the immense green opportunities and continued, yet sustainable, growth of computer science, we must take a coordinated approach to our current challenges, including greater awareness and transparency, improved estimation and wider reporting of environmental impacts. Here, we present a snapshot of where ESCS stands today and introduce the GREENER set of principles, as well as guidance for best practices moving forward. The carbon footprint of computational sciences is substantial, but there is an immense opportunity to lead the way towards sustainable research. In this Perspective the authors lay some fundamental principles to transform computational science into an exemplar of broad societal impact and sustainability. 

/pdf/greener-principles-for-environmentally-sustainable-2gc0crnv.pdf

GREENER principles for environmentally sustainable computational science

Machine learning and deep learning models have become essential in the recent fast development of artificial intelligence in many sectors of the society. It is now widely acknowledge that the development of these models has an environmental cost that has been analyzed in many studies. Several online and software tools have been developed to track energy consumption while training machine learning models. In this paper, we propose a comprehensive introduction and comparison of these tools for AI practitioners wishing to start estimating the environmental impact of their work. We review the specific vocabulary, the technical requirements for each tool. We compare the energy consumption estimated by each tool on two deep neural networks for image processing and on different types of servers. From these experiments, we provide some advice for better choosing the right tool and infrastructure. 

How to estimate carbon footprint when training deep learning models? A guide and review

Machine learning and deep learning models have become essential in the recent fast development of artificial intelligence in many sectors of the society. It is now widely acknowledge that the development of these models has an environmental cost that has been analyzed in many studies. Several online and software tools have been developed to track energy consumption while training machine learning models. In this paper, we propose a comprehensive introduction and comparison of these tools for AI practitioners wishing to start estimating the environmental impact of their work. We review the specific vocabulary, the technical requirements for each tool, and provide some advice on how and when to use these tools.

pdf/how-to-estimate-carbon-footprint-when-training-deep-learning-1hxqbp8q.pdf

Efficiency has been a pivotal aspect of the software industry since its inception, as a system that serves the end-user fast, and the service provider cost-efficiently benefits all parties. A database management system (DBMS) is an integral part of effectively all software systems, and therefore it is logical that different studies have compared the performance of different DBMSs in hopes of finding the most efficient one. This study systematically synthesizes the results and approaches of studies that compare DBMS performance and provides recommendations for industry and research. The results show that performance is usually tested in a way that does not reflect real-world use cases, and that tests are typically reported in insufficient detail for replication or for drawing conclusions from the stated results. 

https://doi.org/10.1016/j.jss.2023.111872

Database management system performance comparisons: A systematic literature review

Major cloud providers have stated public plans to lower their carbon emissions. Historically, this has meant focusing on emissions from producing the electricity consumed by datacenters. While work and challenges remain on this avenue, research and industry are actively working on the next step of reducing carbon embedded in servers and racks. At a high level, a promising direction to reduce embodied carbon is to avoid emissions from new manufacturing, which often requires using existing components, devices, and buildings for longer. However, much of the data around carbon breakdowns and reduction opportunities remains silo-ed, leading to speculations and assumptions - both internally and externally - around the opportunities to reduce datacenter carbon intensity. We aim to clarify some of the misconceptions we have encountered. 

Myths and Misconceptions Around Reducing Carbon Embedded in Cloud Platforms

Energy consumption estimation and profiling for queries in distributed database systems based on a bottom-up comprehensive energy model

Edge computing (EC) serves as an effective technology, empowering end‐users to attain high bandwidth and low latency by offloading tasks with high computational demands from mobile devices to edge servers. However, a major challenge arises when the processing load fluctuates continuously, leading to a performance bottleneck due to the inability to rescale edge node (EN) resources. To address this problem, the approach of task migration is introduced, and EN load prediction model, the resource constrained model, optimal communication overhead model, optimal task migration model, and energy consumption model are built to form a theoretical foundation from which to propose a task migration based resilient framework construction method in EC. With the aid of the domino effect and the combined effect of task migration, a dynamic node‐growing algorithm (DNGA) and a dynamic node‐shrinking algorithm (DNSA), both based on the task migration strategy, are proposed. Specifically, the DNGA smoothly expands the EN scale when the processing load increases, while the DNSA shrinks the EN scale when the processing load decreases. The experimental results show that for standard benchmarks deployed on an elastic framework, the proposed method realizes a smooth scaling mechanism in the EC, which reduces the latency and improves the reliability of data processing.

An elastic framework construction method based on task migration in edge computing

For the study of user sharing behavior in social media, the network environment and incentive mechanisms are two important aspects. Based on the social network theory, this paper put forward the hypothesis, using crawler technology to grab user data on Sina Microblog platform. Based on 1774 samples, using SPSS software to carry out descriptive statistics, correlation analysis and regression analysis. The research shows that the network density in social media positively influences the user's reward-sharing behavior, network site has no significant effect on the reward sharing behavior of users.

/pdf/the-influence-of-user-network-environment-on-reward-sharing-28fv4pnf0r.pdf

The Influence of User Network Environment on Reward Sharing Behavior in Social Media

Cardinality estimation for property graph queries with gated learning approach on the graph database

ILSHIP: An interpretable and predictive model for hypothyroidism

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