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ACM SIGEnergy Energy Informatics Review is a peer-reviewed journal published by ACM, covering Smart Grid Energy Management, Green IT and Sustainability, Cloud Computing and Resource Management.
| Publisher | ACM |
|---|---|
| Reference style | Numbered (ACM) Numbered — [1], [2] in the text [1] Ada Smith, Ben Jones, and Cara Lee. 2023. A representative article title. ACM SIGEnergy Energy Informatics Review 12, 3, Article 45 (2023).
Formats any DOI in ACM SIGEnergy Energy Informatics Review style. No sign-up. |
| Publishes research in | Smart Grid Energy Management Green IT and Sustainability Cloud Computing and Resource Management Parallel Computing and Optimization Techniques Smart Grid Security and Resilience |
| ISSN | 2770-5331 |
| Citation impact (2-yr) | 2.33 |
| h-index | 14 |
| i10-index | 19 |
| Total citations | 690 |
| Top institutions publishing here | University of Massachusetts Amherst |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Scalable Solid-State Drives (SSDs) have ushered in a transformative era in data storage and accessibility, spanning both data centers and portable devices. However, the strides made in scaling this technology can bear significant environmental consequences. On a global scale, a notable portion of semiconductor manufacturing relies on electricity derived from coal and natural gas sources.…
The labeling of electricity is considered an important mechanism to differentiate renewable power generation and, thus, to incentivize the expansion of green energy. However, today's systems for documenting and trading green energy certificates suffer from multiple challenges. These could be addressed by a digital solution that holistically collects and processes production and consumption data. Blockchain-based…
The end of Dennard scaling and the slowing of Moore's Law has put the energy use of datacenters on an unsustainable path. Datacenters are already a significant fraction of worldwide electricity use, with application demand scaling at a rapid rate. We argue that substantial reductions in the carbon intensity of datacenter computing are possible with…
Despite several calls from the community for improving the sustainability of computing, sufficient progress is yet to be made on one of the key prerequisites of sustainable computing---the ability to define and measure computing sustainability holistically. This position paper proposes metrics that aim to measure the end-to-end sustainability footprint in data centers. To enable useful…
The rapid adoption of large language models (LLMs) has led to significant advances in natural language processing and text generation. However, the energy consumed through LLM model inference remains a major challenge for sustainable AI deployment. To address this problem, we model the workload-dependent energy consumption and runtime of LLM inference tasks on heterogeneous GPU-CPU…
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