Year: 2023
Type: conference-paper
Abstract: Reducing the carbon emission of computing systems has become a first-order optimization goal distinct from optimizing for performance or energy consumption. Carbon emissions are due to application execution on a target system (operational emissions) and the production, transportation, and disposal of the system itself (embodied emissions). This paper investigates the impacts of different resource configurations in terms of available DRAM memory on the overall carbon emission of individual application executions. We first propose an application-centric carbon footprint model that considers DRAM and CPU. We then study the model using a widely-used key-value store (RocksDB) and Graph500 applications. The results for RocksDB indicate that the minimal emission configuration is application dependent and can lead to significant emission reductions compared to application-oblivious configurations that use higher DRAM capacity without improving performance. For some applications, small performance degradation may lead to substantial additional emission reductions.
Authors: Sudarsun Kannan, Ulrich Kremer
Institution: Rutgers, The State University of New Jersey
Language: English
Topic: Green IT and Sustainability
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Funder: National Science Foundation
OpenAlex ID: W4385488240