Towards Green Big Data at CERN

Tapio Niemi, Jukka K. Nurminen, Juha Matti Liukkonen, Ari Pekka Hameri

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)


High-energy physics studies collisions of particles traveling near the speed of light. For statistically significant results, physicists need to analyze a huge number of such events. One analysis job can take days and process tens of millions of collisions. Today the experiments of the large hadron collider (LHC) create 10 GB of data per second and a future upgrade will cause a ten-fold increase in data. The data analysis requires not only massive hardware but also a lot of electricity. In this article, we discuss energy efficiency in scientific computing and review a set of intermixed approaches we have developed in our Green Big Data project to improve energy efficiency of CERN computing. These approaches include making energy consumption visible to developers and users, architectural improvements, smarter management of computing jobs, and benefits of cloud technologies. The open and innovative environment at CERN is an excellent playground for different energy efficiency ideas which can later find use in mainstream computing.

Original languageEnglish
Pages (from-to)103-113
Number of pages11
JournalFuture Generation Computer Systems
Early online date2017
Publication statusPublished - 1 Apr 2018
MoE publication typeA1 Journal article-refereed


  • CERN
  • Energy efficiency
  • Green computing
  • Scientific computing


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