Runtime data center temperature prediction using Grammatical Evolution techniques

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@Article{journals/asc/ZapaterRAAMH16,
  author =       "Marina Zapater and Jose L. Risco-Martin and 
                 Patricia Arroba and Jose L. Ayala and Jose Manuel Moya and 
                 Roman Hermida",
  title =        "Runtime data center temperature prediction using
                 Grammatical Evolution techniques",
  journal =      "Applied Soft Computing",
  year =         "2016",
  volume =       "49",
  pages =        "94--107",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  bibdate =      "2017-05-26",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/https://doi.org/10.1016/j.asoc.2016.07.042;
                 DBLP,
                 http://dblp.uni-trier.de/db/journals/asc/asc49.html#ZapaterRAAMH16",
  DOI =          "doi:10.1016/j.asoc.2016.07.042",
  abstract =     "Data Centers are huge power consumers, both because of
                 the energy required for computation and the cooling
                 needed to keep servers below thermal redlining. The
                 most common technique to minimise cooling costs is
                 increasing data room temperature. However, to avoid
                 reliability issues, and to enhance energy efficiency,
                 there is a need to predict the temperature attained by
                 servers under variable cooling setups. Due to the
                 complex thermal dynamics of data rooms, accurate
                 runtime data center temperature prediction has remained
                 as an important challenge. By using Grammatical
                 Evolution techniques, this paper presents a methodology
                 for the generation of temperature models for data
                 centers and the runtime prediction of CPU and inlet
                 temperature under variable cooling setups. As opposed
                 to time costly Computational Fluid Dynamics techniques,
                 our models do not need specific knowledge about the
                 problem, can be used in arbitrary data centers,
                 re-trained if conditions change and have negligible
                 overhead during runtime prediction. Our models have
                 been trained and tested by using traces from real Data
                 Center scenarios. Our results show how we can fully
                 predict the temperature of the servers in a data rooms,
                 with prediction errors below 2C and 0.5C in CPU and
                 server inlet temperature respectively.",
}

Genetic Programming entries for Marina Zapater Jose L Risco-Martin Patricia Arroba Jose Luis Ayala Rodrigo Jose Manuel Moya Roman Hermida Correa

Citations