Exploiting green energy to reduce the operational costs of multi-center web search engines

Carbon dioxide emissions resulting from fossil fuels (brown energy) combustion are the main cause of global warming due to the greenhouse effect. Large IT companies have re- cently increased their efforts in reducing the carbon dioxide footprint originated from their data center electricity con- sumption. On one hand, better infrastructure and mod- ern hardware allow for a more efficient usage of electric re- sources. On the other hand, data-centers can be powered by renewable sources (green energy) that are both environ- mental friendly and economically convenient. In this paper, we tackle the problem of targeting the us- age of green energy to minimize the expenditure of running multi-center Web search engines, i.e., systems composed by multiple, geographically remote, computing facilities. We propose a mathematical model to minimize the op- erational costs of multi-center Web search engines by ex- ploiting renewable energies whenever available at different locations. Using this model, we design an algorithm which decides what fraction of the incoming query load arriving into one processing facility must be forwarded to be pro- cessed at different sites to use green energy sources. We experiment using real traffic from a large search engine and we compare our model against state of the art baselines for query forwarding. Our experimental results show that the proposed solution maintains an high query throughput, while reducing by up to ∼25% the energy operational costs of multi-center search engines. Additionally, our algorithm can reduce the brown energy consumption by almost 6% when energy-proportional servers are employed.