Data centres are quickly evolving to support new demands for Cloud-Computing services. Extreme workload peaks
represent a challenge for the maintenance of the performance and service level agreements, even more when operation
costs need to be minimised. In this paper, we first present an extensive analysis of the impact of extreme workloads in
large-scale realistic Cloud-Computing data centres, including a comparison between the most relevant centralised resource managing models. Moreover, we extend our previous works by proposing a new energy-efficiency policy called Bullfighter
which is able to keep performance key performance indicators while reducing energy consumption in extreme scenarios.
This policy employs queue-theory distributions to foresee workload demands and adapt automatically to workload fluc tuations even in extreme environments, while avoiding the fine-tuning required for other energy policies. Finally, it is
shown through extensive simulation that Bullfighter can save more than 40% of energy in the aforementioned scenarios
without exerting any noticeable impact on data-centre performance.Ministerio de Ciencia e Innovación RTI2018-098062-A-I0
