Abstract

With the raising concerns about the environment, the ICT equipments have been pointed out as a major and ever rising source of energy consumption and pollution. Among those ICT equipments, data centres play obviously a major role with the rise of the Cloud computing paradigm. In the recent years, researchers have focused on reducing the energy consumption of data centres. Furthermore, future environmentally friendly data centres are also expected to prioritize the usage of renewable energies over brown energies. However, managing the energy consumption within a data centre is challenging because data centres are complex facilities which supports a huge variety of hardware, computing styles and SLAs. Those may evolve through time as user requirements can change rapidly. Furthermore, differently from non-renewable energy sources, the availability of renewable energies is very volatile and time dependent: e.g. solar power is obtainable only during the day, and is subject to variations due to the meteorological conditions. The goal in this case is to shift the workload of running applications, according to the forecasted availability of the renewable energy. In this thesis we propose a flexible framework called Plug4Green able to reduce the energy consumption of a Cloud data centre. Plug4Green is based on the Constraint Programming paradigm, allowing it to take into account a great number of constraints regarding energy, hardware and SLAs in data centres. We also propose the concept of an energy adaptive software controller (EASC), able to augment the usage of renewable energies in data centres. The EASC supports two kind of applications: service-oriented and task-oriented applications; and two kind of computing environments: Infrastructure as a Service and Platform as a Service. We evaluated our solutions in several trials executed in the testbeds of Milan and Trento, Italy. Results show that Plug4Green was able to reduce the power consumption by 27% in the Milan trial, while the EASC was able to augment the renewable energy percentage by 7.07pp in the Trento trial.