Autonomic power management of a PC fleet
Abstract
Both the transition to green energy to reduce CO2 emissions to net-zero by 2050 and the increase in energy prices suggest that we must find ways to reduce electricity consumption in all sectors, and in particular in the ICT sector. A large majority of companies have fleets of computers for their employees, of variable size, but growing. While in the operation of these fleets one of the biggest costs is energy consumption, many of the computers spend long periods of time turned on, but idling, thus wasting large amounts of electricity.
Dynamic Power Management (DPM) is a set of techniques and methods that are applied at different levels to reduce the consumption and heat dissipation of a computer. It includes techniques as varied as microprocessor dynamic frequency scalling (DFS) or turning off devices that are not in use. The different DPM tech- niques are directed by a series of energy management policies, which establish the operating guidelines of the different components. These policies are generated using different methods, adapted to the component being managed and the objectives to be achieved.
This thesis presents a DPM technique applied to a complete computer fleet. The goal is to reduce fleet consumption by proactively shutting down computers, while maintaining high levels of user satisfaction. The generation of the policies that direct the energy management system are produced based on data collected from the fleet under study and management. Utilisation models are generated from that data and allow representing and predicting the behavior of each user, thus being able to generate fully customized policies for each user.
One of the main contributions of this thesis is the use of satisfaction as a central metric in order to solve the optimisation problem that is the generation of energy policies. New metrics are defined that allow user satisfaction to be measured when the fleet is being optimized by the energy management system and, most importantly, to generate energy policies that guarantee a certain level of satisfaction for each user.
In order to verify and apply the proposed energy management method, a tool has been implemented that allows obtaining policies for a given fleet, studying variations and, using a simulation method, generating synthetic fleet records.
Finally, a validation of the presented work has been carried out, showing the results that it is possible to save up to 90 % of the energy otherwise wasted.
Dynamic Power Management (DPM) is a set of techniques and methods that are applied at different levels to reduce the consumption and heat dissipation of a computer. It includes techniques as varied as microprocessor dynamic frequency scalling (DFS) or turning off devices that are not in use. The different DPM tech- niques are directed by a series of energy management policies, which establish the operating guidelines of the different components. These policies are generated using different methods, adapted to the component being managed and the objectives to be achieved.
This thesis presents a DPM technique applied to a complete computer fleet. The goal is to reduce fleet consumption by proactively shutting down computers, while maintaining high levels of user satisfaction. The generation of the policies that direct the energy management system are produced based on data collected from the fleet under study and management. Utilisation models are generated from that data and allow representing and predicting the behavior of each user, thus being able to generate fully customized policies for each user.
One of the main contributions of this thesis is the use of satisfaction as a central metric in order to solve the optimisation problem that is the generation of energy policies. New metrics are defined that allow user satisfaction to be measured when the fleet is being optimized by the energy management system and, most importantly, to generate energy policies that guarantee a certain level of satisfaction for each user.
In order to verify and apply the proposed energy management method, a tool has been implemented that allows obtaining policies for a given fleet, studying variations and, using a simulation method, generating synthetic fleet records.
Finally, a validation of the presented work has been carried out, showing the results that it is possible to save up to 90 % of the energy otherwise wasted.
