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This paper proposes a methodology for energy aware management of virtualized data centers (DC) based on dynamically adapting and scaling the computing capacity to the characteristics of the workload. To assess the energy efficiency of DC operation, we have defined a novel ontological model for representing its energy and performance characteristics and a new metric for aggregating Green and Key Performance...
In this paper we propose the development of an Energy Aware Context Model for representing the service centre energy/performance related data in a uniform and machine interpretable manner. The model is instantiated at run-time with the service center energy/performance data collected by monitoring tools. Energy awareness is achieved by using reasoning processes on the model instance ontology representation...
In this paper we approach the high energy consumption problem of large virtualized service centers by proposing a dynamic server consolidation methodology for optimizing the service center IT computing resources usage. The consolidation methodology is based on logically structuring the service center servers hierarchical clusters, consolidation decisions being taken in each cluster using a reinforcement...
This paper addresses the problem of run-time management of a service center energy efficiency by using a context aware self-adapting algorithm. The algorithm adapts the service center energy consumption to the incoming workload by considering service center predefined Green Performance Indicators (GPI) and Key Performance Indicators (KPI). The service center energy performance context is obtained...
This paper presents a self-adapting algorithm that can automatically detect the changes in a system execution context and decide how the system should react. The self-adapting algorithm is characterized by a closed feedback loop with four phases: monitoring, analyzing, planning and execution. The monitoring phase uses the RAP (Resources, Actions, Policies) context model to represent in a programmatic...
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