@PHDTHESIS{ 2016:1467870530,
 	title = {Performance-Aware Energy-Efficient Cloud Orchestration},
 	year = {2016},
 	url = "http://tede2.pucrs.br/tede2/handle/tede/7039",
 	abstract = "The high energy consumption of data centers has been a recurring issue in recent research. In cloud environments, several solutions are being used that aim for energy efficiency, ranging from scaling the processors frequency, through the use of sleep states, to virtual machine placement mechanism. Although these solutions enable the reduction in power consumption, they usually impact on the application performance. To address this limitation, we present an orchestration of different energy-savings techniques and mechanisms to improve the trade-off between energy savings and application performance. To this end, we implemented the Energy-Efficient Cloud Orchestrator ? e-eco ? a management system that acts along with the cloud platform, deciding which sleep state can be better applied on hosts, in addition to choosing the best option between virtual machines consolidation and dynamic processor frequency scaling. To evaluate e-eco, tests were carried out in a real and a simulated environment using scale-out applications on a dynamic cloud infrastructure, taking into account transactions per second as a performance metric. Results showed that our proposal presents the best improvement on the trade-off between energy savings and performance applications for cloud environments when compared with other works presented in the literature.",
 	publisher = {Pontif?cia Universidade Cat?lica do Rio Grande do Sul},
 	scholl = {Programa de P?s-Gradua??o em Ci?ncia da Computa??o},
 	note = {Faculdade de Inform?tica}
}