Multilevel resource allocation for performanceaware energyefficient cloud data centers The massive power consumption of data centers has been a recurring concern in current research. In cloud environments, lots of methods are being adopted that aim for energy efficiency. However, although such methods enable the decrease in power consumption, they regularly affect application performance. In this paper, we present a multilevel resource allocation approach towards dynamic network bandwidth at the physical substrate, managing different powersaving states and workload allocation at the cloud infrastructure at the same time employ virtual machine allocation and selection policies at the cloud platform. In order to evaluate our approach, tests were carried out in a simulated environment using scaleout application on a dynamic cloud infrastructure. Results showed that our proposal presents a better balance regarding a more energyefficient data center with a smaller impact on application performance when compared with other works discussed in the literature.

Adaptive MultiThreshold EnergyAware Virtual Machine Consolidation in Cloud Data Center The everincreasing energy consumption in cloud data centers not only translates to high operating costs, but also leads to negative impact on environment. Dynamic consolidation of virtual machine (VM) is proven to be an efficient way to improve resource utilization and reduce energy consumption in cloud data centers. In this paper, both the CPU utilization of system and SLA of users are taken into account to classify hosts and an adaptive multithreshold energyaware virtual machine consolidation algorithm is proposed to provide different consolidation mechanisms for different types of hosts. First, compound threshold is designed for overload hosts and will be adjusted dynamically to ensure both CPU utilization and SLA. Then a QIearning based method is proposed to further divide underload hosts to save energy. Experiment results show that, our proposed algorithm can optimize resource utilization and reduce energy consumption of the data centers while minimizing the SLA violation rate and the number of migrations.