Luciano Gualberto

Estudo do nitrogênio na combustão do gás natural

Nitrogen content in natural gas was studied in experimental and computational investigations to identify its influence on the emission level of exhaust gases from combustion facilities. Changes in natural gas composition with different N2 concentrations may result from introducing a new source gas into the system. An industrial burner fired at 75 kW, housed in a laboratory-scale furnace, was employed for runs where the natural gas/N2 proportion was varied. The exhaust and in-furnace measurements of temperature and gas concentrations were performed for different combustion scenarios, varying N2 content from 1-10 %v. Results have shown that the contamination of natural gas with nitrogen reduced the peak flame temperature, the concentration of unstable species, the NOX emission level and the heat transfer rate to the furnace walls, resulting from the recombination reactions.

HYPERVISOR AGNOSTIC WORKLOAD CHARACTERIZATION OF VIRTUAL MACHINES

Workload characterization is an important feature in a cloud environment. Using a fast and accurate characterization cloud providers can allocate virtual machines in physical hosts that best fit a specific workload and improve the overall performance without new investments. Current strategies of workload characterization are based on complex algorithms that are difficult to apply in a cloud environment with thousands of virtual machines running. Other strategies to characterize virtual machines rely on several changes in a hypervisor, or virtual machine layer, and are hypervisordependent. This paper presents a hypervisor agnostic characterization methodology that uses standard metrics of Processor and Memory utilization, available in SNMP. Collected data are normalized and applied to a low computational cost decision tree, that is able to characterize a virtual machine in a customizable time window. As evaluation, some tests were performed in different hypervisors (KVM, Xen and VMWare) running spec benchmark and in real workloads, such as Hadoop Cluster in Rackspace and a production Web Server running in a VMWare Farm. Results showed that our methodology is able to infer a very accurate characterization.