The ability to measure the energy consumed by cloud infrastructure is a crucial step towards the development of energy efficiency policies in the cloud infrastructure. There are hardware-based and software-based methods of measuring energy usage in cloud infrastructure. However, most hardware-based energy measurement methods measure the energy consumed system-wide - including the energy lost in transit. In an environment such as the cloud, where energy consumption can be a result of different components, it is important to isolate the energy, which is consumed as a result of executing application workloads. This information can be crucial in making decisions such as workload consolidation. In this paper, we propose an experimental approach of measuring power consumption as a result of executing application workloads in IaaS cloud. This approach is based on Intel’s Running Average Power Limit (RAPL) interface. Application workload is obtained from Phoronix Test Suite (PTS)’ 7zip and aio-stress. To demonstrate the feasibility of this approach, we have described an approach, which can be used to study the effect of workload consolidation on CPU and I/O's power performance by varying the number of Virtual Machines (VMs) . Power is measured in watts. Performance of CPU is measured in Million Instructions per Second (MIPS) and I/O performance (as a result of processing data intensive) is measured in MB/s. Our results on the effect of workload consolidation has been compared with previous research and was found to be consistent. This shows that the proposed method of measuring power consumption is accurate. پرونده مقاله

This paper focuses on the prediction of student learning styles using data mining techniques within their institutions. This prediction was aimed at finding out how different learning styles are achieved within learning environments which are specifically influenced by already existing factors. These learning styles, have been affected by different factors that are mainly engraved and found within the students learning environment. To obtain the learning styles, a data mining technique was used and this explicitly involved the use of pattern analysis in order to identify the underlying learning styles in the data collected from the learners. This paper highlights the five major learning styles that describe the patterns extracted from the collected data. Therefore, considering the changed learning ecosystem, it is clear that prediction of student learning styles can be done when the various factor inputs within the student environment are brought together and analyzed to focus on learning within internet-mediated environments. پرونده مقاله