Impact of primary networks on the performance of energy harvesting cognitive radio networks

In this paper, we investigate the effect of of the primary network on the secondary network when harvesting energy in cognitive radio in the presence of multiple power beacons and multiple secondary transmitters. In particular, the influence of the primary transmitter's transmit power on the energy harvesting secondary network is examined by studying two scenarios of primary transmitter's location, i.e., the primary transmitter's location is near to the secondary network and the primary transmitter's location is far from the secondary network. In the scenario where the primary transmitter locates near to the secondary network, although secondary transmitter can be benefit from the harvested energy from the primary transmitter, the interference caused by the primary transmitter suppresses the secondary network performance. Meanwhile, in both scenarios, despite the fact that the transmit power of the secondary transmitter can be improved by the support of powerful power beacons, the peak interference constraint at the primary receiver limits this advantage. In addition, the deployment of multiple power beacons and multiple secondary transmitters can improve the performance of the secondary network. The analytical expressions of the outage probability of the secondary network in the two scenarios are also provided and verified by numerical simulations.

Multi-Tenant Virtual GPUs for Optimising Performance of a Financial Risk Application

Graphics Processing Units (GPUs) are becoming popular accelerators in modern High-Performance Computing (HPC) clusters. Installing GPUs on each node of the cluster is not efficient resulting in high costs and power consumption as well as underutilisation of the accelerator. The research reported in this paper is motivated towards the use of few physical GPUs by providing cluster nodes access to remote GPUs on-demand for a financial risk application. We hypothesise that sharing GPUs between several nodes, referred to as multi-tenancy, reduces the execution time and energy consumed by an application. Two data transfer modes between the CPU and the GPUs, namely concurrent and sequential, are explored. The key result from the experiments is that multi-tenancy with few physical GPUs using sequential data transfers lowers the execution time and the energy consumed, thereby improving the overall performance of the application.

Cloud Benchmarking For Maximising Performance of Scientific Applications

How can applications be deployed on the cloud to achieve maximum performance? This question is challenging to address with the availability of a wide variety of cloud Virtual Machines (VMs) with different performance capabilities. The research reported in this paper addresses the above question by proposing a six step benchmarking methodology in which a user provides a set of weights that indicate how important memory, local communication, computation and storage related operations are to an application. The user can either provide a set of four abstract weights or eight fine grain weights based on the knowledge of the application. The weights along with benchmarking data collected from the cloud are used to generate a set of two rankings - one based only on the performance of the VMs and the other takes both performance and costs into account. The rankings are validated on three case study applications using two validation techniques. The case studies on a set of experimental VMs highlight that maximum performance can be achieved by the three top ranked VMs and maximum performance in a cost-effective manner is achieved by at least one of the top three ranked VMs produced by the methodology.