Reducing the Power Consumption of Servers with Bandwidth Consideration

[[abstract]]In recent years, cloud computing systems become more and more mature and cloud computing system applications are becoming more widespread. Microsoft, Google, IBM, Amazon has developed applications for the cloud computing environment. The cloud computing environment like a large pool of resources, MapReduce distribute resources in this resource pool to achieve cloud computing. Hadoop MapReduce is a distributing cloud computing system, more and many user this cloud computing system. Green Mater[1] is a resource manager in a cloud computing system which is based on Hadoop MapReduce, Green Master[1] distribute jobs out to the virtual machines with higher computing performance rather than that with lower computing performance. Green Master[2] select virtual machines which have higher performance and assign tasks and reducing virtual machines achieves power-saving effect. This paper focuses on adding network bandwidth in Green Master, we consider the transmission speed of network bandwidth into Green Master and filter out three appropriate virtual machines, and those virtual machines have better performance than Green Master select.[[conferencetype]]國際[[conferencedate]]20140827~20140829[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Kokura, Kyushu, Japa

Seebeck Coefficients in Nanoscale Junctions: Effects of Electron-vibration Scattering and Local Heating

We report first-principles calculations of inelastic Seebeck coefficients in an aluminum monatomic junction. We compare the elastic and inelastic Seebeck coefficients with and without local heating. In the low temperature regime, the signature of normal modes in the profiles of the inelastic Seebeck effects is salient. The inelastic Seebeck effects are enhanced by the normal modes, and further magnified by local heating. In the high temperature regime, the inelastic Seebeck effects are weakly suppressed due to the quasi-ballistic transport.Comment: 3 Figure