Virtualization is a common technology for resource sharing in data center. To make efficient use of data center resources, the key challenge is to map customer demands （modeled as virtual data center, VDC） to the ph...Virtualization is a common technology for resource sharing in data center. To make efficient use of data center resources, the key challenge is to map customer demands （modeled as virtual data center, VDC） to the physical data center effectively. In this paper, we focus on this problem. Distinct with previous works, our study of VDC embedding problem is under the assumption that switch resource is the bottleneck of data center networks （DCNs）. To this end, we not only propose relative cost to evaluate embedding strategy, decouple embedding problem into VM placement with marginal resource assignment and virtual link mapping with decided source-destination based on the property of fat-tree, but also design the traffic aware embedding algorithm （TAE） and first fit virtual link mapping （FFLM） to map virtual data center requests to a physical data center. Simulation results show that TAE＋FFLM could increase acceptance rate and reduce network cost （about 49% in the case） at the same time. The traffie aware embedding algorithm reduces the load of core-link traffic and brings the optimization opportunity for data center network energy conservation.展开更多
Cardiac current source reconstruction is investigated by a fast greedy sparse(FGS) method applied to simulated and real magnetocardiography(MCG) data measured using 61-channel superconducting quantum interference devi...Cardiac current source reconstruction is investigated by a fast greedy sparse(FGS) method applied to simulated and real magnetocardiography(MCG) data measured using 61-channel superconducting quantum interference device. The approach reduces the size of the lead field matrix based on a priori knowledge of dipolar magnetic field map. Consequently, the computational demands and the accuracy of sparse source reconstruction are improved simultaneously. The simulation results demonstrate that the FGS method is capable of reconstructing sparse equivalent current sources using the magnetic field data generated by a single current source with varying orientation or multiple current sources generated randomly. In addition, we analyze the cardiac current source reconstructed with real MCG data at typical instants and discuss the electrical excitation conduction during the QRS complex based on moving sparse source imaging.展开更多
基金This research was partially supported by the National Grand Fundamental Research 973 Program of China under Grant （No. 2013CB329103）, Natural Science Foundation of China grant （No. 61271171）, the Fundamental Research Funds for the Central Universities （ZYGX2013J002, ZYGX2012J004, ZYGX2010J002, ZYGX2010J009）, Guangdong Science and Technology Project （2012B090500003, 2012B091000163, 2012556031）.
文摘Virtualization is a common technology for resource sharing in data center. To make efficient use of data center resources, the key challenge is to map customer demands （modeled as virtual data center, VDC） to the physical data center effectively. In this paper, we focus on this problem. Distinct with previous works, our study of VDC embedding problem is under the assumption that switch resource is the bottleneck of data center networks （DCNs）. To this end, we not only propose relative cost to evaluate embedding strategy, decouple embedding problem into VM placement with marginal resource assignment and virtual link mapping with decided source-destination based on the property of fat-tree, but also design the traffic aware embedding algorithm （TAE） and first fit virtual link mapping （FFLM） to map virtual data center requests to a physical data center. Simulation results show that TAE＋FFLM could increase acceptance rate and reduce network cost （about 49% in the case） at the same time. The traffie aware embedding algorithm reduces the load of core-link traffic and brings the optimization opportunity for data center network energy conservation.
基金supported by the National Natural Science Foundation of China(60771030)the National HighTechnology Research and Development Program of China(2008AA02Z308)+2 种基金the Shanghai Science and Technology Development Foundation(08JC1421800)Shanghai Leading Academic Discipline Project(B004)the Open Project of State Key Laboratory of Function Materials for Information(Shanghai Institute of Microsystem and Information Technology,Chinese Academy of Sciences)
文摘Cardiac current source reconstruction is investigated by a fast greedy sparse(FGS) method applied to simulated and real magnetocardiography(MCG) data measured using 61-channel superconducting quantum interference device. The approach reduces the size of the lead field matrix based on a priori knowledge of dipolar magnetic field map. Consequently, the computational demands and the accuracy of sparse source reconstruction are improved simultaneously. The simulation results demonstrate that the FGS method is capable of reconstructing sparse equivalent current sources using the magnetic field data generated by a single current source with varying orientation or multiple current sources generated randomly. In addition, we analyze the cardiac current source reconstructed with real MCG data at typical instants and discuss the electrical excitation conduction during the QRS complex based on moving sparse source imaging.
