In recent years,vehicular cloud computing(VCC)has gained vast attention for providing a variety of services by creating virtual machines(VMs).These VMs use the resources that are present in modern smart vehicles.Many ...In recent years,vehicular cloud computing(VCC)has gained vast attention for providing a variety of services by creating virtual machines(VMs).These VMs use the resources that are present in modern smart vehicles.Many studies reported that some of these VMs hosted on the vehicles are overloaded,whereas others are underloaded.As a circumstance,the energy consumption of overloaded vehicles is drastically increased.On the other hand,underloaded vehicles are also drawing considerable energy in the underutilized situation.Therefore,minimizing the energy consumption of the VMs that are hosted by both overloaded and underloaded is a challenging issue in the VCC environment.The proper and efcient utilization of the vehicle’s resources can reduce energy consumption signicantly.One of the solutions is to improve the resource utilization of underloaded vehicles by migrating the over-utilized VMs of overloaded vehicles.On the other hand,a large number of VM migrations can lead to wastage of energy and time,which ultimately degrades the performance of the VMs.This paper addresses the issues mentioned above by introducing a resource management algorithm,called resource utilization-aware VM migration(RU-VMM)algorithm,to distribute the loads among the overloaded and underloaded vehicles,such that energy consumption is minimized.RU-VMM monitors the trend of resource utilization to select the source and destination vehicles within a predetermined threshold for the process of VM migration.It ensures that any vehicles’resource utilization should not exceed the threshold before or after the migration.RU-VMM also tries to avoid unnecessary VM migrations between the vehicles.RU-VMM is extensively simulated and tested using nine datasets.The results are carried out using three performance metrics,namely number of nal source vehicles(nfsv),percentage of successful VM migrations(psvmm)and percentage of dropped VM migrations(pdvmm),and compared with threshold-based algorithm(i.e.,threshold)and cumulative sum(CUSUM)algorithm.The comparisons show that the RU-VMM algorithm performs better than the existing algorithms.RU-VMM algorithm improves 16.91%than the CUSUM algorithm and 71.59%than the threshold algorithm in terms of nfsv,and 20.62%and 275.34%than the CUSUM and threshold algorithms in terms of psvmm.展开更多
For the first time in the world advanced multi layered Red Mud and MWCNTs (ARMC) based EMI shielding material has been developed at CSIR-AMPRI, Bhopal. Red mud provides oxides of titanium and iron as precursor and the...For the first time in the world advanced multi layered Red Mud and MWCNTs (ARMC) based EMI shielding material has been developed at CSIR-AMPRI, Bhopal. Red mud provides oxides of titanium and iron as precursor and the MWCNTs provides electrical conductivity characteristics necessary for making desired EMI shielding materials. The novel process involves unique designing of chemical compositions and mineralogical phases of red mud, MWCNTs together with appropriate additive and solvent which results in the simultaneous and synergistic chemical reactions among various constituents thereby forming tailored precursor powder. Further, the ceramic processing of tailored precursor powder in appropriate environment enables formation of advanced ARMC shielding material having a variety of ceramic phases with multi elemental compositions and multi layered crystal structures. The synthesized material was characterized by various techniques namely XRD, PL, FESEM, EDXA. The reflection loss (R. L.) of the sample was calculated based on the measured complex permittivity and permeability. The advanced ARMC material with thickness t = 1.5 mm showed a minimum R. L. of -35.5 Db at 14.0 GHz with a response band width of 1.8 GHz. Thus, the developed advanced ARMC material acts as a good EMI wave absorber.展开更多
文摘In recent years,vehicular cloud computing(VCC)has gained vast attention for providing a variety of services by creating virtual machines(VMs).These VMs use the resources that are present in modern smart vehicles.Many studies reported that some of these VMs hosted on the vehicles are overloaded,whereas others are underloaded.As a circumstance,the energy consumption of overloaded vehicles is drastically increased.On the other hand,underloaded vehicles are also drawing considerable energy in the underutilized situation.Therefore,minimizing the energy consumption of the VMs that are hosted by both overloaded and underloaded is a challenging issue in the VCC environment.The proper and efcient utilization of the vehicle’s resources can reduce energy consumption signicantly.One of the solutions is to improve the resource utilization of underloaded vehicles by migrating the over-utilized VMs of overloaded vehicles.On the other hand,a large number of VM migrations can lead to wastage of energy and time,which ultimately degrades the performance of the VMs.This paper addresses the issues mentioned above by introducing a resource management algorithm,called resource utilization-aware VM migration(RU-VMM)algorithm,to distribute the loads among the overloaded and underloaded vehicles,such that energy consumption is minimized.RU-VMM monitors the trend of resource utilization to select the source and destination vehicles within a predetermined threshold for the process of VM migration.It ensures that any vehicles’resource utilization should not exceed the threshold before or after the migration.RU-VMM also tries to avoid unnecessary VM migrations between the vehicles.RU-VMM is extensively simulated and tested using nine datasets.The results are carried out using three performance metrics,namely number of nal source vehicles(nfsv),percentage of successful VM migrations(psvmm)and percentage of dropped VM migrations(pdvmm),and compared with threshold-based algorithm(i.e.,threshold)and cumulative sum(CUSUM)algorithm.The comparisons show that the RU-VMM algorithm performs better than the existing algorithms.RU-VMM algorithm improves 16.91%than the CUSUM algorithm and 71.59%than the threshold algorithm in terms of nfsv,and 20.62%and 275.34%than the CUSUM and threshold algorithms in terms of psvmm.
文摘For the first time in the world advanced multi layered Red Mud and MWCNTs (ARMC) based EMI shielding material has been developed at CSIR-AMPRI, Bhopal. Red mud provides oxides of titanium and iron as precursor and the MWCNTs provides electrical conductivity characteristics necessary for making desired EMI shielding materials. The novel process involves unique designing of chemical compositions and mineralogical phases of red mud, MWCNTs together with appropriate additive and solvent which results in the simultaneous and synergistic chemical reactions among various constituents thereby forming tailored precursor powder. Further, the ceramic processing of tailored precursor powder in appropriate environment enables formation of advanced ARMC shielding material having a variety of ceramic phases with multi elemental compositions and multi layered crystal structures. The synthesized material was characterized by various techniques namely XRD, PL, FESEM, EDXA. The reflection loss (R. L.) of the sample was calculated based on the measured complex permittivity and permeability. The advanced ARMC material with thickness t = 1.5 mm showed a minimum R. L. of -35.5 Db at 14.0 GHz with a response band width of 1.8 GHz. Thus, the developed advanced ARMC material acts as a good EMI wave absorber.