Earticle

To further enhance optimizing effect of virtual machine resource allocation, virtual machine resource allocation algorithm of probabilistic optimization based on SME-FFD (Simulated Evolution – First Fit Decreasing ) is proposed aiming at NP hard optimization problem in the process of virtual machine resource allocation in cloud computing. First of all, an optimization evaluation scheme of virtual machine resource allocation is proposed, and strong climbing optimization ability of simulated evolutionary algorithm is adopted to carry out iterative evolution to selection, evaluation and ranking of virtual machine resource allocation ; after that, based on SME algorithm obtaining resource allocation ordering, the secondary allocation on virtual machine and physical host resources ranked is conducted using FFD to improve efficiency and effectiveness of resource allocation; in the end, experimental comparison is conducted in CloundSim grid lab in the University of Melbourne and gridbus cloud simulation platform, the results show that CPU usage rate of proposed algorithm reaches up to 47%, memory usage rate reaches up to 56%, therefore, it may effectively reduce physical machine usage quantity and realize the goal of energy consumption.

We present a new method for solving an inverse problem of flux emergence which transports subsurface magnetic flux from an inaccessible interior to the surface where magnetic structures may be observed to form, such as solar active regions. To make a quantitative evaluation of magnetic structures having various characteristics, we derive physical properties of subsurface magnetic field that characterize those structures formed through flux emergence. The derivation is performed by inversion from an evolutionary relation between two observables obtained at the surface, emerged magnetic flux and injected magnetic helicity, the former of which provides scale information while the latter represents the configuration of magnetic field.