Abstract:With the rapid development of Internet, the traditional computing environment is making a big migration to the cloud-computing environment. However, cloud computing introduces a set of new security problems. Aiming at the virtual machine (VM) escape attack, we study the traditional attack model and attack scenarios in the cloud-computing environment. In addition, we propose an access control model that can prevent virtual machine escape (PVME) by adapting the BLP (Bell-La Padula) model (an access control model developed by D. Bell and J. LaPadula). Finally, the PVME model has been implemented on full virtualization architecture. The experimental results show that the PVME module can effectively prevent virtual machine escape while only incurring 4% to 8% time overhead.
To investigate changes in global wind speed phenomena, we constructed homogenized monthly time series (1980-2018) for 4,722 meteorological stations. Through examining monthly-averaged wind speeds (MWS), we found that seasonal wind speed range (SWSR; calculated as the difference between maximum and minimum MWS) has declined significantly by 10% since 1980 (p < 0.001). This global SWSR reduction was primarily influenced by decreases in Europe (-19%), South America (-16%), Australia (-14%), and Asia (-13%), with corresponding rate reductions of -0.13, -0.08, -0.09 and -0.06 m s-1 decade-1, respectively (p < 0.01). In contrast, the SWSR in North America rose 3%. Important is that the decrease in SWSR occurred regardless of the stilling or reversal of annual wind speed. The shrinking SWSR in Australia and South America was characterized by continuous decreases in maximum MWS and increases in the minimum. For Europe and Asia, maximum and minimum MWS declined initially after 1980, followed by substantial increases in minimum MWS (about 2000 and 2012, respectively) that preserved the long-term reduction in the range. Most reanalysis products (ERA5, ERA-Interim, and MERRA-2) and climate model simulations (AMIP6 and CMIP6) fail to reproduce the observed trends. However, some ocean-atmosphere indices (seasonality characteristics) were correlated significantly with these trends, including West Hemisphere warm pool, East Atlantic Patten, Pacific Decadal Oscillation, and others. These findings are important for increasing the understanding of mechanisms behind wind speed variations that influence a multitude of other biogeophysical processes and the development of efficient wind energy generations, now and in the future.
China has realized a 56-fold increase in installed wind capacity, from 5.9 GW in 2007 to 328 GW in 2021. In addition to increasing installed capacity, plans to substantially increase wind energy production for climate change mitigation also depend on future wind speeds, which strongly influences the efficiencies of installed turbines within individual wind farms. A reversal in globally decreasing wind speeds over several decades has been reported previously. However, subsequent studies using other data sources reported only a slight increase or no reversal in China. These uncertainties regarding China’s wind energy production hamper estimates of wind energy production potential. Here, our analysis of quality-controlled wind speed measurements from in-situ stations shows that the wind speed decline in China reversed significantly since 2012 (P < 0.001), but with substantial spatio-temporal variability. We further estimated the capacity factor growth and the wind power gain solely associated with the changes in wind speed ranges from 31.6 to 56.5 TWh yr-1 based on the 2019 installed capacity. This estimate explains 22.0 to 39.3% of the rapid increase in wind generation capacity factor in China during 2012–2019. The result implies that the site selection of wind farms should consider both current wind situation and future wind speed trends. Further studies are needed to understand the driving factor of wind speed recovery in support of the wind energy industry.
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