Extreme dynamic mapping: Animals map themselves on the “Cloud”

Author(s):  
Eugene Potapov ◽  
Valery Hronusov
Keyword(s):  
2021 ◽  
Vol 37 (1--4) ◽  
pp. 1-27
Author(s):  
Yiming Zhang ◽  
Chengfei Zhang ◽  
Yaozheng Wang ◽  
Kai Yu ◽  
Guangtao Xue ◽  
...  

Unikernel specializes a minimalistic LibOS and a target application into a standalone single-purpose virtual machine (VM) running on a hypervisor, which is referred to as (virtual) appliance . Compared to traditional VMs, Unikernel appliances have smaller memory footprint and lower overhead while guaranteeing the same level of isolation. On the downside, Unikernel strips off the process abstraction from its monolithic appliance and thus sacrifices flexibility, efficiency, and applicability. In this article, we examine whether there is a balance embracing the best of both Unikernel appliances (strong isolation) and processes (high flexibility/efficiency). We present KylinX, a dynamic library operating system for simplified and efficient cloud virtualization by providing the pVM (process-like VM) abstraction. A pVM takes the hypervisor as an OS and the Unikernel appliance as a process allowing both page-level and library-level dynamic mapping. At the page level, KylinX supports pVM fork plus a set of API for inter-pVM communication (IpC, which is compatible with conventional UNIX IPC). At the library level, KylinX supports shared libraries to be linked to a Unikernel appliance at runtime. KylinX enforces mapping restrictions against potential threats. We implement a prototype of KylinX by modifying MiniOS and Xen tools. Extensive experimental results show that KylinX achieves similar performance both in micro benchmarks (fork, IpC, library update, etc.) and in applications (Redis, web server, and DNS server) compared to conventional processes, while retaining the strong isolation benefit of VMs/Unikernels.


2021 ◽  
Vol 13 (11) ◽  
pp. 2126
Author(s):  
Yuliang Wang ◽  
Mingshi Li

Vegetation measures are crucial for assessing changes in the ecological environment. Fractional vegetation cover (FVC) provides information on the growth status, distribution characteristics, and structural changes of vegetation. An in-depth understanding of the dynamic changes in urban FVC contributes to the sustainable development of ecological civilization in the urbanization process. However, dynamic change detection of urban FVC using multi-temporal remote sensing images is a complex process and challenge. This paper proposed an improved FVC estimation model by fusing the optimized dynamic range vegetation index (ODRVI) model. The ODRVI model improved sensitivity to the water content, roughness degree, and soil type by minimizing the influence of bare soil in areas of sparse vegetation cover. The ODRVI model enhanced the stability of FVC estimation in the near-infrared (NIR) band in areas of dense and sparse vegetation cover through introducing the vegetation canopy vertical porosity (VCVP) model. The verification results confirmed that the proposed model had better performance than typical vegetation index (VI) models for multi-temporal Landsat images. The coefficient of determination (R2) between the ODRVI model and the FVC was 0.9572, which was 7.4% higher than the average R2 of other typical VI models. Moreover, the annual urban FVC dynamics were mapped using the proposed improved FVC estimation model in Hefei, China (1999–2018). The total area of all grades FVC decreased by 33.08% during the past 20 years in Hefei, China. The areas of the extremely low, low, and medium grades FVC exhibited apparent inter-annual fluctuations. The maximum standard deviation of the area change of the medium grade FVC was 13.35%. For other grades of FVC, the order of standard deviation of the change ratio was extremely low FVC > low FVC > medium-high FVC > high FVC. The dynamic mapping of FVC revealed the influence intensity and direction of the urban sprawl on vegetation coverage, which contributes to the strategic development of sustainable urban management plans.


2016 ◽  
Vol 8 (11) ◽  
pp. 931 ◽  
Author(s):  
Jing Wang ◽  
Jingfeng Huang ◽  
Ping Gao ◽  
Chuanwen Wei ◽  
Lamin Mansaray

Author(s):  
Sergio Salvatore ◽  
Alessandro Gennaro ◽  
Andrea Auletta ◽  
Rossano Grassi ◽  
Diego Rocco

2016 ◽  
Vol 8 (2) ◽  
pp. 160 ◽  
Author(s):  
Galo Carrillo-Rojas ◽  
Brenner Silva ◽  
Mario Córdova ◽  
Rolando Célleri ◽  
Jörg Bendix

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