Realization of Virtual Resource Management Framework in IaaS Cloud Federation

Author(s):  
Anant V. Nimkar ◽  
Soumya K. Ghosh
2021 ◽  
Author(s):  
Alireza Mahini ◽  
Reza Berangi ◽  
Amir Masoud Rahmani ◽  
Hamidreza Navidi

2005 ◽  
Vol 12 (6) ◽  
pp. 11-17 ◽  
Author(s):  
G.T. Karetsos ◽  
S.A. Kyriazakos ◽  
E. Groustiotis ◽  
F. Di Giandomenico ◽  
I. Mura

2006 ◽  
Vol 36 (12) ◽  
pp. 3053-3062 ◽  
Author(s):  
Robert K McCann ◽  
Bruce G Marcot ◽  
Rick Ellis

In this introduction to the following series of papers on Bayesian belief networks (BBNs) we briefly summarize BBNs, review their application in ecology and natural resource management, and provide an overview of the papers in this section. We suggest that BBNs are useful tools for representing expert knowledge of an ecosystem, evaluating potential effects of alternative management decisions, and communicating with nonexperts about making natural resource management decisions. BBNs can be used effectively to represent uncertainty in understanding and variability in ecosystem response, and the influence of uncertainty and variability on costs and benefits assigned to model outcomes or decisions associated with natural resource management. BBN tools also lend themselves well to an adaptive-management framework by posing testable management hypotheses and incorporating new knowledge to evaluate existing management guidelines.


Author(s):  
Shanthi Thangam Manukumar ◽  
Vijayalakshmi Muthuswamy

With the development of edge devices and mobile devices, the authenticated fast access for the networks is necessary and important. To make the edge and mobile devices smart, fast, and for the better quality of service (QoS), fog computing is an efficient way. Fog computing is providing the way for resource provisioning, service providers, high response time, and the best solution for mobile network traffic. In this chapter, the proposed method is for handling the fog resource management using efficient offloading mechanism. Offloading is done based on machine learning prediction technology and also by using the KNN algorithm to identify the nearest fog nodes to offload. The proposed method minimizes the energy consumption, latency and improves the QoS for edge devices, IoT devices, and mobile devices.


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