Implementation of a Private Cloud: A Case Study

2014 ◽  
pp. 635-647 ◽  
Author(s):  
Prachi Deshpande ◽  
S. C. Sharma ◽  
S. K. Peddoju
Keyword(s):  
Private Cloud

scholarly journals Cloud Bioinformatics in a Private Cloud Deployment

2014 ◽  
pp. 205-220 ◽  
Author(s):  
Victor Chang
Keyword(s):  
Biological Sciences ◽  
Medical Training ◽  
Area Network ◽  
Private Cloud ◽  
User Friendliness ◽  
Storage Area Network ◽  
Time Saving ◽  
Cloud Computing Adoption ◽  
Storage Area

This chapter describes service portability for a private cloud deployment, including a detailed case study about Cloud Bioinformatics services developed as part of the Cloud Computing Adoption Framework (CCAF). The Cloud Bioinformatics design and deployment is based on Storage Area Network (SAN) technologies, details of which include functionalities, technical implementation, architecture, and user support. Bioinformatics applications are written on the SAN-based private cloud, which can simulate complex biological sciences and present them in a way that anyone without prior knowledge can understand. Several bioinformatics results are discussed, particularly brain segmentation, which demonstrates different parts of the brain simulated by the private cloud. In addition, benefits of CCAF are illustrated using several bioinformatics examples such as tumour modelling, brain imaging, insulin molecules, and simulations for medical training. The Cloud Bioinformatics solution offers cost reduction, time-saving, and user friendliness.

scholarly journals Cloud Bioinformatics in a Private Cloud Deployment

2015 ◽  
pp. 2373-2388
Author(s):  
Victor Chang
Keyword(s):  
Biological Sciences ◽  
Medical Training ◽  
Area Network ◽  
Private Cloud ◽  
User Friendliness ◽  
Storage Area Network ◽  
Time Saving ◽  
Cloud Computing Adoption ◽  
Storage Area

This chapter describes service portability for a private cloud deployment, including a detailed case study about Cloud Bioinformatics services developed as part of the Cloud Computing Adoption Framework (CCAF). The Cloud Bioinformatics design and deployment is based on Storage Area Network (SAN) technologies, details of which include functionalities, technical implementation, architecture, and user support. Bioinformatics applications are written on the SAN-based private cloud, which can simulate complex biological sciences and present them in a way that anyone without prior knowledge can understand. Several bioinformatics results are discussed, particularly brain segmentation, which demonstrates different parts of the brain simulated by the private cloud. In addition, benefits of CCAF are illustrated using several bioinformatics examples such as tumour modelling, brain imaging, insulin molecules, and simulations for medical training. The Cloud Bioinformatics solution offers cost reduction, time-saving, and user friendliness.

scholarly journals Debugging Network Reachability with Blocked Paths

2021 ◽  
pp. 851-862
Author(s):  
S. Bayless ◽  
J. Backes ◽  
D. DaCosta ◽  
B. F. Jones ◽  
N. Launchbury ◽  
...  
Keyword(s):  
Path Analysis ◽  
Formal Model ◽  
Reachability Analysis ◽  
Network Behavior ◽  
Private Cloud ◽  
Industrial Case Study ◽  
Feasible Path ◽  
Network Troubleshooting ◽  
Complete Path

AbstractIn this industrial case study we describe a new network troubleshooting analysis used by VPC Reachability Analyzer, an SMT-based network reachability analysis and debugging tool. Our troubleshooting analysis uses a formal model of AWS Virtual Private Cloud (VPC) semantics to identify whether a destination is reachable from a source in a given VPC configuration. In the case where there is no feasible path, our analysis derives a blocked path: an infeasible but otherwise complete path that would be feasible if a corresponding set of VPC configuration settings were adjusted.Our blocked path analysis differs from other academic and commercial offerings that either rely on packet probing (e.g., tcptrace) or provide only partial paths terminating at the first component that rejects the packet. By providing a complete (but infeasible) path from the source to destination, we identify for a user all the configuration settings they will need to alter to admit that path (instead of requiring them to repeatedly re-run the analysis after making partial changes). This allows users to refine their query so that the blocked path is aligned with their intended network behavior before making any changes to their VPC configuration.

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