Discuss about the Fog to Cloud Interfaces and Protocols.
Fog computing is an emerging software technology that helps distribution of files, documents and other storable materials in the online file hosting or cloud storage systems. Fog computing significantly increases the efficiency and accuracy of file distribution in the virtual storage interface.
In this proposal, fog computing is explored and a project plan has been prepared for implementation of fog computing in a cloud using system.
The main problem domain in this case is the increasing popularity of Internet of Things (IoT) worldwide. Due to the benefits, more and more people are installing IoT and hence, the stream traffic is increasing significantly. IoT uses cloud computing system for storing and processing the files and data of the users (Vaquero & Rodero-Merino, 2014). With increasing traffic, the storing and processing activities become significantly slower and inaccurate. There is a need of a dedicated software service that will increase the efficiency and speed of data distribution and processing in the cloud storage system.
The main purpose of fog computing is to increase the speed and efficiency of data distribution and processing in suitable cloud server that will help optimizing the services of the Internet of Things as well as securing necessary data of the users. The use of fog computing is justified by the following benefits (Stojmenovic & Wen, 2014).
One of the leading organizations in the field of development of fog computing is OpenFog Consortium. Hence, this organization can be chosen as the sponsor who will help in the development of fog computing system and connecting it to the available cloud storage server.
The main research questions that are to be answered during the course of this research are as follows.
Fog computing is a software data management service similar to cloud computing. Cloud is a virtual interface for processing and storage of data that will otherwise require physical storage system. Due to its wide range of benefits, many new technologies have been developing centering cloud computing system that also include Internet of Things (Bonomi et al., 2014). With increasing use of Internet of Things, the data management and storage by cloud have significantly reduced in efficiency and accuracy. Hence, the new fog computing system will be developed in order to reduce the traffic in the cloud and increase accuracy and efficiency of data management and storage. Moreover, fog can also be used to solve the technical and privacy risks faced by the cloud computing system. However, fog computing requires high degree of technical setup (Luan et al., 2015). Hence, for the project, the technical setup will have to be upgraded and a proper project plan will have to be prepared for analysis and implementation of fog computing system in the available cloud server setup.
The research method will follow standard literature review process as well as practical evaluation. For the literature review, works of different researchers on cloud and fog computing will be analyzed for understanding the basic requirements and the functions. For system development, the available technical setup will be upgraded and the proposed system will be developed and implemented.
Data collection will be done via two methods. The primary data will be collected from the literature survey that will provide the existing and optimized data. On the other hand, secondary data will be collected from system testing where certain values will be generated.
The main ethical issue in this case is the security of the personal data of the users. As the cloud computing system handles a huge amount of personal data of the users, it is to be ensured that these data and information do not get leaked or stolen.
The project will have to comply with the rules and guidelines set by the government for handling online data and softwares. In course of system development, no blocked or banned websites should be accessed and no downloads should be done from unreliable or unverified sources.
Data analysis will be conducted based on the comparison between primary and secondary data. This will help obtain the most accurate results and can be used for optimizing the developed system involving fog computing system.
The deliverables of the project are as follows.
Figure 1: WBS of the Project
(Source: Created by Author)
The main risk of fog computing is security and privacy risk. The ip address of the user can easily be spoofed in fog computing service and hence, unethical users may fake his ip to access personal information of other users and use them for unethical activities (Aazam & Huh, 2014). Hence, it is to be ensured that these types of activities do not happen in the future. Currently, there are no reliable techniques for the mitigation of the ip spoofing issue. Hence, another important aspect of this research will be to analyze the risks and solve the problems by inventing new risk mitigation techniques.
|
Task No. |
Task Name |
Duration |
|
1.0 |
Fog Computing Development Project |
86 days |
|
1.1 |
Project Planning Phase |
13 days |
|
1.1.1 |
Analysis of the Research Requirements |
3 days |
|
1.1.2 |
Development of Project Plan |
5 days |
|
1.1.3 |
Estimation of Time and Resources |
5 days |
|
1.2 |
Project Initiation Phase |
5 days |
|
1.2.1 |
Verification of Project Plan |
2 days |
|
1.2.2 |
Approval of Research Plan |
1 day |
|
1.2.3 |
Initiation of the Project |
2 days |
|
1.3 |
Project Execution Phase |
56 days |
|
1.3.1 |
Literature Review |
15 days |
|
1.3.2 |
Collection of Primary Data |
5 days |
|
1.3.3 |
Analysis of Primary Data |
10 days |
|
1.3.4 |
Procurement of Technical Resources |
3 days |
|
1.3.5 |
Development of System Planning |
5 days |
|
1.3.6 |
Development of the Proposed System |
20 days |
|
1.3.7 |
Implementation of Fog Computing System |
15 days |
|
1.3.8 |
Connecting the System with Cloud Storage Server |
5 days |
|
1.3.9 |
Collection of Secondary Data |
5 days |
|
1.3.10 |
Analysis of Secondary Data |
8 days |
|
1.4 |
Project Closing Phase |
25 days |
|
1.4.1 |
Analysis of Primary and Secondary Data |
15 days |
|
1.4.2 |
Development of System Protocol |
10 days |
|
1.4.3 |
Development of System Security |
6 days |
|
1.4.4 |
Final Testing and Analysis |
3 days |
|
1.4.5 |
Risk Analysis |
5 days |
|
1.4.6 |
Risk Mitigation |
5 days |
|
1.4.7 |
Project Completion |
2 days |
|
1.4.8 |
Stakeholder Signoff |
1 day |
Figure 2: Gantt Chart of the Project
(Source: Created by Author)
Conclusion
In this proposal, a project plan has been prepared for the analysis and development of fog computing system. In this proposal, the development plan has been described and the work plan for the project has been developed for estimating the phases of the project as well as the overall time limit within which, the project will be completed.
References
Aazam, M., & Huh, E. N. (2014, August). Fog computing and smart gateway based communication for cloud of things. In Future Internet of Things and Cloud (FiCloud), 2014 International Conference on (pp. 464-470). IEEE.
Bonomi, F., Milito, R., Natarajan, P., & Zhu, J. (2014). Fog computing: A platform for internet of things and analytics. In Big Data and Internet of Things: A Roadmap for Smart Environments (pp. 169-186). Springer International Publishing.
Chen, N., Chen, Y., Song, S., Huang, C. T., & Ye, X. (2016, October). Smart Urban Surveillance Using Fog Computing. In Edge Computing (SEC), IEEE/ACM Symposium on (pp. 95-96). IEEE.
Dastjerdi, A. V., Gupta, H., Calheiros, R. N., Ghosh, S. K., & Buyya, R. (2016). Fog computing: Principles, architectures, and applications. arXiv preprint arXiv:1601.02752.
Luan, T. H., Gao, L., Li, Z., Xiang, Y., Wei, G., & Sun, L. (2015). Fog computing: Focusing on mobile users at the edge. arXiv preprint arXiv:1502.01815.
Madsen, H., Burtschy, B., Albeanu, G., & Popentiu-Vladicescu, F. L. (2013, July). Reliability in the utility computing era: Towards reliable fog computing. In Systems, Signals and Image Processing (IWSSIP), 2013 20th International Conference on (pp. 43-46). IEEE.
Mahmud, R., & Buyya, R. (2016). Fog Computing: A Taxonomy, Survey and Future Directions. arXiv preprint arXiv:1611.05539.
Park, S., & Yoo, Y. (2017). Network Intelligence Based on Network State Information for Connected Vehicles Utilizing Fog Computing. Mobile Information Systems, 2017.
Peng, M., Yan, S., Zhang, K., & Wang, C. (2016). Fog-computing-based radio access networks: issues and challenges. IEEE Network, 30(4), 46-53.
Stojmenovic, I., & Wen, S. (2014, September). The fog computing paradigm: Scenarios and security issues. In Computer Science and Information Systems (FedCSIS), 2014 Federated Conference on (pp. 1-8). IEEE.
Vaquero, L. M., & Rodero-Merino, L. (2014). Finding your way in the fog: Towards a comprehensive definition of fog computing. ACM SIGCOMM Computer Communication Review, 44(5), 27-32.
Yannuzzi, M., Milito, R., Serral-Gracià, R., Montero, D., & Nemirovsky, M. (2014, December). Key ingredients in an IoT recipe: Fog Computing, Cloud computing, and more Fog Computing. In Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), 2014 IEEE 19th International Workshop on (pp. 325-329). IEEE.
Yi, S., Li, C., & Li, Q. (2015, June). A survey of fog computing: concepts, applications and issues. In Proceedings of the 2015 Workshop on Mobile Big Data (pp. 37-42). ACM.
Zhu, J., Chan, D. S., Prabhu, M. S., Natarajan, P., Hu, H., & Bonomi, F. (2013, March). Improving web sites performance using edge servers in fog computing architecture. In Service Oriented System Engineering (SOSE), 2013 IEEE 7th International Symposium on (pp. 320-323). IEEE.
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