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Fog and edge Project Report
Student’s Name
Instructor’s Name
Institutional affiliation
Course name and number
Due Date
Fog and edge Project Report
Abstract
Internet architecture for future new technical techniques in the 5G network is provided by the FOG computing architecture. It is possible to enhance data distribution services by using modern technologies. The dynamic topic of computing network technology and information is explored in further detail. To improve communication, the data transfer employs a novel technical technique.
Introduction and Methodology
Cloud technology on a 5Gigabit ethernet provides a platform for FOG environment development. Flexible software architecture can be implemented using a variety of architectural options. Fog nodes, the cloud server at the back end, and client devices can achieve this. As a result, the underlying hardware resources can be improved5G networks must handle data from billions of end-user devices using fog computing architecture. To create complex offload processing policies, devices incorporate a layer for supplying and hosting computational storage. To describe end-user devices linked to the fog network, there is a device layer. Devices such as mobile devices (eMBB), gateways, sensors, and actuators are used in IoT. The data is transmitted to the network in order to execute data transmission. This layer runs software that incorporates embedded coding for computing activities (Aggarwal and Kumar, 2019). As part of edge computing’s architectural hierarchy, the cloud layer provides a way to handle computationally intensive operations with massive storage. A Based Band Unit is included for processing data that goes from one truck to another.
SaaS (Software as a Service) cloud computing will be utilized to supply end customers with an application ready for usage. In order to accomplish this, patches and updates must be applied to a range of devices (Xu, Qian, and Hu, 2018). An end-user application is required to access the cloud-based apps. Browsers may then access the same user dashboard.
Edge computing involves transferring data to a remote cloud or other centralized systems, whereas local processing does not require this. When data is transmitted to centralized sources across shorter distances, it is processed more rapidly, which helps applications and services at the edge.
This standard uses fog computing to specify how edge computing should work and allows computation, storage, and networking services between end-devices and cloud computing data centers. Defensive computing is another area where fog is used as a starting off point.
Application requirements
Cloud computing may be used to run business software such as Bitrix and Quickbooks. Quickbook is an online accounting solution for businesses that use the Quickbook language. Management, social collaboration, and communication capabilities are available through Bitrix. Using an internet connection, data storage apps may be used to store information, and they can also be used for retrieving information. It enables data backup and retrieval. Applications like Box and Google Suite may be used for data storage and backups (Kumari et al., 2019). High-speed gadgets can benefit from the usage of a 5G network, which can improve their performance. Using Box’s secure online environment, you can manage material, collaborate, and work on projects. Various files can be stored on the cloud platform as a result of this. In addition, it allows for the integration of services through the use of “drop and drag” services. In terms of backup and online storage, Google Suite is the most acceptable option out there. Among the cloud apps that may be managed are calendars, forms, and hangouts.
Data is gathered or processed at the edge if it remains on the sensor device or IoT Gateway. Computing capability will be added in the future when it comes to edge computing. It is precious when the data must be used to modify machine behavior in real-time. Fog computing occurs when computing is spread, and the data processing capability is left in the local network where the sensor device is placed. Data from this source is used for parameterization changes between orders given to production lines and a variety of other applications where a very low latency on vast volumes of information is critical.
Connecting devices to the cloud to handle their data centrally creates new problems. The migration of all data to the cloud isn’t always necessary, and in some situations, reaction speed is essential. Edge computing and fog computing are two significant approaches to distribute compute resources in these situations. In addition, complex solutions like those have hidden technological costs, and a few use cases can help you evaluate the practicality of edge computing in your organization.
Fuzzing and edge programming have their advantages since they make use of private provider networks. High-capacity storage and processing are possible with fog nodes. The Fog nodes feature a method for delivering queries to the cloud for centralized processing designed to be faster. For critical services and applications, latency may be reduced to a minimum (Gupta et al., 2017). To centralize storage and processing, fog nodes can send data to the cloud for storage and processing. Local IoT processing is ensured by this architecture in addition to processing the information locally, and fog node data can be transmitted to it. Data may be transferred to the cloud for processing, which is its major utility. In addition, this decreases network latency, resulting in faster reaction times and a more positive user experience. Sensitive data is better protected using this technology, and the data may be handled on a local level. For extra analytics, it is a subset of data. Devices connected to the Internet of Things are a logical application for edge computing. Machine, component, or device remote sensors create huge quantities of data. It takes a lot more time to evaluate, log, and track data if it has to be transmitted back over an extended network link. Applications may be brought closer to local edge servers or Internet of Things (IoT) devices with this platform. Insights are gained more quickly, reaction times are shortened, and bandwidth is increased. As a result of the exponential growth of IoT devices and their increasing computing power, unprecedented amounts of data have been created and transferred. Additional data volume growth will be driven by an increase in the number of mobile devices connected to 5G networks.
Cloud computing and artificial intelligence (A.I.) were previously expected to aid automate and speed up innovation by providing actionable insights from data. Data generated by connected devices is so large and complicated that the network infrastructure can no longer handle it. There are issues about sentence structure and latency when devices send all of their information to the cloud or a centralized data center. Edge computing, in which data is processed and analyzed closer to the point of origin, is a more efficient choice. Considering that data does not need to be transmitted across a network and processed in a cloud or data center, latency has been significantly lowered. With edge computing, faster and more extensive data processing may be achieved – including mobile edge computing on 5G networks.
Evaluation
Combining technology with fog and edge programming has its advantages. IoT devices are connected to it, which may be utilized to access a remote cloud for offering certain services. In order to ensure centralized cloud design, it contains a larger volume of real-time data. As a result, hybrid design is encouraged. It enables real-time data processing and data collecting. It utilizes fog computing and edge computing to analyze data in real-time and modify network traffic. Using fog and the edge as enablers gives both consumers of IoT devices and technology suppliers additional choices. Since cloud servers are no longer confined to a single location, IoT services may be dispersed and offered greater flexibility.
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References
Aggarwal, S. and Kumar, N., 2019. Fog computing for 5G-enabled tactile Internet: Research issues, challenges, and future research directions. Mobile Networks and Applications, pp.1-28.
Kumari, A., Tanwar, S., Tyagi, S., Kumar, N., Obaidat, M.S. andRodrigues, J.J., 2019. Fog computing for smart grid systems in the 5Genvironment: Challenges and solutions. IEEE wireless communications, 26(3), pp.47-53.
Gupta, H., Chakraborty, S., Ghosh, S.K. and Buyya, R., 2017. Fog computing in 5G networks: an application perspective. Cloud and FogComputing in 5G Mobile Networks: Emerging advances and applications,pp. 23-56.
Xu, S., Qian, Y. and Hu, R.Q., 2018, December. Privacy-preserving data preprocessing for fog computing in 5G network security. In 2018IEEE Global Communications Conference(GLOBECOM) (pp. 1-6). IEEE.
Appendix
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