Fog Computing PDF

FOG Computing What is Fog Computing? Fog Computing is the term introduced by Cisco that refers to extending cloud computing to an edge of the enterprise’s network. Thus, it is also known as Edge Computing or Fogging. It facilitates the operation of computing, storage, and networking ser...

FOG Computing What is Fog Computing? Fog Computing is the term introduced by Cisco that refers to extending cloud computing to an edge of the enterprise’s network. Thus, it is also known as Edge Computing or Fogging. It facilitates the operation of computing, storage, and networking services between end devices and computing data centers Fog Computing The solution to the challenges mentioned in the previous section is to distribute data management throughout the IoT system, as close to the edge of the IP network as possible. The best-known of edge services in IoT is fog computing. Any device with computing, storage, and network connectivity can be a fog node. Examples include industrial controllers, switches, routers, embedded servers, and IoT gateways. Analyzing IoT data close to where it is collected minimizes latency, offloads gigabytes of network traffic from the core network, and keeps sensitive data inside the local network History of Fog Computing The term fog computing was coined by Cisco in January 2014. This was because fog is referred to as clouds that are close to the ground in the same way fog computing was related to the nodes which are present near the nodes somewhere in between the host and the cloud. It was intended to bring the computational capabilities of the system close to the host machine. After this gained a little popularity, IBM, in 2015, coined a similar term called “Edge Computing”. Types of Fog Computing Device-level Fog Computing: Device-level fog computing utilizes low-power technology, including sensors, switches, and routers. It can be used to collect data from these devices and upload it to the cloud for analysis. Edge-level Fog Computing: Edge-level fog computing utilizes network-connected servers or appliances. These devices can be used to process data before it is uploaded to the cloud. Gateway-level Fog Computing: Fog computing at the gateway level uses devices to connect the edge to the cloud. These devices can be used to control traffic and send only relevant data to the cloud. Cloud-level Fog Computing: Cloud-level fog computing uses cloud-based servers or appliances. These devices can be used to process data before it is sent to end users. Components of Fog Computing Edge devices: Edge devices are the network devices nearest to the data source. Edge devices consist of sensors, PLCs (programmable logic controllers), and gateway routers. Data Processing: Data processing occurs locally on edge devices rather than being routed to a central location for processing. The end effect is greater performance and lower latency. Data Storage: Instead of transferring data to a central place, edge devices can keep information locally. This increases security and privacy while lowering latency. Connectivity: For fog computing to work, edge devices must be connected to the rest of the network at high speeds. This can be done using wired or wireless methods. When to Use Fog Computing? It is used when only selected data is required to send to the cloud. This selected data is chosen for long-term storage and is less frequently accessed by the host. It is used when the data should be analyzed within a fraction of seconds i.e Latency should be low. It is used whenever a large number of services need to be provided over a large area at different geographical locations. Devices that are subjected to rigorous computations and processing must use fog computing. Real-world examples where fog computing is used are in IoT devices Devices with Sensors, Cameras (IIoT-Industrial Internet of Things), etc. Advantages of Fog Computing This approach reduces the amount of data that needs to be sent to the cloud. Since the distance to be traveled by the data is reduced, it results in saving network bandwidth. Reduces the response time of the system. It improves the overall security of the system as the data resides close to the host. It provides better privacy as industries can perform analysis on their data locally. Disadvantages of Fog Computing Congestion may occur between the host and the fog node due to increased traffic (heavy data flow). Power consumption increases when another layer is placed between the host and the cloud. Scheduling tasks between host and fog nodes along with fog nodes and the cloud is difficult. Data management becomes tedious as along with the data stored and computed, the transmission of data involves encryption-decryption too which in turn release data. Applications of Fog Computing It can be used to monitor and analyze the patients’ condition. In case of emergency, doctors can be alerted. It can be used for real-time rail monitoring as for high-speed trains we want as little latency as possible. It can be used for gas and oils pipeline optimization. It generates a huge amount of data and it is inefficient to store all data into the cloud for analysis.  Edge Computing Fog Computing ▪ Less scalable than fog computing. ▪ Highly scalable when compared to edge computing. ▪ Millions of nodes are present. ▪ Billions of nodes are present. ▪ Nodes in this computing are installed closer to the cloud(remote ▪ Nodes are installed far away from the cloud. database where data is stored). ▪ Edge computing is a subdivision of fog computing. ▪ Fog computing is a subdivision of cloud computing. ▪ The bandwidth requirement is very low. Because data comes ▪ The bandwidth requirement is high. Data originating from edge from the edge nodes themselves. nodes is transferred to the cloud. ▪ Operational cost is higher. ▪ Operational cost is comparatively lower. ▪ High privacy. Attacks on data are very low. ▪ The probability of data attacks is higher. ▪ Edge devices are the inclusion of the IoT devices or client’s ▪ Fog is an extended layer of cloud. network. ▪ The power consumption of nodes filter important information ▪ The power consumption of nodes is low. from the massive amount of data collected from the device and saves it in the filter high. ▪ Fog computing helps in filtering important information from the ▪ Edge computing helps devices to get faster results by massive amount of data collected from the device and saves it processing the data simultaneously received from the devices. in the cloud by sending the filtered data. Conclusion Finally, fog computing delivers cloud capabilities to the edge of networks, increasing efficiency, lowering latency, and improving data processing capabilities. It is perfect for real-time data analysis, low-latency applications such as IoT, and situations where data privacy and security are critical. While it provides scalability and lower bandwidth usage, it also has issues in managing data congestion and increasing power consumption. Fog computing is making progress in applications such as healthcare monitoring, industrial IoT, and real-time analytics across a variety of industries.