As cloud technologies and standards evolve sluggishly, concerns towards the server infrastructure, processing power and hosting virtualization use have become a top priority. The traditional cloud model has for a long time been the most popular model, providing people with expandable resources and remote space for data. Still, with the expansion of IoT comes the requirement for data to be processed as it comes in, hence the fog computing era has begun. Cloud and fog computing differ when compared to purpose, usage and performance levels.
What is Cloud Computing?
This allows the users to utilize a distributed computing model and allows the consumers to be able to have access to computing resources such as servers, storage, security databases, network. Embedded tasks are instead performed via virtual, remote resources stored within data centers owned and maintained by cloud service providers such as Amazon Web Services (AWS), Microsoft Azure, Google Cloud and others. Rates are built around what is called utility computing in Galaev.
Which describes the main cost of moving infrastructure to the cloud
The long payback period due to the absence of equipment that allows over-providing services. Cloud computing has several advantages. These include, Scalability: If the demand increases, or higher resources are required, cloud service providers can quickly increase the services Scaling if there is a decrease in demand. Cost Efficiency: Resources consumed are paid for by the users, hence holdings of much hardware is not necessary.
Accessibility: Internet-enabled data and applications can be accessed from any place in the world.
Maintenance: It is the responsibility of Cloud service providers to do the maintenance and updates of the hardware and software provided.
What is Fog Computing?
Fog computing or edge computing as it is sometimes called can be described as a distributed computing framework which brings cloud computing to the periphery of the network. In fog computing, data is processed in or near the network end-user devices (instead of moving the all the data to the cloud). Routers and gateways can include the local processing in a cloud oriented workflow.
Fog computing is essential in such applications because they require real-time processing and immediate responses with minimal latency. Common applications for fog computing are IoT devices, self-driving cars, smart cities, and industrial manufacturing.
Some benefits of fog computing include:
Less Time: Citing all data from a center increases the time it would take in such analysis and fog computing seeks to eliminate that by pragmatically analyzing the data where it is generated.
Save Bandwidth: Fog computing cuts down on the volume of data that needs to be moved to and from the cloud thus saving bandwidth.
Enhanced Security: There is the possibility of overseeing and sorting the data on the site without external data transfer thus protecting the relative sensitive data from traveling long distances.
Reliability: Localization of the process guarantees that the systems can be self-dependent even when there is a lack of access to the cloud.
Key Differences Between Cloud versus Fog Computing
Location of Data Processing:
Cloud Computing: All data is centralized and processed offsite in big data centres.
Fog Computing: Data processing is done at locations that are nearest to the information source, which could be the outer part of a dispersed network.
Latency:
Cloud Computing: Clouds are many miles away from the client which means it takes time to get data to the cloud and server it back again to the client which in turn causes high latency.
Fog Computing: Processing data that is being collected is conducted locally so fewer operations such as transmission to the cloud are needed lowering the latency.
Bandwidth Usage:
Cloud Computing: Bandwidth consumption is more in this case since use of the cloud always leads to downloads of data to the cloud.
Fog Computing: Use of Bandwidth is not excessive since little data processing is performed remotely but rather most of the information is processed within the system.
Application Scenarios:
Cloud Computing: This is appropriate for applications that would need very high scale up but do not need to face strict time delays such as storage of data, data analysis at high volumes, and web services.
Fog Computing: This works well for applications which need to be done in real time and respond with little or no time lag regarding requests made such as IoT systems. Self-driving cars. Automation in industry.
Conclusion
Although cloud computing continues to be an effective way to manage and process large amounts of data, fog computing provides a follow up for applications requiring immediate response and low latency. It is imperative for businesses, as well as developers to comprehend the divide between these two models while designing systems for their application needs. Depending on the requirements of a given application, the system can leverage the cloud’s power or fog computing.
