To understand the shift from cloud to fog and edge computing, let us briefly have a look at the basic concept underlying these computing technologies.
Cloud Computing
Cloud computing is basically management, processing, storage and computation of data by remote web hosted servers. This existed for years and gave birth to services such as Software-as-a-Service (SaaS) and Platform-as-a-Service (PaaS).
- Cloud replaced the need for large data centres open access to data and computing from various connected devices.
- It helped organisations saving huge resources on data management and in-house computing.
- Cloud helped the rise of enterprise services offering remote computing and software application as services through SaaS and PaaS.
Edge Computing
Edge computing can be referred as an offshoot of cloud that makes data processing available on the edge of a network or very close to the place of origin for the data. For example, a sensor generating a data can store it to allow easy access without depending on the network. It may or may not depend on the cloud server depending on the situation.
- Most of the times in closed Machine 2 Machine (M2M) and the Internet of Things (IOT) systems Edge Computing helps in solving the problem of data build-up.
- Businesses mainly make use of Edge computing for solving data build-up with solutions like data aggregation, data filtering, data scrubbing, denaturing, etc.
- Edge computing helps companies reduce the cost of making data available on the network.
- It also helps companies to maintain latency and network bandwidth.
Fog Computing
Fog computing works just as the Edge computing with the only difference in bigger capability in scaling up computation. Fog Computing do the data aggregation at the original source before the data reaches cloud or for other apps. With broader computational capacity it enables data processing and intelligence closer to the original source.
- Fog Computing besides enabling data aggregation and processing close to the original source can also scale up and spread computational tasks.
- It can spread data processing tasks like data packaging, real-time analytics, data filtering and data removal between a service and the edge of the network.
- The Fog computing helps to move the computation to where the data is created rather than moving just the stored data.
Broader application of Fog Computing
Thanks to Fog Computing we have a very flexible and seamless data processing environment that addresses several concerns at the same time. Fog computing is an answer to the latency, bandwidth and issues related to cost of data processing. Across many mission-critical applications where faster response time is crucial, the Fog computing in a Machine2Machine environment reliably deals with issues concerning latency.
As per the traditional means, all surveillance cameras and sensors used to stream the data to the cloud. This often caused latency problems with many systems in the workplace, industrial or public environments where faster feedback is often crucial for effectiveness. Storing and processing data close to the source thanks to Fog computing made many systems more efficient more than ever before.
A surveillance system that used to work in a centralised manner by accessing captured footage from various cameras in the cloud, can now direct individual cameras to track a missing person or a vehicle locally. This will distribute the load of computational tasks across many cameras and sensors in a city-wide surveillance system and thus will ensure greater efficiency and output.
The meeting point of mobility and Fog computing
Mobile app landscape is extremely getting diversified, multifaceted and spread across all conceivable niches. In this reality, latency in network-dependent apps will continue to be a major challenge. Besides, the apps of the future will be more demanding in terms of access to computational prowess. With the proliferation of apps and mobile activities is crossing all limits, a reliable device-independent data processing solution is something most connected apps will find helpful to work upon.
Lastly, as more and more devices will jump on the bandwagon of IOT based connected reality with the mobile apps still playing the central role, reliable data processing close to the source of data will be crucial for ensuring performance. Fog computing with its distributed data processing can answer to all these demanding situations arising out of mobile apps. It is quite obvious that mobile app development company future will depend more on a solution like Fog computing that relieves them from latency problem of cloud and storage constraints of devices.
Fog Computing opportunity is robust
According to 451 Research, the fog computing market will reach a whopping $18 billion capitalisation by 2022. Some of the most important reasons that propel this growth of Fog computing include the following.
- Fog computing will help to boost the business agility to a great extent. A rich reserve of tools will help to create a lot of fog apps and deploy them appropriately wherever needed.
- Fog computing can easily integrate with existing IT environments and mobile app landscape with the Fog nodes requiring same range of controls and procedures.
- Fog computing will also allow local analytics and easy cloud integration.
- Fog computing will also help saving network bandwidth by saving and processing data locally.
- The typical cloud control environment works well for building apps to work with Fog computing.
- Fog computing can hugely benefit large workplaces, industries and smart cities that need to process a lot of data and ensure low latency with local processing.
The whole connected reality we live in with a plethora of camera, sensors and gadgets need more processing capabilities every day. This is why the robust local computing solution of fog computing looks so promising and future-ready now.
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