The relatively new approach to IT infrastructure design and data processing complements cloud computing to deliver key information to end users faster.
Your IT infrastructure likely spans on-premises and cloud environments. Within that infrastructure, have you considered where it makes the most sense to process data?
It’s a key decision that impacts how quickly your end users can access the data they need to collaborate with each other and communicate with customers. To decrease latency, many businesses moved their database, analysis and reporting applications to the cloud. This gives end users anytime, anywhere access. But with the Internet of Things (IoT) integrating with big data technologies—such as artificial intelligence—there’s now a lot more strain placed on cloud infrastructures.
Enter fog computing, which extends computing capabilities to the edge of enterprise networks so businesses can streamline storage access and networking services between devices and cloud data centers. Bringing computing capabilities closer to devices allows data to be processed more efficiently compared to process flows that send data to the cloud for processing and then back to end-user devices.
In addition to resolving latency problems, the fog computing paradigm can address security, network reliability, performance, and privacy issues that are sometimes difficult to manage in a cloud model. This does not mean cloud computing is going away; fog computing simply enhances cloud infrastructures, which are still critical to business functions and disaster recovery strategies.
As end users and customers demand more computing firepower, fog computing offers a way for businesses to better leverage their technologies by decentralizing data analytics. Here are a few of the key benefits:
- Permits end users to access smaller, specific data sets instead of receiving additional data they never need to access.
- Utilizes a more organized approach to retrieve relevant data so analytics can be performed in near real-time.
- Provides low-latency connections between end-user devices and analytics endpoints with the required bandwidth compared to data traveling back-and-forth to cloud data centers for processing.
- Enables data processing when end users do not have a sufficient bandwidth connection to a data center.
- Allows IT to deploy security and compliance policies via segmented network traffic and virtual firewalls.
The end result is that data can be delivered securely to the people who need it much faster. Because fog computing extends cloud computing to the network edge, it’s ideal for IoT networks and other applications that require real-time interactions. The network fabric stretches from the outer edges of where data is created to where it will eventually be stored, whether that's in the cloud or in an on-premises data center.
Fog Computing Use Cases
There’s a host of fog computing use cases for real-time analytics—from manufacturing systems that need to react to assembly-line machine events as they happen, to financial institutions that use real-time data to inform traders and credit card companies that monitor for fraud. Another environment where fog computing can accelerate data processing is traffic congestion. Cities and states can connect data from roadside sensors and vehicles to analyze traffic patterns in real time in order to redirect traffic and alleviate congestion.
And with access to real-time data on the status of packages in transit, shipping firms can use fog computing to better manage drone deliveries. Video surveillance can also benefit from fog computing. Think of all the cameras now deployed by private businesses and the government sector, generating massive amounts of data. If that data is processed near the edge of an IoT network, it will allow law enforcement agencies to react more quickly to incidents impacting public safety. This contrasts with traditional cloud-based models that are no longer adequate to analyze video data due to latency, network availability, and the cost to transmit data to the cloud and back.
Fog computing can also play a key role as mobile carriers roll out 5G networks, which will require dense antenna deployments generating much larger amounts of data than 4G networks. A fog computing architecture can help manage the applications running on a 5G network and process connections to cloud data centers to ensure mobile subscribers experience clear connections and ample bandwidth.
The Payoff: Helping Your End Users Work More Effectively
If your company is ready to explore fog computing, a great resource is the OpenFog Consortium. Their mission is to drive industry and academic leadership in fog computing and networking architecture, testbed development, and interoperability and composability deliverables that seamlessly bridge the cloud-to-things continuum.
Based on the potential explosive growth of IoT networks, giving serious consideration to fog computing is key. Your business will need to consider the sustainability of your current IT architecture; otherwise, IoT deployments may run into issues that cannot be addressed by cloud-only models. These include latency, network bandwidth, reliability and security.
Fog computing helps take on these challenges by adding a hierarchy of data computing elements between the cloud and your endpoint devices as well as between your devices and gateways. And that pays off by giving your end users the information they need to work effectively with each other and your customers.