Enterprise IoT is among the hottest technology trends that CIOs are working hard to stay in sync with. Most enterprises are still at the beginner stage of IoT adoption or workplace productivity and quality-of-work-life improvement.
As IoT technologies improve and become financially viable for enterprises, CIOs and data officers will need to come to terms with the fact that more devices means more data. A question that logically follows: Are current cloud storage models capable of handling this much data without compromising the ease of access and real time updates?
The problem of too much data, too far in the cloud
It’s true that IoT can, and will, generate unprecedented volumes of data, which will put a lot of strain on most enterprise Internet infrastructures. Companies need to find ways to meet this challenge and solve the problem of data management.
Of course, cloud computing is capable of handling large data volumes, but it’s yet to be exposed to the massive data that an IoT-heavy enterprise will generate every day. This is where fog computing comes to the fore. Fog computing? Is that San Francisco-based with all that fog in that city? No! We’ve got more on this; read on.
Cloud and edge model limitations for IoT applications
Backed by focused high-velocity data transfer requirements in businesses, and tremendous growth potential in conjugation with other technologies such as Big Data and IoT, fog computing has soared in the past few years.
For technology innovators in the 5G, IoT, artificial intelligence, and virtual reality space, fog computing is vehicle for progress. This is directly linked to the challenges that conventional cloud storage and computing models pose.
Some of these are latency in data access, causing less than optimum performance, issues around limitations of network bandwidth, challenges around data privacy and security, and concern around reliability. Edge computing with intelligent endpoints is an alternative, but it suffers from issues such as energy and space constraints, environmental issues, modularity constraints, and security issues.
Fog computing, thankfully, addresses most of these constraints, and enables the achievement of scalable, dynamic, and effective IoT ecosystems.
Fog computing vs. cloud computing
This table succinctly captures the difference in cloud and fog computing, evaluated on major parameters.
In conventional cloud architecture, even the smallest data element is sent to a central processing and computing engine via edge node devices. This, of course, causes latency.
On the other hand, fog computing works by empowering edge node devices to carry out some level of local data processing, resource pooling, cache data management, load balancing, dense geographic distribution, latency reduction, and local device management. This directly results in better quality of service and enhanced end user experiences.
How does fog computing solve the problem for IoT?
Fog computing reduces the gap between the cloud (remote data and computing power resources) and the things (the gadgets that are a part of your enterprise IoT). A fog computing model takes communications, control, computing, analysis, and of course, storage, much closer to where the data is originated. This enables significantly lower processing times and eventually lower network costs, which are critical success enablers for IoT ecosystems.
Technically, fog computing is best envisaged as an extension of conventional cloud computing; here, implementations of the cloud architecture reside in several layers of the network topology.
Fog node layers can be added to the architecture and used to make applications run at optimized network levels. Particularly in applications that require processing times less than a few milliseconds, fog computing becomes a salient solution. Just like the solution Alejandro and Matt had in “Sicario” to cut the head off of the snake! Get with the program, Kate! Your method was not working! OK, back on topic!
Because most IoT gadgets depend on such superfast data processing speeds for effective operations, the marriage of IoT and fog computing is inevitable. Virtual reality applications, emergency response systems, and autonomous drones – all are perfect examples of IoT applications heavily dependent on the kind of fast data access that fog computing can enable.
The core ‘smart’ factor of fog computing
Almost all benefits brought by fog computing in an IoT heavy ecosystem is because of its ability to bifurcate the data coming from IoT devices on the basis of how important “timing” is for them. Time sensitive data, such as measurement of critical performance parameters, triggering of alarms, making course corrections, device statuses, fault warnings, etc. are locally analyzed in edge node devices, which results in lower latency.
Data that’s not too critical in terms of timing is relayed to the central computing engine and mainframe to be retrieved later. Such data include reports, logs, files, and so forth.
Fog computing leverages basic cloud computing mechanisms and adds another layer of edge node device-based processing to deliver tremendous advantages. Here are a few of them, particularly relevant for IoT:
- With a globally distributed network, enterprises can ensure close to zero downtime using fog computing.
- Load balancing enables more effective utilization of existing network and cloud resources.
- Maximum network bandwidth utilization helps keep network costs in check, and reduced payback periods.
- The agility that fog computing-powered IoT brings to a business is unprecedented, with automated intelligent decisions being made in split seconds. That is a little sharper than Napoleon Dynamite or Anthony Weiner!
- Reduction in latency in serving data requests, enabling seamless and interruption free operations of critical IoT devices.
- Overall improvement in end user experiences and quality of service.
Fog computing is no eyewash
It is estimated that in 2015, close to 570 million IoT devices owned by enterprises and governments used fog computing. In its report “Edge Computing in the IoT”, BI Intelligence estimates that by 2020, this number will multiple several times and reach up to 5.6 billion devices. Make no mistake, fog computing is no fancy name for a technology that’s almost the same as something already in place.
Fog computing brings in focused improvements in data processing via cloud, and makes it possible for enterprises to leverage the power of other technologies, tools, and platforms that are time-sensitive in their requirements of processing and storage resources.
From smart cities to remote oil and energy extractions, from smart traffic management to virtual reality, from environmental conservation to the next level of intelligent lighting in workplaces – fog computing has the makings of a core engine that can drive the IoT train at top speeds, and it will not be out of control like the train in “Unstoppable.”
Photo credit: Flickr / Ler
