The Internet of Things (IoT) is poised to create the next big technological revolution. In the future, every device — whether it’s a fridge, a car or an oilfield drill rig — will be connected and talk to other devices.
One of the reasons why IoT has gained momentum in the recent past is the rise of cloud services. Though the concept of M2M existed for over a decade, organizations never tapped into the rich insights derived from the datasets generated by sensors and devices. Existing infrastructure was just not ready to deal with the massive scale demanded by the connected devices architecture. That’s where cloud becomes an invaluable resource for enterprises.
With abundant storage and ample computing power, cloud became an affordable extension to the enterprise data center. The adoption of cloud resulted in increased usage of Big Data platforms and analytics. Organizations are channelizing every bit of data generated from a variety of sources and devices to the cloud where it is stored, processed, and analyzed for deriving valuable insights. The combination of cloud and Big Data is the key enabler of Internet of Things. IoT is all set to become the killer use case for distributed computing and analytics.
Cloud service providers such as Amazon, Google, IBM, Microsoft, Salesforce, and Oracle are offering managed IoT platforms that deliver the entire IoT stack as a service. Customers can on-board devices, ingest data, define data processing pipelines that analyze streams in real-time, and derive insights from the sensor data. Cloud-based IoT platforms are examples of verticalized PaaS offerings, which are designed for a specific use case.
While cloud is a perfect match for the Internet of Things, not every IoT scenario can take advantage of it. Industrial IoT solutions demand low-latency ingestion and immediate processing of data. Organizations cannot afford the delay caused by the roundtrip between the devices layer and cloud-based IoT platforms. The solution demands instant processing of data streams with quick turnaround. For example, it may be too late before the IoT cloud shuts down an LPG refilling machine after detecting an unusual combination of pressure and temperature thresholds. Instead, the anomaly should be detected locally within milliseconds followed by an immediate action trigged by a rule. The other scenario that demands local processing is healthcare. Given the sensitivity of data, healthcare companies don’t want to stream critical data points generated by life-saving systems. That data needs to be processed locally not only for faster turnaround but also for anonymizing personally identifiable patient data.
The demand for distributing the IoT workloads between the local data center and cloud has resulted in an architectural pattern called Fog computing. Large enterprises dealing with industrial automation will have to deploy infrastructure within the data center that’s specifically designed for IoT. This infrastructure is a cluster of compute, storage, and networking resources delivering sufficient horsepower to deal with the IoT data locally. The cluster that lives on the edge is called the Fog layer. Fog computing mimics cloud capabilities within the edge location, while still taking advantage of the cloud for heavy lifting. Fog computing is to IoT what hybrid cloud is to enterprise IT. Both the architectures deliver best of both worlds.
Cisco is one of the early movers in the Fog computing market. The company is credited with coining the term even before IoT became a buzzword. Cisco positioned Fog as the layer to reduce the latency in hybrid cloud scenarios. With enterprises embracing converged infrastructure in data centers and cloud for distributed computing, Cisco had vested interest in pushing Fog to stay relevant in the data center. Almost after five years of evangelizing Fog computing with little success, Cisco finally found a legitimate use case in the form of IoT.
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