Changing analytics landscape with the rise of edge and fog computing
Gartner predicts earlier that during 2022, 75% of enterprise-generated data will be created and processed outside a centralized data center, “lake” or cloud.[1] DataAge 2025 study also reported that by 2025, 20 percent of all data created will be real-time in nature that would no longer require sending it to the core of the network, to the cloud as raw data for processing.[2] According to Gartner edge computing and analytics capabilities will be further enhanced by new AI tiny chips, 5G technologies and advanced processing.
An interesting example where edge analytics was used is the double-engine boat “SilverHook” with a speed of 200 miles/hour. The data generated was received and instantly analyzed to assess the boat’s performance during the race. An algorithm generates data to be returned to the crew in seconds. The challenge is that the double powerful engine must operate at its highest level without overheating while increasing the chances to beat world records. The real-time feedback allows the release of pressure just at the right time based on real-time analytics embedded in the boat navigation system.[3]
In this changing analytics landscape, there is a shift from cloud analytics to edge analytics to address latency as well as security issues.[4] With an unprecedented growth of already voluminous data, the time that it will take for data to transit from a IoT device to “negotiate” a complex network topology at the cloud, it could be too lengthy for effective device management at the edges. By the time data reaches the cloud for analysis, the opportunity to act on it may be missed, especially in remote locations.[5]
For data to travel from the edge, where it is generated to a cloud provider and back, it may take between 150 to 200 milliseconds, while at the edges it may only take between 2 and 5 milliseconds for data to transit back-and-forth locally. The implications can be enormous, when dealing of image recognition or signal processing to assess the quality of products and improve processes and overall productivity. [6]
[1] Rob van der Meulen (2018) What Edge Computing Means for Infrastructure and Operations Leaders
[2] Raghavan Srinivasan (2018) Edge Computing and the Future of the Data Center, Seagate Tech.
[3] Bastien L (2016) Analytics on the Edge : le futur du Big Data
[4] Sahni, Y et al. (2017) Edge mesh : a new paradigm to enable distributed intelligence in internet of things IEEE access, 2017, v. 5, p. 16441–16458)
[5] Sahni, Y et al. (2017) Edge mesh : a new paradigm to enable distributed intelligence in internet of things IEEE access, 2017, v. 5, p. 16441–16458)
[6] CISCO (2015) Fog Computing and the Internet of Things: Extend the Cloud to Where the Things Are
