Trend 6: Global Digitization: Impact of IoE Potential Effects of IoE on Global Data Centers Cloud services are accelerated in part by the unprecedented amounts of data being generated by not only people but also machines and things. Cisco GCI estimates that 600 ZB will be generated by all people, machines, and things by 2020, up from 145 ZB generated in 2015. Figure 26 shows examples of the amounts of data that will be generated by planes, automobiles, and buildings, among other things and systems. Figure 26. Smart City: Multiple Applications Create Big Data What Makes a Smart City? Multiple Applications Create Big Data Connected Plane 40 TB per day (0.1% transmtted) Connected Factory 1 PB per day (0.2% transmitted) Public Safety 50 PB per day (<0.1% transmitted) Weather Sensors 10 MB per day (5% transmitted) Intelligent Building 275 GB per day (1% transmitted) Smart Hospital 5 TB per day (0.1% transmitted) A city of one million will generate 200 million gigabytes of data per day by 2020 Smart Car 70 GB per day (0.1% transmitted) Smart Grid 5 GB per day (1% transmitted) Source: Cisco Global Cloud Index, 2015–2020 Most of the more than 600 ZB that will be generated by 2020 will be ephemeral in nature and will be neither saved nor stored. Much of this ephemeral data is not useful to save, but we estimate that approximately 10 percent is useful, which means that there will be 10 times more useful data being created (60 ZB, 10 percent of the 600 total) than will be used (6 ZB) in 2020. Edge or fog computing might help bridge this gap. M2M Data Analytic Requirements Drive Fog/Cloud Computing Growth in machine-to-machine connections and applications is also driving new data analytics needs. Although not all M2M applications promote a lot of traffic, the sheer number of these connections is capable of delivering intelligent, actionable information if the data from them can be analyzed. Figure 27 maps several of such M2M applications for their complexity of protocol environment and data analytic needs. IoT applications have very different characteristics. In some cases application analytics and management can occur at the edge device level whereas for others it is more appropriately handled centrally, typically hosted in the cloud. The opportunity for fog propositions (distributed intelligence) and the associated intelligent gateways is the strongest in markets that meet two conditions. First, their focus of data analytics is on the socalled aggregation level, and, second, they are subject to a problematic degree of protocol complexity. Especially the first aspect is set to be decisive in determining the demand for intelligent gateways as a product segment. In general, such markets reside in the Industrial IoT domain – involving verticals such as manufacturing, extractive industries, and healthcare. Applications such as smart metering can benefit from real-time analytics of aggregated data that can optimize the usage of resources such as electricity, gas, and water. Local level analytics is suited for those applications that require the data to be stored and analyzed locally due to either regulatory reasons or because the cost of transportation of the data upstream and associated wait-time for analysis is prohibitive. A key issue for IoT in the coming years is subsidiarity, i.e. performing the data analysis at the appropriate level. In most cases, there will be a blend of approaches and the functionality to manage local as well as central application management will be increasingly critical. Cisco Global Cloud Index: Forecast and Methodology, 2015–2
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