Still other examples are often aligned with utilities, such as water treatment or electricity generation, to ensure that equipment is functioning properly and to maintain the quality of output. Other examples involve predictive analytics that can guide equipment maintenance and repair before actual defects or failures occur.

Some examples include retail environments where video surveillance of the showroom floor might be combined with actual sales data to determine the most desirable product configuration or consumer demand. The idea of business intelligence can vary dramatically. Processing often involves normalizing and analyzing the data stream to look for business intelligence, and only the results of the analysis are sent back to the principal data center. In many cases, the computing gear is deployed in shielded or hardened enclosures to protect the gear from extremes of temperature, moisture and other environmental conditions. Although only 27% of respondents have already implemented edge computing technologies, 54% find the idea interesting.Įdge computing puts storage and servers where the data is, often requiring little more than a partial rack of gear to operate on the remote LAN to collect and process the data locally. The concept of edge computing isn't new, and it is rooted in decades-old ideas of remote computing - such as remote offices and branch offices - where it was more reliable and efficient to place computing resources at the desired location rather than rely on a single central location. The principle is straightforward: If you can't get the data closer to the data center, get the data center closer to the data. So IT architects have shifted focus from the central data center to the logical edge of the infrastructure - taking storage and computing resources from the data center and moving those resources to the point where the data is generated.

The prospect of moving so much data in situations that can often be time- or disruption-sensitive puts incredible strain on the global internet, which itself is often subject to congestion and disruption. Gartner predicted that by 2025, 75% of enterprise-generated data will be created outside of centralized data centers. This remains a proven and time-tested approach to client-server computing for most typical business applications.īut the number of devices connected to the internet, and the volume of data being produced by those devices and used by businesses, is growing far too quickly for traditional data center infrastructures to accommodate. Results of that work are then conveyed back to the client endpoint. That data is moved across a WAN such as the internet, through the corporate LAN, where the data is stored and worked upon by an enterprise application. In traditional enterprise computing, data is produced at a client endpoint, such as a user's computer.

Edge computing brings data processing closer to the data source.Įdge computing is all a matter of location. Take a comprehensive look at what edge computing is, how it works, the influence of the cloud, edge use cases, tradeoffs and implementation considerations. Thus, edge computing is reshaping IT and business computing. Only the result of that computing work at the edge, such as real-time business insights, equipment maintenance predictions or other actionable answers, is sent back to the main data center for review and other human interactions.

Rather than transmitting raw data to a central data center for processing and analysis, that work is instead performed where the data is actually generated - whether that's a retail store, a factory floor, a sprawling utility or across a smart city. In simplest terms, edge computing moves some portion of storage and compute resources out of the central data center and closer to the source of the data itself. Businesses are responding to these data challenges through the use of edge computing architecture. Bandwidth limitations, latency issues and unpredictable network disruptions can all conspire to impair such efforts. The traditional computing paradigm built on a centralized data center and everyday internet isn't well suited to moving endlessly growing rivers of real-world data. But this virtual flood of data is also changing the way businesses handle computing.

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