By Jill King | March 14, 2019 | edge computing
In a variety of industries, such as healthcare, smart retail outlets, manufacturing, agricultural, financial, and many other, trends in data collection, analysis, storage, and retrieval, have come together to make the Edge Computing one of the highest value propositions in recent years for many enterprises. However, the path to an Edge strategy that is both efficient and cost effective is often not easily found. IT organizations tasked with processing the influx of time-critical data generated by connected devices and applications at the edge are running into the challenge executing a solution that won’t increase network loads or limit the response to real-time insights.
In an effort to tackle these challenges head-on, edge computing has become a bigger part of the conversation, as enterprises begin to deal with the massive data volumes being produced by these devices. Along with the dramatic improvements in the technology and devices used to collect and process data at the edge, the use-cases justifying an edge computing strategy are growing by the number. This new IT avenue of edge analysis is even now providing businesses with real-time insights, researchers with a wider range of data to analyze, and those with manufacturing or other physical assets the ability to maintain and deploy faster, more efficiently, and cost effectively.
For any business looking to embark on an effective edge strategy, there is more to think about than the devices and sensors generating data at the edge. With the rapid increase of connected devices in recent years, the tens of thousands of new/potential edge node sites have become one of the primary challenges facing IT organizations. It is no surprise that maintaining these servers has become burdensome, slow, and expensive, especially in environments with low bandwidth connections or remote locations where infrastructure is lacking.
In industries such as oil and gas, retail, and manufacturing, where a business may have data streams coming in from multiple collection points at a variety of locations, the ability to mesh the data collected at one location with that of the others is a critical step in any edge computing strategy. This means having a singular way to manage multiple nodes at the edge−no small ask in areas with limited connectivity and high latency. Thankfully, the solution is more familiar than you might think. Bright Edge allows IoT users to deploy nodes at the edge, in a variety of geographical areas, and manage those over their lifetime from a single cluster. With Bright Edge, users can also add nodes at edge locations, via one change on the cluster and be free from the burden of on-site management at multiple locations. This means no more need to send an IT team to each site-users get all the benefits of Bright Cluster Manager across the entire IoT and edge infrastructure.
With Bright Edge, edge nodes can run any supported HPC scheduler, adding flexibility and ease of implementation. With this innovative IoT solution, it is possible to manage resources spanning multiple locations as a single cluster, and with edge nodes largely controlled from the local edge director, this frees IT to work on adding to IoT value instead of maintaining a fleet manually. IoT investments should be about extracting value and maximizing investments in local devices and analytics that can power on-site understanding of operations, not wasting man-power managing infrastructure at distributed locations. With Bright Edge, IT managers shift from great complexity to extreme simplicity with ease, all with the ability to add more nodes and capabilities without significant overhead.
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