AS Prasad, Vertiv

Simplifying deployment and management of network edge

Simply put, edge computing brings the physical location of cloud servers closer to the devices with which they interact in order to reduce delays and improve the quality of service. This allows the processing of data near the edge of the network — by the device itself or by a local computer or server — rather than data being transmitted back to a core data centre.

This structure enables accelerated and real-time data processing that allows the smart applications and devices to respond almost instantaneously for reduced latency.

As automation practices continue to evolve, and business expectations continue to rise, there are vast amounts of data being generated by employees and machines. The main technological problem companies face is the timely processing and analysis of that generated data. Fortunately, the growth of edge computing, coupled with the implementation of traditional industrial control system architecture, provides increasing levels of operational flexibility.

To address this influx of data and keep pace with the ongoing digital transformation, we need to enable the edge through standardization that streamlines infrastructure deployment. In other words, we must simplify the edge.

While customers enjoy the benefits of edge computing, managing these networks involves facing tremendous challenges related to bandwidth, availability and security. These network edge deployments are becoming mission-critical facilities with similar IT infrastructure and support requirements as a traditional data centre. What makes them different is that they are scattered around the globe and typically lack any sort of on-site IT personnel.

The opportunity around edge computing is substantial as is the complexity, which is detailed in the following examples:

* In cases that are data intensive, layers of storage and computing are required between the endpoint and the cloud to reduce bandwidth costs or latency. An example of this application is a smart home that supports multiple data-intensive devices and systems, including entertainment, HVAC systems, and security.

* With cases involving human latency, computing delays compound the problem and negatively impact the user experience. This can be costly for businesses when considering the potential for customer attrition. Challenges of this kind can be seen in applications such as e-commerce where speed has a direct impact on customer experience.

* In cases where human-led latencies aren’t an issue, machine-led delays can be particularly troublesome. Tolerance levels are typically much lower, because of the expectation of speed that comes with machine processing of data. As an example, any delays in commodities and stock trading, where amounts fluctuate within fractions of a second, may turn potential gains into losses.

* Life-critical applications involve complexities that impact human health or safety, and therefore, have very low latency and very high availability requirements. Speed and reliability are critical. Common use cases include smart transportation, digital health, connected and autonomous cars, and drones.

The edge complexities faced by various organizations require a robust infrastructure that can be rapidly deployed to support the future of computing. That’s why organizations are relying on more intelligent IT systems and infrastructure that deliver remote visibility and control in these locations. The objective is to enable a more sophisticated network edge and to simplify these increasingly complex environments.

This simplification can be done by leveraging cloud-based platforms that can convert massive amounts of data generated at the edge into actionable information. The outcome will be a more robust, effective edge of the network with self-healing capabilities that will require limited on-site human management.

For a host of organisations, the edge has become the most mission-critical part of their entire digital ecosystem. Intelligent infrastructure systems, along with machine learning capabilities, working in tandem with cloud-based analytics, are essentially transforming the way we perceive edge computing and edge services. In the following years, we can expect momentous advances in this space and preparing for the transformation will require close collaboration with network and IT service providers.

— Dr. AS Prasad, GM – Product and Marketing, Vertiv India.

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