The rise of edge computing will come as no surprise to anyone following computing architectures during the past 40-plus years.
Although the topological concept of edge computing is decades old, it was only recently pushed into the limelight because of the limitations imposed by the centralized implementation of hyperscale cloud and the growing investment in the “internet of things.”
Edge computing places content, data and processing closer to the applications, things and users that consume and interact with them. It takes the traditional information technology workload quandary of “What goes where?” and encourages workload and capability placement that optimizes the balance of latency, bandwidth, autonomy and security across a continuum of options, from hyperscale cloud data centers to home thermostats.
As organizations strive to remain competitive in the digital business era, especially in a post-pandemic world, edge computing is a new enabler of customer insight and retention. Digital business relies on the convergence of the physical and the digital to create new business moments, and it benefits from and is enabled by edge. As one example, enterprises are using edge topological ideas to cut WAN costs dramatically, with one Gartner client cutting its WAN costs in half while improving resiliency and improving user experience.
Compounding the need for edge computing is the explosive amount of data being generated outside of the data center, as estimates describe 50% of enterprise data being generated outside of the core by 2023. Not all of the wide variety of data needs to be or even should be delivered to the core. Enterprises are using the edge model to create value from data and observations at the edge, just as the early promoters of digital business promised.
Edge computing lets us thumb our noses at the speed of light, to work around it to expand the reach and capabilities of information technology. It is not a technology looking for a market, per se, but rather a topology and design being fulfilled with technologies such as IoT and the cloud to enable new solutions.
To that end, here are six components of the future state of edge computing that infrastructure and operations leaders must incorporate into their cloud computing plans as a foundation for new application types over the long term:
Edge diversity will demand hardware abstraction and vendor-independent architectures
A diverse set of requirements at the edge will drive an equally diverse set of edge computing and storage platforms at the edge. As such, independent software vendors, system integrators and enterprises will look to build cloud-independent solutions while at the same time expressing interest in using common architectures and open-source technologies to speed development and avoid vendor lock-in.
Edge computing will help drive a major shift in application development and delivery architectures for those incumbent system integrators and ISVs in the retail and industrial areas, too. Early efforts may stitch together edge-located assets and core-based existing applications, but applications based on microservices and flexible deployment locations and options will fuel the second wave and growth of edge computing.
Security on the edge
As with most IT initiatives, security is often cited as a concern when enterprises start to consider edge computing. However, zero-trust models and the implementation of a Secure Access Service Edge or SASE will help alleviate such concerns as early as next year.
Edge solutions will also obtain a significant boost in manageability and security from network-based and SaaS-based orchestration platforms. In particular, SASE is typically delivered as a service and based on the identity of the device or entity, combined with real-time context and security and compliance policies.
Interest in 5G grows
The growing interest in 5G solutions, in particular mobile edge compute or MEC, will offer reduced latency and a way to connect the rising number of edge devices, particularly in the consumer space. 5G and other future network technologies and optimization techniques will enable edge computing to continue to expand adoption, while servicing a wider range of use cases. It is expected to mature within the market over the next two to five years.
Cloud-out versus edge-in
The cloud and the edge complete each other: They are complementary rather than competitive or mutually exclusive. But two different perspectives, cloud-out and edge-in, will foster very different architectural approaches.
Edge-in to cloud describes an architecture in which edge applications, servers and gateways are designed and built independently from any hyperscale cloud they may connect to for application services, such as analytics. The rise of edge-in cloud architectures results from the need to pull cloud services to the edge and use them selectively in edge-specific ways, rather than push public cloud architecture to the edge as a complete platform solution.
Edge video analytics
Edge video analytics is the real-time analysis for object detection and avoidance and event recognition of video data, either executed within a video camera or edge computing system. Edge video analytics systems have the ability to run deep neural network algorithms within either the camera or the edge computing system.
Edge video analytics will be deployed broadly as the foundation for early edge applications, such as video surveillance and visual quality inspection. This will expand the benefits of sensor monitoring to include pattern and anomaly detection and even inclusion of legacy all-analog devices into an IoT or edge computing implementation.
Edge as a service
Edge as a service describes a model in which the edge platform, or even the edge applications themselves, are offered via provider-owned and operated assets, requiring little to no infrastructure on the part of the customer.
One of the greatest impediments to deploying edge computing in 2020 was the lack of broadly accepted infrastructure and operation models, putting the onus on the enterprise to piece together solutions from still-emerging technology stacks and operational models. To combat this, edge as a service focuses on a delivery model for edge computing in which a communications services provider, cloud provider or even ISV or system integrator provides all or nearly all of the infrastructure required to deliver edge-based applications, shielding the customer from technical and market volatility.
Edge computing is moving away from IoT-focused to a broadly considered complement to the more centralized hyperscale cloud. The edge role in distributed cloud and digital business ensures that despite the nascent market, technologies and architectures, edge computing is here to stay. Bear in mind that turning the edge from chaos into a competitive differentiator with greater customer insight, performance and customer satisfaction is a journey and not a destination.
Bob Gill is a research vice president in Gartner’s Infrastructure Strategies group and is the founder and leader of the Edge Research Community at Gartner. He wrote this article for SiliconANGLE. He and other Gartner analysts will provide more insight into edge computing at the Gartner IT Infrastructure, Operations and Cloud Strategies conference, taking place virtually through Thursday, Dec. 10, in the Americas and EMEA.
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