Top trends in Edge Computing
Things are moving fast in the edge computing space. According to IDC research, by 2023, more than 50% of new enterprise IT infrastructure deployments will take place at the edge of the network instead of central data centers. It is almost impossible to have a tech conversation without touching on some aspect of edge computing. So we asked Pavel Kozlov, a Solution Architect at Forte Group, to share his opinion on this topic.
Metaverse
The edge computing market trend is the metaverse — a massive, interoperable three-dimensional world for things like gaming, socializing, shopping, meeting, and learning. Right now, the metaverse is still in its infancy. However, it has the potential to become an $800 billion market. And edge computing will play a key supporting role.
The metaverse contains several different layers, including a decentralization component that combines blockchain and edge computing infrastructure to enable simultaneous streaming experiences at scale. Edge computing takes the pressure off the public cloud and makes it possible to achieve lightning-fast, 3D experiences for users.
Edge Containers
Looking forward to seeing more software containers deployed at the edge of the network, along with orchestration platforms like Kubernetes. Containerization involves packaging applications in a way that isolates software and its dependencies from other components. Containers are highly portable and lightweight, which makes them easy to deploy and maintain. You can use them in public, private, and hybrid cloud environments.
Unlike traditional cloud containers, edge containers run at the edge of the network. They offer several advantages including lower latency, global load balancing, and lower bandwidth consumption.
5G and Edge computing
Organizations are investing in 5G to streamline data collection, power IoT devices, and enhance digital experiences. Under optimal conditions, 5G is about 10 times faster than 4G. But while 5G can provide fast local connectivity, the technology doesn’t account for computing or storage. Most businesses still run supporting functions through a central data center, which creates latency and reduces network performance.
Looking forward, more companies will augment 5G deployments with edge computing. This approach can reduce latency, and help companies generate stronger ROI from their 5G rollouts. Edge computing also helps with use cases like virtual and augmented reality and self-driving cars which require processing heavy amounts of real-time data with minimal latency.
Edge Security
Cybersecurity is a rising concern moving, from cloud computing to artificial intelligence. A multitude of various growing technologies, such as 5G and IoT, present their own cybersecurity vulnerabilities. Edge computing is being leveraged to mitigate these potential threats.
Traditional, centralized networking and data storage gives attackers a singular hub to target. Edge computing diversifies this network, in a sense, and provides greater protection.
This does not mean that edge computing is flawless. In fact, will be defined by companies grappling with edge computing’s security flaws, rather than its strengths. A Kollective report showed that 66% of IT teams see edge computing as a threat to their organizations.
One of the primary threats that edge computing enables is its increase of physical data sources. Because edge computing relies on more physical resources to be placed in the real world, physical attackers are given more targets to compromise networks. If malicious actors somehow gain access to these devices, they could extract valuable information, tamper or destroy node circuits, or even change entire operating systems and node software.
Still, this topic is rather divisive. Some, like Kollective VP of corporate development Kirk Wolfe, believe that edge computing’s concerns gloss over its benefits and might even be slowing adoption. Clearly, cybersecurity is an ongoing question that rests on many advancing technologies, including edge computing. Expect these questions to be continually debated as the technology moves forward.
AI on the Edge
AI and Deep Learning applications are gaining prominence across industries as well as verticals, and businesses will demand high-density power from proximate data centers. Thus, Edge data centers are an ideal mechanism to support AI/ML workloads, as they enable high levels of computational power on smaller physical footprints.
Edge Computing by industry
Agriculture:
Agriculture shows some of the most promising edge computing. Farmers use edge technology to track water use and animals, decide where to put fertilizer and in what amounts, analyze soil quality, and monitor crop growth. Even tractors can become part of an edge network, along with sensors spanning fields.
Retail:
IDC FutureScape: Worldwide Retail Predictions reveals that “by 2025, 90% of the top 2000 retailers will employ edge computing to harness the explosion of data in stores for better workforce productivity and customer experience while reducing costs by 20%.” In the retail-specific use cases where data-intensive, latency-dependent processing is needed most, edge computing can help optimize customer experiences and greatly streamline operations. It’s also one use case where multi-layer security will be paramount and need to be built from the start.
Healthcare:
The healthcare industry has been at the forefront of IoT adoption, so it’s only right that they’re leading in the edge computing technology space as well.
Large hospitals are realizing it’s more beneficial for operations to keep data, such as medical sensors, electronic health records, and digital imaging systems, close by rather than pushing them to the cloud.
Recently, HCA Healthcare teamed up with Red Hat to ideate and create a real-time sepsis diagnostics solution. Using edge computing, HCA Healthcare was able to cut this traditionally long process down to nearly a day less. This has increased healthcare substantially as the project has been implemented.
Manufacturing:
Edge computing allows manufacturers to automate factory floor and supply chain processes through advanced robotics and machine-to-machine communication closer to the source, rather than sending data to a server for analysis and response. For example, scanning sheet metal to detect fatigue, monitoring flow through pipes, or keeping track of automated machine cycles, to reduce latency, resulting in faster analysis and correction.
