Once viewed as a niche computing architecture for Internet of Things (IoT) projects, edge computing has rapidly matured into a far more mission-critical computing framework. With its ability to extend data processing beyond the data center, edge computing is now widely seen as the key to unlocking new levels of productivity and innovation.
In the edge model, computing resources are moved closer to the physical location where data is created in order to minimize latency, preserve bandwidth and allow devices to work with near-real-time data. This is accomplished with the deployment of multiple “micro data centers,” which can be anything from microcomputers running lightweight software to large shipping containers outfitted with servers, storage and networking equipment.
While it remains central to IoT operations, edge computing now provides critical connectivity for a growing range of business applications. International Data Corp. (IDC) recently identified more than 150 edge use cases across various industries and domains, including retail and omnichannel operations, manufacturing, asset management and freight monitoring, utilities, smart cities and intelligent transportation systems, and public safety and emergency response.
The Data Processing Dilemma
Many of these use cases are driven by the need to improve data processing for artificial intelligence (AI), machine learning (ML) and analytics applications. These technologies are built to analyze massive datasets in order to identify patterns and develop actionable business insights. However, traditional wide-area networking (WAN) architectures are ill-suited for the amount of data traffic these processes generate.
AI and ML data processing in a traditional WAN environment requires data to make an extended round trip to a remote data center or the cloud. Data is collected from multiple sources and transmitted across already-congested Internet pipes for processing and analysis before results are returned to users. Moving all that data back and forth is highly inefficient, chewing up bandwidth and creating latency issues.
The edge model eliminates that round-trip journey, enabling local analysis that produces faster response times, reduced storage costs and bandwidth optimization. Single-hop data transfers are particularly useful for running real-time services and improving mobile data analytics. That’s why IDC predicts that by next year more than half of all new enterprise IT infrastructure will be deployed at the edge rather than inside data centers.
Call on the Experts
However, successfully crafting and implementing an edge computing framework can be a challenging proposition. Managing a variety of distributed micro data centers and their compute, networking and storage resources can be a big job. The manual configurations required to differentiate and segment traffic must be updated regularly as application profiles and business needs change. That means someone from IT has to visit each location for every update.
Security is another major consideration. The edge model creates an increased attack surface by locating servers, storage and other resources outside the network perimeter and closer to data sources. In addition, edge devices lack the physical protections that are normally put in place in data centers, making them vulnerable to a range of threats.
By moving bandwidth-intensive data processing closer to users and data sources, edge computing resolves many of today’s most critical networking challenges and opens the door to numerous new business applications. However, designing, implementing and managing a secure edge environment requires a significant amount of IT expertise.
Technologent has decades of experience in the design, installation and provisioning of distributed networking solutions for clients around the world. In addition, our cybersecurity, data storage, application management and cloud consulting teams help ensure a comprehensive approach to edge deployments. Contact us to learn more about the many benefits edge computing can deliver for your business.
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