Challenges of edge computing

2:27:44

2024-12-16

949

Although edge computing has the potential to provide compelling benefits across a multitude of use cases, the technology is far from foolproof. Beyond the traditional problems of network limitations, there are several key considerations that can affect the adoption of edge computing:

Limited capability. Part of the allure that cloud computing brings to edge -- or fog -- computing is the variety and scale of the resources and services. Deploying an infrastructure at the edge can be effective, but the scope and purpose of the edge deployment must be clearly defined -- even an extensive edge computing deployment serves a specific purpose at a pre-determined scale using limited resources and few services
Connectivity. Edge computing overcomes typical network limitations, but even the most forgiving edge deployment will require some minimum level of connectivity. It's critical to design an edge deployment that accommodates poor or erratic connectivity and consider what happens at the edge when connectivity is lost. Autonomy, AI and graceful failure planning in the wake of connectivity problems are essential to successful edge computing.
Security. IoT devices are notoriously insecure, so it's vital to design an edge computing deployment that will emphasize proper device management, such as policy-driven configuration enforcement, as well as security in the computing and storage resources -- including factors such as software patching and updates -- with special attention to encryption in the data at rest and in flight. IoT services from major cloud providers include secure communications, but this isn't automatic when building an edge site from scratch.
Data lifecycles. The perennial problem with today's data glut is that so much of that data is unnecessary. Consider a medical monitoring device -- it's just the problem data that's critical, and there's little point in keeping days of normal patient data. Most of the data involved in real-time analytics is short-term data that isn't kept over the long term. A business must decide which data to keep and what to discard once analyses are performed. And the data that is retained must be protected in accordance with business and regulatory policies.

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