With the growing demand for faster processing of data, businesses are shifting from the cloud to the edge. This technology, which is characterized by decentralized processing driven by a distributed, accessible AI architecture, enables data processing inside the system in less than a few milliseconds, providing real-time insights. Edge computing’s newly discovered success can be attributed to the proliferation of the Internet of Things (IoT) devices. Although the edge offers low latency and better security than the cloud, edge adoption has many challenges.
The main challenge is to first define the functions at the edge that need to be performed. This is because, compared to other data processing technologies, the current edge hardware is relatively less efficient. For unique functions, one has to place the edge at strategic endpoints (usually time-sensitive) to maximize the best out of it. Since these edge devices appear to be present at remote locations, the equipment must be able to function with minimal interference. It is important to note that while edge systems usually need low maintenance, as needed, they are monitored and modified.
Another disadvantage of the edge hardware is that such systems have a small machine footprint. This is why it is difficult to embed the functionality of a full-fledged data center. Recent developments in edge AI chips, however, include some exciting future news. The inclusion of the Graphical Processing Unit (GPU) or Visual Processing Unit (VPU) in the edge system is currently allowing it to appeal to a wider range of models and complexity of applications.
Edge computing’s industrial advantages are becoming more evident. For example, automotive self-driving cars will rely on edge for navigation, retail point of sale terminals will utilize edge to collect and analyze data to enhance the customer experience. But low latency and uninterrupted internet connectivity will require all of these services and applications. In other words, while edge brings users closer to data services, it minimizes overall limitations of network bandwidth. However, in the event of losing a link to the data center or cloud host or during unstable data connections, edge vendors should ensure that the device can function independently and cache data. Also, to ensure that end-users can rely on the infrastructure, edge computing systems must be managed consistently and effectively.
Since edge computing is a distributed model, it is clear that its security issues vary significantly from a centralized model. In edge networks, third-party providers do not have to entrust confidential information to consumers. This is, however, possible if the edge provider is keen to invest in protecting its local network and each endpoint may be a potential point of entry for malicious entities without protection. The logical protection and the application and data security of that computer also need to be safeguarded, apart from physical security.
To save costs and speed up deployment, many edge devices do not natively encrypt data, according to Frost and Sullivan. Therefore, before the large-scale roll-out of edge projects, IT managers need a security system in place to protect the devices in response to a cyber attack.
Heavy cost constraints also prevent mass deployment of IT to the Edge, apart from the above. It is possible to restrict the reasons for such higher prices of edge systems to two key factors: R&D in edge-enabled software frameworks and hardened devices used to protect chips from temperature, humidity, and dust shifts.
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