As industries today are adopting more edge computing framework, they must pay heed to common challenges in edge adoption

With the increasing demand for faster data processing, companies are switching to edge from cloud. This technology which is characterized by distributed, open AI architecture powered decentralized processing, edge allows data processing within the device in less than a few milliseconds, which provide real-time insights. The new found popularity of edge computing can be accredited to the proliferation of the internet of things (IoT) devices. While edge promises of low latency and better security than cloud, there are many challenges to edge adoption.

The key challenge is to first identify the functions that need to be performed at the edge. This is because, the existing edge hardware is comparatively less powerful than other data processing technologies. So to maximize the best out of it, one has to position edge at strategic end points (generally time sensitive) for specific functions. Since these edge devices tend to be present at remote locations, the devices must have the capability to run with minimal intervention. It is important to note that though edge systems typically need low maintenance, they are monitored and updated as required.

Another hardware constraint of edge is that such devices have limited computing footprint. This is why embedding the functionalities of a full-fledged data center is challenging. However, latest developments in edge AI chips offer some promising news for future. Today, the presence of Graphical Processing Unit (GPU), or Visual Processing Unit (VPU) in edge device, is empowering it to cater to a wider range of models and application complexity.

The industrial benefits from edge computing are become more apparent. For instance, self-driving cars in automotive sector, will rely on edge for navigation, point of sale terminals at retail outlet will leverage edge to capture and analyze data to improve customer experience. But all these services and applications will require low latency and uninterrupted internet connectivity. In other words, while edge brings data resources closer to users, it minimizes overall network bandwidth constraints. However, edge vendors should ensure that the system can operate independently and cache data in the event of losing a connection to the data center or cloud host, or during unreliable data connections. Further, edge computing systems must be managed in a consistent and effective manner in order to ensure that end-users can rely on the infrastructure.

Since, edge computing is a distributed model, it is obvious that its security concerns are very different from a centralized model. In edge systems, users do not have to entrust sensitive information to the third-party provider. However, this is possible if the edge vendor is keen on investing in securing its local network and without protection, each end point can be a potential point of entry for malicious entities. Apart from physical security, the logical security and the application and data security of that device also need to be safeguarded.

According to Frost and Sullivan, to save costs and speed up deployment, a lot of edge devices don't encrypt data natively. Therefore, IT managers need a security framework in place before the large-scale roll-out of edge projects, to shield the devices in wake of a cyberthreat.

Apart from the above, heavy cost constraints also prevent mass deployment of IT to the Edge. The reasons for such higher price of edge systems can be narrowed to two main factors: R&D in edge enabled software frameworks, and hardened equipment used to protect chips from changes in temperature, humidity, and dust.