Edge Computing: The Future of the Hybrid Cloud

In edge computing, data processing and compute power is distributed and decentralized to the extreme ends of the network. This is not an entirely new concept. From an architecture principle perspective, we have seen such distributed models before when it came to geographically distributed grid computing and peer-to-peer networking.

Edge computing has its obvious advantages over centralized computing. For starters, it provides for improved Quality of Service (QOS) given that lower data volumes lead to decreased latency and improved overall transmission costs. It also decreases dependency on the core computing environment, be it a cloud or an on-premise environment, therefore eliminating the centralized server/cloud as a potential bottleneck. Additionally, a distributed architecture provides for greater scalability as it is easier to virtualize processing capacity on an as-needed and real-time basis.

However, the need for edge computing today is not driven by these advantages but to address the need to perform specialized computing on local devices (read: Internet of Things). For example in the Automotive industry, when it comes to self-driving cars, the cars need to be intelligent enough to take immediate action as required in an emergency, without any dependency on a centralized network. Similarly, think about intelligent robots who while still connected to a centralized network will still need to be able to independently act. Its also the same for sensors and physical equipment at a manufacturing unit, which should be able to self-heal or regulate themselves as possible.

Edge computing has also become possible today because of the ability of smaller devices to now have immense processing power. Most modern cars now have well over 50 microprocessors and most households have multiple connected devices.

This doesn't mean that edge computing will blow over centralized cloud computing. These worlds will need to co-exist and a hybrid will form. There were good reasons for businesses to move from on-premise computing to cloud computing. Cloud computing allows for better overall management of applications and data given its centralized architecture model. They are also simpler to create and support. The self-driving car, the intelligent robot and the intelligent sensor will still need to communicate with a centralized cloud to receive patches and also send useful updates that could further be distributed by the centralized cloud to other connected devices.

GE was a pioneer with its Predix platform for Edge management. Amazon followed soon with its Greengrass platform in 2016 and earlier this month, Microsoft launched its Azure IoT Edge platform. This is a new and exciting space and much remains to be seen on how new technology and business will unravel.