Everything is constantly changing, and so are successful organizations and their operations to keep abreast of the changing technology and fulfill business needs in the smartest way possible. One of the most recent innovations in the technology space is cloud computing. Cloud computing enables saving information on shared servers instead of local drives. However, with the constantly growing need for faster connections and speedier response times, the connected devices equipped with the advanced predictive model call for a more sophisticated technology that can cope up with the next-generation IoT.
For example, think of a sudden breakdown in a boiler operation, which happens to be at the core of an industrial plant. The impact is not only limited to itself but also to all downstream systems, and the disruption can be big enough to bring all operations to a sudden halt. Such breakdowns can also directly affect the end consumers as well. In modern maintenance approaches, edge computing-based predictive maintenance is gaining ground for increasing the reliability of the equipment by sensing the problems even before they arise and even faster than cloud computing.
Edge intelligence is based on fog computing where the processing of data and related analysis are carried out closer to the edge where the industrial devices and sensors are actually located, instead of sending the data to a remotely located, centralized cloud. Comparison of the baseline data to the real-time data takes place in the edge itself, and alerts are triggered if the real-time performance falls beyond the baseline data range. There are four types of edge computing, namely:
1. Devices - The devices that perform the analysis and real-time inferencing without the involvement of the edge server or the enterprise hybrid multi-cloud
2. Edge server - It consists of one or more tiny servers also known as a nano datacenter
3. Edge network - It is a small data center consisting of everything from a few to many server racks.
4. Enterprise hybrid multi-cloud - It offers the classic enterprise-level model storage and management, device management, and especially, enterprise-level analytics and dashboards.
Uploading a huge amount of data on remote data centers can be avoided by using edge intelligence, as many maintenance issues can be resolved on the site using analytics and processing applications residing on the edge. Many industries, including the manufacturing industry, benefit from it. Other industries include:
- Transportation – Edge computing helps businesses manage vehicle fleets based on real-time conditions.
- Retail - The nature of retail businesses varies with varying local environments. Edge computing can be an effective solution for data processing at each local store.
- Efficient healthcare - Clinicians can take prompt decisions to help patients in time.
- Agriculture - Data is collected and analyzed to measure the impact of environmental factors and improve crop-growing algorithms, thereby ensuring harvesting in peak conditions.
Edge computing is continuously gaining focus. For example, Amazon, Microsoft, and Alphabet– all three of these tech giants' cloud offerings -- Amazon Web Services, Microsoft Azure, and Google Cloud -- support edge computing in both hardware and software. The edge computing market is seeing exponential growth. In fact, IDC predicts that spending on edge computing will reach $250 billion by 2024.
Edge computing, thus, processes system data within the network, and only relevant data or information are conveniently bundled and sent to cloud, thereby reducing latency issues. This brings a subset of the computation and data storage closer to the location where it is needed to improve response times and save bandwidths. Some of the main benefits of edge computing over pure cloud computing include secured data management, lower connectivity costs and better security practices, reliable, uninterrupted connections, and quicker analysis and decision-making. Edge will indeed complement cloud computing in the future to evolve into a hybrid approach of computing.