Why Edge Computing Market Will Grow 30 Percent by 2022

Increasing numbers of big-data workloads and the rise of real-time computing have slowed production in the cloud; conventional architectures have been unable to meet future demand. Edge computing is coming to the rescue.


We’re hearing a lot about so-called “edge computing”—largely because our devices (smartphones, laptops, tablets, IoT devices) on the fringes of centralized systems can hold much more information and do more with it than in years past.

Definition: Edge computing is a method of optimizing cloud computing systems by performing data processing at the edge of the network, near the source of the data. This reduces the communications bandwidth needed between sensors and the central data center by performing analytics and knowledge generation at or near the source of the data. This approach requires using resources that may not be continuously connected to a network, such as laptops, smartphones, tablets and sensors.

By its very nature, edge computing—which also includes these devices communicating with each other via Bluetooth and other non-cloud methods—decreases workloads that used to be processed inside 24/7 cloud computing systems. This not only increases the efficiency of computing and data applications but also promotes further implementation of emerging technologies, such as artificial intelligence and 5G bandwidth.

This made edge computing a focus of discussion in 2017; naturally, interest continues to grow in the new year. OpenStack, for one open source community, has taken a central position in all of this change.

Substantial Growth Expected in Next Four Years

With this background, TrendForce on Jan. 22 forecast that the edge computing market of products and services will grow by a compound annual growth rate (CAGR) of more than 30 percent from 2018 to 2022. That’s a non-trivial expansion pattern, to say the least.

TrendForce analyst Jimmy Liu reminded eWEEK in a media advisory that the traditional architectures of cloud computing systems have led the market for many years and have created new business opportunities, such as cloud storage and big data analysis. However, with increasing numbers of big-data workloads and the rise of real-time computing, conventional architectures are slowing down and eventually will be unable to meet future demand.

With its decentralized structure, edge computing integrates networks, computing, storage and self-management at the edge of field devices and cloud gateways, enabling real-time response of field devices and enhancing the efficiency of data collection and advanced application. Edge computing also can reduce the cost compared with traditional architectures, Liu said.

Supply Chains Have Been Developing Industry Standards, Ecosystem

A number of new corporate alliances have been setting new standards for edge computing, because the trend is expected to bring changes to the architectures and actual applications in the market. For example, the Multi-Access Edge Computing (MEC) of the European Telecommunications Standards Institute (ETSI), the OpenFog Reference Architecture for Fog Computing and the Edge Computing Consortium led by Huawei have been actively developing reference architectures to establish new business ecosystems .

In addition, several companies have rolled out their edge-computing solutions, such as Azure IoT Edge launched by Microsoft, which puts machine learning, advanced analytics and AI services at front-end IoT devices which are closer to the source of data. Chip software provider Arm also introduced Mbed Edge, a computing platform to assist in protocol translation, gateway management and edge computing.

The rest of the industry chain—including server providers, network equipment providers, industrial computer providers, traditional manufacturers, and open source organizations—have all introduced corresponding solutions.  

Implementation of AI, 5G Will Depend on Edge Computing

Edge computing will have significant implications for the widespread use AI and 5G. Liu said that AI used to rely on powerful cloud-computing capabilities for data analysis and algorithm, but with the advancement of chips and the new architectures, field devices and gateways have been given basic AI abilities that allow them to assist in the initial data screening and analysis, immediate response to requirements and other attributes.

This advantage can further improve existing services in industries, smart cities and consumer markets, such as real-time alerts, security system, smart assistant and predictive maintenance, Liu said.

Edge computing is also an important technological transformation for 5G. Compared with 3G and 4G era, 5G network features far more diverse applications and network demands. Therefore, 5G networks must offer corresponding solutions for different applications and requirements, Liu said.

Chris Preimesberger

Chris J. Preimesberger

Chris J. Preimesberger is Editor-in-Chief of eWEEK and responsible for all the publication's coverage. In his 15 years and more than 4,000 articles at eWEEK, he has distinguished himself in reporting...