The evolution of edge computing is garnering a lot of attention to businesses these days. With the rise of faster networking technologies, such as 5G, are enabling edge computing systems to stimulate the creation or support of real-time applications like video processing and analytics, autonomous cars, automation solutions, artificial intelligence and robotics, among others. The advent of edge computing makes it possible to perform computation and store data closer to the devices where it is being gleaned, instead of relying on the cloud.
The exponential growth of the internet of things devices has led to the development of edge computing. As IoT devices are connected to the internet for either receiving information from the cloud or delivering data back to the cloud, processing that data requires massive computation. Edge computing allows data generated by IoT devices to be assessed at the edge of the network before being sent to the cloud infrastructure. Leveraging cloud architecture may have cost-effective as companies that used the cloud for many of their applications found that the costs in bandwidth were higher than they expected.
Edge computing emerges as an effective technology and continues to grow as more sophisticated devices increasing at an unprecedented rate. Here we have accumulated top trends of edge computing everyone should look for in 2020 and beyond.
Heightened Adoption of Edge
As IoT devices are continuously increasing and becoming quite powerful, collecting, storing, and processing more data than ever, this has paved ways for companies to optimize their networks and relocate more processing functions closer to the origin of data at the network edge. With the rise of 5G technology, edge computing is also accelerating in terms of adoption, allowing businesses to use applications and process data at a quick pace. According to a report, the global market of edge computing is predicted to grow at a CAGR of 26.5 percent from US$2.8 billion in 2019 to US$9 billion by 2024.
Cloud will Move to the Edge
According to Gartner, by 2025, companies will produce and process more than 75 percent of their data outside of traditional centralized data centres, that is, at the edge of the cloud. This will enable data to be assessed, processed, and transmitted at the edge of a network. It is also expected that next-generation applications, which will focus on machine-to-machine interaction with concepts such as IoT, Machine Learning and AI, will drive the businesses’ move towards edge computing. This computing technology leverages digital devices, often placed at various locations, to transfer the data in real-time or later to a central data repository.
Edge Computing will Become Prevalent
The potential of edge computing will become apparent when 5G wireless networks go mainstream in the coming days. As this ever-fast network technology will users to enjoy consistent connectivity without even realizing it, this will augment the rapid adoption of edge computing. The leading graphics and AI acceleration hardware designer and manufacture NVIDIA brought its EGX edge computing platform last year that helps telecom operators adopt 5G networks capable of supporting edge workloads. The new NVIDIA Aerial software developer kit will assist telecom service providers to develop virtualised radio access networks, enabling them to support smart factories, AR and VR and cloud gaming.
AI and Machine Learning will Go Mainstream
The potentials of AI and machine learning have already proven across industry verticals. Now, developers are exploring ways to integrate AI with IoT to assist companies in a variety of industries to benefit from the data generated by connected devices. This is supposed to improve production capabilities, boost efficiencies and lessen operating costs by looking into real-time data from multiple points to create actionable insights. However, high-performance computing (HPC) applications involving AI may require much higher power densities. In this way, edge data centers will become effective for supporting AI and ML workloads as they have the potential to enable high levels of computing power on smaller physical footprints.