By Leonardo Murillo
With the endless opportunity offered by loT devices, many companies are rethinking how they process data. Edge computing, which allows you to capture and process data closest to where that data is needed, is becoming the next big thing in cloud computing.
A new approach to distributed system architecture, edge computing helps you go beyond what’s possible with traditional cloud-based environments to process data at speed, improve security, and more.
The main benefits of edge computing include:
- Speed: The edge enables a wide range of new use cases and applications that may require millisecond response times. Data is processed faster and can be used for real-time decisions, with processing taking place closer to the source.
- Scalability: By relying on the highly distributed computing model that the edge provides, companies can reduce IT infrastructure costs. Network traffic costs are dramatically reduced by eliminating the need to transport data to centralized processing locations, making it easier to scale operations with cloud-based tools.
- Security: The distributed nature of an edge-based solution means data can be used and disposed of without transporting over a network. Data can be anonymized and secured before shipping for storage. These characteristics allow simple assurance of data governance and sovereignty.
- Reliability: Edge solutions are distributed by definition, and usually built to work in complete isolation and sometimes without any means of connectivity. This type of architecture eliminates most single points of failure and guarantees undisrupted operation for each independent site.
How Edge Computing Works
Edge computing enables data processing to take place closest to where information is generated. It relies on the growing availability of high-speed wireless connections, the increase in computational capacity in small form factor devices, and various advances in packaging, managing, and delivering software to distributed environments. The edge encompasses everything that handles computations, stores data, and is reachable via a network that expands outwards from your cloud or data center.
What really makes edge computing a breakthrough technology is that it allows you to get as close as you can to the data source – for example, smartphones, wearable tech, and a growing range of industrial devices connected to the Internet. This on-site processing helps avoid latency, controls cost, and is often employed to meet compliance concerns for location-bound data.
Leveraging Google’s edge devices and cloud tools, you aren’t limited to a specific location for machine learning. You can pick your machine learning location based on your standards for architectural efficiency, data consistency, and data privacy.
What Edge AI’s Used for
Edge AI provides close to instantaneous predictions, insights, and recommendations. By processing data closest to where that data is, you can shift to near-instantaneous responses, enhance data privacy, and the ability to work offline or in poor connections.
Organizations can combine machine learning capabilities across the various tiers of their edge architecture, processing and shipping data from devices into the far edge, onto the near edge, and eventually into the cloud or data center, progressively extracting insights as data becomes less specific yet more voluminous.
How Edge Computing Will Evolve and Grow Applications
Edge computing is a fast-growing area, particularly with the impending 5G revolution and the consistent and dramatic increase in computational capacity on small form factor, handheld, and other devices, including all things IoT.
In general, the edge is poised to revolutionize nearly every facet of technology and business by enabling rapid decision-making at the point of data. More compute capacity, faster networks, extremely low latency, and machine learning available at the edge allow us to free up human creativity. We can begin to create experiences that we currently can’t even imagine, the answers to many of today’s most pressing problems can be solved by rapidly leveraging data that revolves around the user.
We already see hints of these new solutions at work today and many businesses have been able to leverage local user data by sending it to the cloud, this comes of course with a penalty in terms of speed, responsiveness, and cost. The natural next step is moving some of those features closest to the user so they can better leverage the data that is closest to them.
The Challenges of Edge Computing
There are many advantages to edge computing, but, of course, it doesn’t come without its challenges. There is still a lot of uncertainty about what artificial intelligence and machine learning can do, with a lot of it being unfounded. Privacy and security will remain a challenge during the next era of AI that edge computing will bring.
Additionally, computational capacity at the edge and the ability to effectively train in a distributed edge are also major challenges the technology landscape will face. But if Moore’s law continues to be true, we can expect innovation to quell these sore spots as organizations seek to ramp up adoption.
With 5G networks on the rise, edge computing opportunities will only grow and make it easier to transmit high volumes of data. Next, read about our 5G-ready edge AI for retail solution with Google Cloud.