Is Edge Computing the Death of the Cloud?

From our latest Cloud Guide, we take a look at how one of the latest cloud innovations is designed to bring your data closer to you than ever before.

With the explosion of IoT, connected devices are collecting more and more information through sensors, cameras, accelerometers, LiDAR, and depth sensors. Connected products are inherent in all kinds of industries from manufacturing to automotive, health tech, energy, utilities, and wearable tech. Aided by the convergence of AI and 5G, the quantity of data being collected is only expanding. It's estimated that a fully autonomous car will encompass over 60 microprocessors and sensors and generate more than 300 terabytes of data per year. Or conversely, in an hour-long trip, up to 25GB of information will be sent to and from a connected vehicle (equivalent to about 100 hours of video).

What Is the Problem with All This Data?

With these massive quantities, capturing, aggregating, and analyzing data becomes a challenge. Not all data is useful, yet time-sensitive data such as autonomous vehicles, noxious gas monitoring, healthcare, and safety equipment is at risk of lag. A split-second delay of data (derived from, for example, a car being able to identify a pedestrian on the road, or a malfunctioning insulin pump) going to the cloud and back to the device could be disastrous or deadly. Other data sites face the challenge of a location where the use of IoT in rugged environments, such as an offshore oil refinery, underground mine, or deepwater well can result in unstable links with limited bandwidth and variable latency. Arguably less life or death, a virtual reality hang out would be less than immersive with poor data processing.

Enter edge computing, a concept with so many definitions according to industry and use cases that the Open Glossary of Edge Computing was created under the stewardship of The Linux Foundation, a community-driven process to develop and improve upon the terminology.

A split-second delay of data (derived from, for example, a car being able to identify a pedestrian on the road or a malfunctioning insulin pump) going to the cloud and vack to the device could be disastrous and deadly.

As the Linux Foundation explains, edge computing is:

"The delivery of computing capabilities to the logical extremes of a network in order to improve the performance, operating cost, and reliability of applications and services. By shortening the distance between devices the cloud resources that serve them, and also reducing network hops, edge computing mitigates the latency and bandwidth constraints of today's Internet, ushering in new classes of applications.

In practical terms, this means distributing new resources and software stacks along the path between today's centralized data centers and the increasingly large number of devices in the field, concentrated, in particular, but not exclusively, near the last mile network, on both the infrastructure and device sides."

Edge computing positions intelligence and processing capabilities closer to where the data originates, improving the ability to perform real-time analytics for actionable insights. As with scenarios like rugged environments, reducing the amount of data being sent to the cloud and between sensors minimizes latency and reduces time, energy, and bandwidth expenditures.

What Are the Most Common Edge Computing Use Cases?

A 2015 report by IDC forecasts that by 2019 45% of IoT-created data will be stored, processed, analyzed, and acted upon close to or at the edge of the network.

Perhaps the most significant economic benefactor to date is the industrial sector where the incorporation of data collection and processing at the edge better facilitate predictive maintenance and lower energy costs.

Cities are becoming connected through smart city initiatives focused on traffic patterns, weather, and the functionality of public utilities such as lighting, parking meters, smart traffic lights, buildings, transport, and waste collection. These initiatives involve the deployment of high-bandwidth and latency-sensitive apps that draw information from multiple sources. The generated data cannot be useful when stored in a remote, centralized data center. It must be closer to the point of interaction, something edge computing can enable. For example, if a smart city traffic control center detects a traffic bottleneck or accident, the information can be used to inform local bus timetables immediately of the delay and simultaneously recommend an alternative transport to visitors departing a nearby sports stadium after a game.

While healthcare is slower to adopt edge computing capabilities, when you consider a typical hospital room may contain up to 20 machines, the opportunity is compelling. Data from those 20 devices could be pulled into a single dashboard and combined with patient history from the electronic health record (EHR) to enable more evidence-based, real-time health care. The result is less time waiting for results and potentially fewer hospital visits.

It's Not a Competition Between Edge and Cloud

Edge computing in IoT does not mean the death of the cloud. Instead, it's a scenario which shifts functionality between edge gateways and cloud backends in the latest architecture. This will most likely be an aggregate model, encompassing the isolation of the edge with the power of selective aggregation of data and "nodes" at the edge.

Cloud computing will always have its place. For example, while many IoT devices require real-time decision making at the edge, businesses may need historical analysis for process improvement and model development. This is best achieved when the data from multiple edge devices can be combined centrally. It can facilitate an interconnected relationship where insights gained from historical analysis can be pushed back to the edge so that an IoT-enabled edge device continually evolves to make better real-time decisions. Thus, the computing model becomes a combination of edge and cloud computing where IoT devices operate at the edge in real time, collect and process raw data at the edge, and share metadata to the cloud for comprehensive historical analysis and continuous process improvement.

Security Will Remain a Challenge

It's arguable that edge computing enjoys some data security distinct from cloud computing as data on an edge device does not travel over a network where it's prone to interception. However, enterprise data centers are subject to well-established security defenses and security procedures which is less the case with edge computing. With connecting devices onto the internet that previously enjoyed obscurity when it came to security, the attack site becomes bigger. Every connected sensor and actuator represents a potential point of compromise for a malware DDoS attack. This was seen with the Mirai Botnet attack in late 2016 that commandeered hundreds of thousands of IoT devices in a large-scale Distributed Denial of Service (DDoS) attack. Any company, city, or device builder that considers the opportunity of edge computing will need to keep security in mind.

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