Picture a leak on a piece of manufacturing machinery. You need to act quickly to reconfigure valves and pumps to stop the leak, because you can not afford for it to go unnoticed and damage expensive equipment or bring line production to a halt. This kind of instantaneous decision-making is ideal for multi-access edge computing (MEC). This technology enables the real-time processing of data, close to where it is needed.
MEC is a kind of computing that moves processing away from a central data center closer to where data-generated insights are made. Such a move enables real-time analytics and faster response times in a variety of critical applications. This is a complement to a cloud connection for routine monitoring and control of those same devices.
Jump to what you need:
- Characteristics of MEC
- Benefits of multi-access edge computing
- How is MEC used?
- Multi-access edge computing use cases: IoT edition
- What is IoT edge computing?
MEC is fundamentally about efficiencies–in decreasing computation times so insights can be acted upon faster. And in decreasing distance insights have to travel. The fundamental thesis driving MEC is that the asset generating the data needs to respond in real time based on insights from that very data.
The primary characteristics of MEC are related to its architecture and availability:
Low latency: Because computation happens close to the source of data, enterprises can enjoy faster response times and a more seamless experience.
Real-time: An increase in computation capacity at the edge and decreased latency mean users can act on data insights in real time.
Proximity of data-processing platform: Reduced centralized data processing. While the central cloud still processes long-term data insights that can then be fed back into the data loop, more immediate processing takes place on the MEC platform, close to data sources.
The growth of MEC has not been in isolation. Specialty hardware such as AI accelerators on a chip have helped with processing of machine learning models at the edge.
The ability of MEC to deliver real-time insights at the edge opens up a range of benefits. These include:
Custom data processing per end use case
No longer does all data have to be routed to the cloud and back. In this hybrid model, enterprises can choose how and where to process data and act on insights. Long-term model crunching and pattern analysis in advanced machine learning models can still take place on the central cloud while real-time data processing for immediate actionable insights can take place at the source of data through MEC. The approach can be customized by the use case at hand.
Decreased network congestion
Anyone who has worked from home during the peak of the COVID-19 pandemic knows the strains on network systems when everyone is using data-intensive operations like video meetings. Storing, processing, and acting on data at the source through MEC, decreases network traffic and subsequent bottlenecks.
Greater availability of assets
Distributing computing resources over a variety of end points distributes the load strategically. If the central cloud server is unavailable for some reason, that does not necessarily mean disrupted operations at the data source. When all infrastructure necessary for operations is located close to where they are needed, there is less chance of dramatic disruption.
Easy scaling of operations
Just as with all cloud computing, MEC enables enterprises to bite off just as much resources as is needed to do the job. They can scale operations easily and in proportion to the number of end users or endpoints on the network. MEC is a much more resource-efficient way of data processing.
Underlying MEC is a strong infrastructure network for connectivity. Assuming that MEC essentially involves pushing out resources to the ends of a cellular network, it is typically implemented by placing storage and computing power at the edge of the Radio Access Network (RAN).
The rapid growth in data-driven digital transformation has led to an explosion of cases where insights at the edge can drive new operations in real time. Some of the many use cases of MEC include:
We are making steady progress toward autonomous driving and marking those along the way with improvements such as parking assist and lane assist technologies. Input data to vehicles needs to be processed in real time and at the edge, something MEC facilitates.
Smart buildings, smart traffic lights that ease congestion and reroute commuters. Vehicle-to-vehicle and vehicle-to-pedestrian communications promise to make the roads safer and more fuel-efficient for all.
Custom notifications depending on location data. Coupons that upsell get routed through mobile devices. The edge in retail is already pressed into service but MEC can deliver more seamlessness between the real and digital worlds. Digital mirrors that enable customers to try on and order apparel through the push of a button are also waiting in the wings for larger scale deployments.
During stadium events, fans can get deals on coupons for merchandise if a particular player scores a goal. The large digital screens can deliver more interactive content based on how the game is unfolding. Such selected targeting of content acts on real data in real time, something MEC enables.
Augmented and mixed reality
Augmented and mixed reality enable digital elements to be layered on top of the physical world. In mixed reality the two can also interact. AR and MR are being deployed through mobile devices at the edge to troubleshoot broken assets and help workers train on equipment without having to consult bulky and outdated manuals. Reactive AR and VR methods very much depend on successful implementations of MEC.
Suggested reading: Edge vs. Cloud: Which Computing Technology is Right for You?
One of the biggest promises of digital transformation in asset-driven industries like manufacturing is that machines can be embedded with sensors to generate data about their health. By developing machine learning models that compare what the machines are telling us in real time against what the baseline should look like, plant managers can be alerted when there is a problem that might disrupt production.
The Industrial Internet of Things (IIoT) or IoT has a key role to play here in data harnessing and transmission for processing. In multi-access edge computing, a variety of machines on the plant floor can relay information to the MEC platform for immediate data processing and insights.
IoT uses MEC for its immediacy and lower latency. Manufacturing is not the only industry in which IoT-embedded devices need speedy action. Fleet management, wearables and telehealth, and logistics are just a few of the many industries where IoT and MEC work in concert to deliver greater efficiencies in real time.
IoT deployments at scale means greater need for real-time processing at the edge so enterprises can realize the complete benefits of the technology. Edge computing platforms like AWS IoT Greengrass and Microsoft Azure IoT Edge, improvements in network architecture, and the way in which data is stored and processed is making asset-driven industries a part of the wave of digital transformation.
IoT edge computing is a kind of MEC specifically for IoT-embedded devices. IoT data is often used to detect anomalies, whether that be suspicious cargo at airports, fraud in credit card processing, or suspicious temperature profiles of machines, indicating possible overheating.
In all such cases, feedback needs to be immediate to avert larger problems. IoT edge computing does the job by delivering computing resources close to the data source, whether that be extrusion machines or cargo scanners.
As IoT deployments increase in the future–the global market for industrial IoT grew a whopping 22 percent in just 2021 — expect the demand for MEC to increase along with it.
Suggested reading: Edge Computing in IoT: 5 Reasons it’s Important
Stay connected with Hologram
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