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Edge Computing

Edge Computing

Need for speed: When the powers of 5G and Edge computing combine

The game's changed for compute and storage needs after Edge computing entered the scene; it moved these functions away from the cloud and closer to the source, processing data much faster. 5G networks showed a similar value proposition, where it facilitated data transfers faster than older internet generations. So, bringing these technologies together means ultra-fast speeds—and we mean single digit latencies… It’s like your built-for-speed car meets a speed-inducing fuel!
Although multi-access edge computing (MEC) in concept can work without 5G, 5G networks and enhanced speeds can bring new use cases to life across several industries that have zero tolerance for lags, including healthcare, industrial applications, automobiles, and security, among others, allowing for use cases such as robotic surgery, self-driving cars, online gaming.
To tap into this emerging market, edge and cloud infrastructure providers such as Microsoft, Amazon Web Services (AWS), Google, and HPE have been teaming up with several networking partners, including SK Telecom, Verizon, and AT&T to offer private and public 5G MECs for enterprises. Last year, we saw several partnerships like these taking place and expanding to geographies and markets, and we expect the same to continue. However, power and space constraints, as well as difficulties in optimizing investments per site, may play spoilsport in adopting MECs.

What is MEC?

Multi-access edge computing (MEC; formerly known as “mobile edge computing”) refers to a subset of edge computing where the compute and storage take place at the edge of the service provider’s network (such as through base stations, radio nodes, and aggregation points), rather than in traditional infrastructure, like servers in industrial premises, retail stores, smart homes, etc. MECs have a technical advantage over standard edge computing as its placed in close proximity to the RAN (radio access network), with lower latency access to devices connected to the network, rather than relying on the core network for compute and storage (latency can be reduced to single digit milliseconds from 10–20ms for edge computing in general vs 30–75ms for cloud).
In fact, MEC is a commonly accepted standard, though not a mandate, for technology to be considered “edge computing.” The criteria to meet the “MEC” standard were introduced by European standardization company, European Telecommunications Standards Institute (ETSI), which specifies the elements needed to create a standardized and open environment for apps so that they can be hosted in an MEC environment.
Although MEC can in theory work with several generations of mobile networks, the superior speeds of 5G enable several use cases, which rely on low latency and real-time analysis. So, we see many edge computing companies partner with telcos and 5G networking solutions providers to offer “5G MECs.” These partnerships combine the networking skills of telcos with the IT, storage, and computing solutions and infrastructure of edge/cloud computing players.

MEC facilitates faster responses by using edge servers near the RAN

Diagram 1_MEC
Source: Juniper Networks

What kind of MECs exist in the market?

Classifying MECs, like that of cloud and edge infrastructure, is based on the access individuals and enterprises have to the respective MEC. Public MECs are available to any customer, while private MECs are placed in business premises (such as campuses, factories, fulfillment centers, etc.), and as the name implies, remain private.

Private vs Public MECs

What is driving the use of MECs?

  • Ultra low latency: Given the data is processed closer to the source and the need for moving data to core locations is avoided, MECs can deliver ultra (or extreme) low latency, compared to traditional edge and cloud computing. As mentioned above, private MECs have reportedly delivered single-digit millisecond (ms) latency, while standard edge computing data center operators typically deliver around 10–20ms latency. 
  • Better management of bandwidth: MECs optimize and conserve network bandwidth, as less data reaches the core network to be computed and stored. In turn, this reduces network congestion. Bandwidth management is particularly useful for processing large volumes of data (such as high-quality videos) and delivering real-time analysis. 
  • Higher network reliability: Since the data compute and storage take place near the RANs, reliance on the core network is minimized, and therefore, the downtime of the core network may not impact data processing. Even further, applications can be hosted in multiple locations by using RANs.   
  • Greater data security and privacy: In addition to the higher security stemming from the data being processed closer to the source and needing less travel time, private MECs also minimize the use of public resources; in turn, enhancing data security and privacy.  
  • Operating cost savings: MECs can deliver operating cost savings since they avoid the need for setting up and maintaining data centers.  

MEC use case map

How are incumbents leveraging this trend?

Edge computing incumbents tend to partner with telcos and network providers to enable enterprises to deploy private and public MECs. Although the disruptors in our coverage typically don’t get involved in offering the MECs, they should benefit from the higher MEC adoption as it can increase the demand for IoT, edge application, and analytics softwares (typically offered by them).
Prominent incumbents in the MEC space include Microsoft, Amazon, Google, and HPE. Amazon has a wide range of cited use cases for its MECs, likely supported by offering both private and public MEC solutions, which opens the door to many use cases, such as autonomous driving and similar automobile solutions (which may not be possible through private MECs). However, despite having less cited use cases, Microsoft has partnered with several telcos to broaden the reach of its MECs across the globe, including partnerships in the Americas (AT&T, Lumen, and Verizon), Middle East (Etisalat), and the Asia Pacific (SK Telecom).

MEC products and stated use cases

Edge computing partnerships to offer MECs

Looking ahead, what’s next?

MEC adoption is likely on the rise, riding on the growing demands for these new user cases in the end industries in the coming years. This should lead to a further increase in partnerships between edge computing infrastructure providers and network providers, similar to those highlighted above, as companies look to broaden their geographic reach and product offering (launching private as well as public MECs). 
However, the adoption of MECs might face the following common constraints:
  • Power and space constraints: MECs may not have the same computing and storage capacity as the core network, which can limit its success in events of exceptionally high traffic. Although the bandwidth is optimized by processing data locally, in the event the local data volume is large, there could be computing constraints for MECs.
  • Challenges in optimizing investment per-site: MECs deal with multiple variables relating to its network traffic, such as the number of subscribers, traffic types, and demand, and therefore, provisioning the correct volume of resources becomes a challenge. Over or under provisioning is more likely for MECs over central networks, as each MEC node must be sized and scaled separately. 
  • Higher 5G implementation costs: Although operating costs are likely to decrease when using MECs over standard edge computing, the cost of implementing the 5G network may be higher because of infrastructure costs. These costs are dependent on the implementation method, with lower infrastructure costs meaning a possible trade-off concerning latency. Implementing MECs in the tower location itself remains the costliest — although it can deliver ultra-low latency as little as 5ms— while deploying MECs at RAN aggregation points and mobile cores can reduce costs, though increase latency north of 10ms.   

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