Manage Learn to apply best practices and optimize your operations.

Mobile edge computing promises wide variety of uses

Mobile edge computing can bring applications and data closer to the end user, resulting in lower latency and better data analytics for network virtualization and IoT.

Gaining traction from a variety of trends, including network virtualization and the internet of things, mobile...

edge computing provides traditional data center compute and storage functions at the edge of the network. As a result, it can reduce application response times for lower latency, increase reliability and improve wireless network security.

Mobile edge computing is based on the principle that moving compute and storage capabilities to the edge of the network improves application performance for end users and devices. It provides an alternative to the centralized data center model, which sends data to and from centralized data center facilities. Time-critical applications, including telemedicine, video and self-driving cars, don't work well in the centralized cloud model, which will subsequently drive mobile edge computing progress. However, mobile edge computing is still in the early stages of development and predeployment.

The goal of mobile edge computing is to process data closer to the original application source, thereby speeding response times to the end user. In addition, it sends data to the data center only when necessary, reducing the amount of data traffic on the network and reducing latency.

As a result of its advantages, a number of standards bodies, including Open Edge Computing and OpenFog, are working to provide mobile edge computing frameworks. These frameworks are still in early stages of development.

Applications that benefit from mobile edge computing typically have requirements for low latency and benefit from transmitting data locally for security reasons. Advantages of intelligence on the network edge include the ability to perform complex data analytics, real-time response and increased service agility.

Trends driving mobile edge computing

The key trends driving mobile edge computing efforts include network virtualization, pervasive video, 5G and internet of things (IoT). Let's take a closer look.

Network functions virtualization. NFV provides an architecture for transforming network applications from dedicated hardware to software running on standard compute platforms. Network applications that typically run at the network edge, including security, routing, access concentration and wireless edge base stations, will stay at the edge, running on local compute platforms. The platforms can be located at the customer site, on virtual customer premises equipment (vCPE), or at the service provider's edge location or point of presence. Leading service providers have deployed proven NFV technology and plan broader deployments, including vCPE deployments in 2017 and 2018.

Fifth generation wireless deployment. The fifth-generation wireless standard, or 5G, is an emerging standard for the deployment of next-generation wireless networks. It will rely on mobile edge computing to provide the intelligence on the network edge for complex traffic handling and routing. While 5G won't be widely deployed until 2019 or later, its architectural principles rely on edge compute capabilities to coordinate radio traffic and reduce application latency.

IoT and IoT gateways. As the term for a wide range of applications that connect devices to a centralized network or data center, the power of IoT resides in the connection of physical things to analytics to gain insights from device-generated data. For some IoT applications, mobile edge computing is needed to provide analytics, security, better performance and low latency. These applications, including self-driving cars, will suffer or become impractical if all IoT data must go through a centralized data center.

Video requirements. Video now comprises the majority of internet traffic, and its bandwidth requirements continue to grow rapidly. Delivering high-quality video is complex due to the amount of data, the number of video formats and standards, and the requirements for low jitter and latency. As mobile network providers push video applications that include live TV on mobile phones and real-time video conferencing, the need for compute at the edge of the network continues to increase.

Mobile edge computing challenges

Mobile edge computing is a powerful architecture, which provides significant benefits to deploy compute and analytics intelligence at the network edge.

Mobile edge computing is in the early stages of its technology evolution, and providers face a multitude of architectural choices in deploying it. Due to cost constraints, service providers need to be judicious about where and when they place edge computing devices or platforms. Because mobile edge computing technology is still unproven, service providers can't guarantee proven return on investment. Service providers are likely to start deploying mobile edge computing nodes in specific places, including in densely populated city locations, sports stadiums and at their existing aggregation points of presence.  The wide variety of use cases makes creating an application ecosystem to develop the specific software needed for mobile edge computing time-consuming, especially with a lack of standards-based deployments.

Diverse mobile edge computing supplier groups

The potential supplier pool for mobile edge computing is diverse and includes the following:

  • Network equipment suppliers, like Cisco, Ericsson, Huawei and Nokia;
  • IT providers, including Hewlett Packard Enterprise, Dell, IBM and Oracle;
  • Semiconductor manufacturers, like Intel and NXP; and
  • A wide variety of application, IoT and security software providers.

Conclusions and recommendations

Mobile edge computing is a powerful architecture that provides significant benefits to deploy compute and analytics intelligence at the network edge. Key drivers for its success will be the deployment of IoT applications that require high reliability and low latency, NFV, video and 5G. In the early stages of evolution, with architectures and standards still developing, mobile edge computing requires a range of IT, network and application providers to develop products that work well together in a specific ecosystem. Service providers must carefully examine the economics of mobile edge computing to understand the best locations and applications to deploy in to drive their revenues.

Next Steps

The pros and cons of an edge data center

Cloud and IoT merge with edge computing

Edge data centers may not be for everyone

This was last published in November 2016

Dig Deeper on Software-defined data center

PRO+

Content

Find more PRO+ content and other member only offers, here.

Join the conversation

2 comments

Send me notifications when other members comment.

By submitting you agree to receive email from TechTarget and its partners. If you reside outside of the United States, you consent to having your personal data transferred to and processed in the United States. Privacy

Please create a username to comment.

How long do you think it will take for mobile edge computing to become a reality?
Cancel
Like many technologies, I think that mobile edge computing will remain an idea until a compelling usage model that drives revenue arises.  The tradeoff is the economies of scale of cloud-scale data centers versus 50 ms (100 ms) roundtrip of latency.  For consumer applications, like tapping on your cell-phone these latencies are imperceptible.  For machine to machine (IOT) applications they can become much more important.  If the Internet becomes a communications medium for vehicle to vehicle communications then it will be critical to deploy edge computing.
Cancel

-ADS BY GOOGLE

SearchNetworking

SearchEnterpriseWAN

SearchCloudProvider

SearchUnifiedCommunications

SearchSecurity

SearchDataCenter

Close