Continuous Intelligence for 4G/5G Mobile Edge Computing – RTInsights

Combining compute, connectivity, and low latency communication to endpoints lets MEC-hosted applications analyze streaming data on-the-fly and respond before storing it.

Next-gen mobile networks can deliver powerful services that take advantage of low-latency, high bandwidth, and edge computing. This article describes a software architecture for continuous intelligence applications that operate in 4G/5G Mobile Edge Computing (MEC) environments.

MEC offers low latency computing co-located with wireless
base-stations and close to fixed networks. It interfaces directly to the
provider Radio Access Network (RAN), which manages connectivity to wireless
devices. MEC environments are also richly connected – to the provider’s core
network, the Internet, and public and private clouds. As a result, MEC offers a
unique opportunity to mobile providers to deliver valuable services that use continuous intelligence to analyze, learn, and predict from streaming
data, on-the-fly, and respond in real-time.

See also: Why Businesses Are Implementing Edge Analytics in Their Line of Work

Continuous intelligence enables applications to analyze, react (and then store). Valuable, low latency responses are computed on-the-fly in the MEC context, but data storage occurs later, often in a public cloud or on-prem. This simplifies application architecture and compliance because data is analyzed on-the-fly but not stored in the MEC. Continuous intelligence capabilities should be thought of as supplemental to cloud computing services jointly offered by operators and cloud service providers. To deliver continuous intelligence capabilities to applications, providers need to add a data processing layer that is stateful, and that allows applications to continuously compute, driven by data using a stateful paradigm that is best thought of as a “stateful functions as a service” paradigm.

Mobile + Edge Computing = Opportunity

Continuous Intelligence offers mobile providers the ability
to turn MEC into a powerful revenue generation platform. It exploits proximity
to devices to compute on high-volume data with low latency, and can deliver
insights a million times faster,
and at a tenth of the cost of
on-prem or cloud-based applications.

MEC is optimized for mobile
use cases
for edge computing and is documented in an ETSIstandard. Applications in the MEC environment
can take advantage of its rich connectivity and access to the Radio Access
Network to discover powerful insights that are impossible to find in a
general-purpose edge computing environment. The RAN manages services for all
devices and using a continuous intelligence platform enables applications to
gather a dynamic understanding of the real-world context of each device in
real-time, and is a foundation for powerful new services:

  • Carriers can enhance their network services on the fly,
    dynamically maximizing connection quality, assigning resources to devices or
    network slices, identifying faults, delivering quality of service guarantees,
    ensuring privacy, and enhancing security.
  • Customer-facing MEC applications serve both enterprise
    and consumer needs:

    • Industrial
      : MEC applications can help predict equipment failures, detect
      problems, optimize supply chains, manage inventory, and customize production.
    • Augmented
      (AR) and virtual reality (VR) applications help workers understand
      the environment around them and repair or undertake work that relies on the
      dynamic fusion of visual and digital information.
    • Retail: MEC
      is required to deliver immersivein-store environments that require low
      latency, proximity, and personalization and offer new forms of payment.
    • Security and safety: mobile device location of
      one or more users can give emergency teams new tools to help in their response
      to emergencies.
    • Smart cities:
      Applications can dynamically predict future traffic load and gauge public
      transit needs for citizens, tailoring each user’s experience to their own
      travel plans. Information from sensors in utility infrastructure, fused with
      information from mobile devices, can help predict energy demands and control
      user environments based on personal preferences.

MEC has a powerful advantage over general-purpose edge
computing, namely its proximity to the RAN, which offers low latency access to
information about mobile devices. Dynamically building a picture of the
real-world allows these applications to react in real-time to complex edge

Exploiting MEC

The juxtaposition of compute, rich connectivity, and low
latency communication to endpoints makes it possible for MEC hosted
applications to analyze streaming data on-the-fly and respond before storing, so responses can be
delivered a million times faster than with a traditional “store then
analyze” approach that is hampered by database latency. Using this
approach, insights are computed immediately in-memory, in context of a rich
contextual awareness that helps to identify relationships – such as proximity,
correlation, containment, and more – that enables deep insights. Continuous
intelligence applications

  • Continuously
    analyze, learn, and predict, driven by data
    : Each update is statefully
    processed immediately in-memory. This improves performance.
  • Securely analyze
    behavior over time:
    This helps applications discover deep insights.
  • Analyze in
    : Applications can discover hidden meaning in data, including real-world relationships – like
    containment, proximity, and correlation between different sources
  • Always have the
    : Algorithms analyze, estimate, and predict continuously from
    boundless data streams, as data arrives, rather than relying on batch-based

To deliver these benefits to
developers, the MEC needs to support an application platform that continuously
updates a live model of the edge environment and its endpoints that can be
securely used to deliver powerful new services and to enhance carrier networks.

MEC is not just a “Closer Cloud”

The stateless, RESTful
microservice-and-database architecture that has been so successful in the cloud
needs to be enhanced with a stateful processing model. Accessing a database to
gather state for analysis is a million times slower than stateful in-memory
analysis at CPU speed. A stateful computing architecture is key. Although there
is a trend toward faster in-memory databases, they don’t run applications and can’t
continuously find relationships
between entities. Other approaches, such as
event streaming, can only act as a buffer between the real world and
applications. Reasoning about the meaning
of a set of events requires a stateful system model that captures the states of
all entities that the application needs to analyze.

Building a “LinkedIn for Things” in the MEC

It’s important to recognize that it is the continuous,
concurrent state changes of data sources that are critical for
situational analysis. Moreover, continuous intelligence needs to express fluid
relationships, unlike traditional databases that capture fixed relationships (trucks have engines). Dynamically computed relationships (a truck “with bad braking
behavior” “is approaching” an inspector) are typical in a fluid environment with mobile devices.
Insights such as “bad braking behavior” and “is
approaching” require continuous, stateful analysis to dynamically
determine how the application should respond, moment by moment. The analysis
must fuse real-time GPS information with static data – the real street map. The
application must respond immediately, in context (an inspector on the same
street and ahead of the truck should be alerted, but not others). Since there’s
no point telling an inspector to stop a truck that has already passed,
responses must be real-time.

The flow of continuous intelligence and automatic responses
must be driven concurrently for every truck and inspector. Tens of millions of
evaluations may need to be executed concurrently – for each “thing”
and its current relationships.

The dynamic nature of the relationships between data sources
suggests that we should represent the environment as a graph in the digital
domain. Graph databases are used in social networking apps, but their graphs
change relatively slowly. A continuous intelligence graph needs to be fluid –
relationships are inherently dynamic – so computation must occur in the graph, in memory, driven by the
arrival of data. The analysis must occur in the context of the current state of and relationships between sources
in (the ever-changing) graph of relationships rather than “over (a pre-built) graph.”

Continuous intelligence demands stateful in-memory
processing to optimize performance and to enable continuous computation for
real-time responses. It embraces event streaming and other infrastructure
patterns, focusing instead on the application layer capabilities needed to
develop and operate stateful applications that consume streaming events at
scale. Although modern databases can store streaming data for later analysis,
and update relational tables or modify graphs, continuous intelligence drives analysis from the arrival of data
– using an “analyze, react, then store
architecture that builds and executes a live computational model from streaming
data. Whereas streaming analytics applications use a top-down
visualization/user-driven control loop, continuous intelligence applications
continuously compute and stream insights, deliver truly real-time user
experiences, and facilitate real-time automatic responses at massive scale.


MEC offers an opportunity for mobile providers to deliver
continuous intelligence services to their customers and to optimize their own
network operations. Rather than focus on the delivery of the same kinds of
applications that customers can deliver from IaaS clouds, providers should take
advantage of key attributes of MEC: low latency, data volume reduction, and
rich interconnection to networks and services.

Most importantly, MEC is uniquely situated to identify dynamic relationships between data sources, including proximity and correlation, to surface insights and respond in real-time to real-world needs. Cloud applications that provide value by analyzing and deriving value from streaming or real-time data can adopt continuous intelligence alongside MEC to offer valuable new services to customers. These services can take advantage of the benefits provided by 5G and stateful computing to compete in today’s fast-moving markets and enable new opportunities for network resilience and business continuity.

This UrIoTNews article is syndicated fromGoogle News