A Horizontal Taxonomy for the Industrial IoT
3.3
Data Item Scale
Metric: More than 10,000 addressable data items
Architectural Impact: Selective delivery filtering
Scale is a fundamental challenge for the IIoT. It is also complex; there are many dimensions of
scale, including number of “nodes”, number of applications, number of developers on the
project, number of data items, data item size, total data volume, and more. We cannot divide
the space by all these parameters.
In practice, however, they are related. For instance, a system with many data items probably has
many nodes.
Figure 6: IIoT Applications with Many Data Items
IIoT systems often produce far too much data to send
everything to every possible consumer. “Gust control” in
a wind turbine farm, for instance, needs weather
updates from the turbines immediately “up wind”, a
specification that changes with time. Traffic control
systems are very interested only in vehicles approaching
an intersection. These applications require the
architecture to provide selective data availability, so only
the right information loads the network and the
participants.
Despite the broad space, we have found that
two simple metric correlate well with
architectural requirements. The first is
addressable “data item scale”, defined as the
number of different data instances that could
be of interest to different parts of the system.
Note that this is not the same as the size of a
single large data set, such as a stream of data
from a single fast sensor. The key sca le
parameter is the existence of many different
data items that could potentially be of
interest to different consumers. So, a few fast
sensors create only a few addressable data
items. Many sensors or sources create many
data items. A large number of addressable
data items implies difficulty in sending the
right data to the right place.
When systems get “big” in this way, it is no
longer practical to send every data update to
every possible receiver. We find the
challenge is significant for as few as 100 data
items. It is extreme for systems with more
than 10,000 addressable data items. Above this limit, managing the data itself becomes a key
architectural need. These systems need an architectural design that explicitly understands the
data, thereby allowing selective filtering and delivery. There are two approaches in common use:
run-time introspection that allows consumers to choose data items themselves, and “datacentric” designs that empower the infrastructure itself to understand and actively filter the data
system-wide.
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December 2015