Key Takeaways From SDX Central's 2017 “Innovations in Edge Computing and MEC” Report (Part 2)
by Brad Johnson, on Nov 7, 2017 10:53:23 AM
IOT technologies are helping decision-makers to quantify new insights from industrial equipment, workers, buildings, and other sources. These real-time edge data insights have the potential to reduce operating costs, improve efficiency, or deliver new capabilities. As businesses increasingly turn to IOT and analytics technologies in order to maximize the efficiency industrial environments, it’s no surprise that Edge Computing has become one of the more hyped buzzwords of 2017. With massive volumes of data being generated by connected sensors, the Edge Computing model has proven to be an important building block for the industrial analytics applications of the future.
Previously, in Part 1of this series, we reviewed some of the benefits of deploying Edge Computing in industrial contexts. In Part 2 of this 3 Part series, we’ll focus on these use cases where Edge Computing can deliver the most value. SDX Central’s “Innovations in Edge Computing and MEC” report cites “analytics, compliance, security and Network Functional Virtualization” as the four primary use cases for adopting Edge Computing. In this post, we’ll explore why SDX Central groups Edge Computing applications in this way, and what that means about industrial data applications in the future.
This is the largest class of Edge Computing applications. SDX Central explains that “the edge collects a large amount of data about users, network conditions, local context, consumer behavior etc…sending all of this data to the core may be counter-productive for two reasons: high latency and wasted bandwidth.” Edge Computing provides a more efficient alternative to centralized processing, allowing applications to distribute the burden of processing raw data among edge nodes. “In many closed loop automation situations, e.g performance degradation requiring corrective action within milliseconds, letting a centralized application drive closed-loop automation is just not practical.” Edge Computing provides the means for ultra-low latency analysis of sensor data locally, enabling control system to respond before data is transmitted to centralized applications. Moreover, Edge Computing enables the reduction of massive volumes of data at the source. Though “the amount of data generated by edge devices and functions can be substantial when you consider items such as log files...it is much more cost effective to run analytics on the edge and send small batches of condensed information to the core.” Analytics applications for Edge Computing “may include a range of activities such as event correlation, big data applications, machine learning etc.”
In many industries, analytics systems are implemented specifically for compliance purposes. “Compliance consists of a broad variety of applications that could range from copyright enforcement to geographical data placement,” states SDX Central. For example, “copyright enforcement comes in play during concerts, plays, sports events etc. where an audience member does not have the rights to transmit the event video via their cell phone. An edge application could either disable the upstream transmission completely or reduce the resolution to make the transmission compliant.” Another major compliance concern for decision-makers is data locality, or the physical, geographic location of data. “Geographic placement comes in when, by law, a certain piece of data has to reside in a particular geography. Edge applications can enforce these laws,” as individual edge nodes can be configured to store their own data locally, and make it available to centralized applications without needed to store in a central database (which may be outside an approved area).
4. NFV (Network Functional Virtualization)
Edge computing can also benefit network virtualization implementations. As SDX Central points out, though “Network Functional Virtualization (NFV) is not an edge application, strictly speaking...access virtual network functions (VNFs) such as vRAN, C-RAN, vCMTS, vOLT need to run at the same location as edge computing, making it highly desirable to run both VNFs and edge applications on the same if not similar infrastructure. For this reason, the ETSI MEC approach is highly synergistic to the ETSI NFV approach.” Edge computing’s benefits are not limited to proximity applications, as Edge Computing provides an environment that serves the purposes of NFV implementations, and therefore improves operations for the the entire network.
Learn how SWIM can empower industrial applications by using Edge Computing to optimize and alleviate the sensor data overload. Swim ESP can help transform and manage sensor data using Machine Learning on edge devices.