Will 5G accelerate edge computing as a service?
Mobile edge computing has been talked about for a couple of years now as the way to bring cloud computing downstream to the local processing needs of a variety of compute intensive and privacy concerned workloads. But without 5G, is it just wishful thinking? And can network operators create a market for their implementation of edge computing as a service?
Bringing compute resources to the edge means reducing the amount of data that needs to be sent up to the cloud. Indeed, for many data-intensive applications like video surveillance/security, natural speech recognition, health/wellness related monitoring, etc. the need to process data as close to the source as possible is mission critical.
The reasons are multifaceted but boil down to three primary issues: keeping the networks clear of massive amounts of data needing to be sent up into the cloud, keeping latency low to allow rapid response as the data is processed locally, and privacy concerns that are alleviated (mostly) by keeping data processing as local to the data source as possible thus eliminating massive amounts of potentially hackable personal data from being stored in the cloud.
Mobile edge computing has been talked about for a couple of years now as the way to bring cloud computing downstream to the local processing needs of a variety of compute intensive and privacy concerned workloads. But without 5G, is it just wishful thinking? And can network operators create a market for their implementation of edge computing as a service?
Bringing compute resources to the edge means reducing the amount of data that needs to be sent up to the cloud. Indeed, for many data-intensive applications like video surveillance/security, natural speech recognition, health/wellness related monitoring, etc. the need to process data as close to the source as possible is mission critical.
The reasons are multifaceted but boil down to three primary issues: keeping the networks clear of massive amounts of data needing to be sent up into the cloud, keeping latency low to allow rapid response as the data is processed locally, and privacy concerns that are alleviated (mostly) by keeping data processing as local to the data source as possible thus eliminating massive amounts of potentially hackable personal data from being stored in the cloud.
There are many ways to segment cloud computing so that a localized processing capability can be deployed. Indeed, products like Microsoft’s Azure Stack, Google Cloud IoT Edge and AWS’s IoT Greengrass are designed specifically to run in an edge environment while maintaining all of the features and functions familiar to cloud users (e.g., cloud management functions, analytics, etc.).
By running at the edge, these services can exhibit near-real-time performance with minimal latency, operate with intermittent or no wide area network connectivity, enable secured data transmissions and interactions, and run on edge computers scaled for the particular needs of the application, from small to large server implementations. This flexibility is critical to many implementations of IoT solutions like smart cities, security, mission critical healthcare, autonomous vehicles, etc.
What’s become a debate lately is just where these edge servers should be placed. Network operators (e.g., AT&T, Verizon, Vodafone), looking to take maximum advantage of their move to network function virtualization (NFV) in their core networks, which is implemented with general purpose computers as opposed to past generation of specific purpose-built devices, want to convince customers to use their systems for their edge computing needs.
