Edge computing is supposed to help deal with the deluge of data coming from sensors, phones, cars, factories and such. Processing data near where it is created will help with issues of latency and the challenges of moving large datasets back to a central cloud. It will, but developers will need to build a strategy for orchestrating the movement and availability of data for their applications.
By way of analogy, a symphony could play Beethoven on their own, but audiences always see a conductor leading the symphony. The conductor has a key role in ensuring that the musicians play the right notes at the right time – he or she interprets the music and sets the tempo. Without a conductor, precision gives way to chaos. Similarly, developers have to have data in the right place, at the right time, or else find their applications are giving inaccurate responses or crashing.
Edge Research Group has examined the issue of data orchestration in a new whitepaper entitled “Orchestrating Data in the Age of Edge Computing: Challenges and Opportunities”. The paper, commissioned by Kmesh.io and Edge Gravity by Ericsson, addresses many critical issues surrounding data provisioning for edge applications, including but not limited to, the following:
· Data needs of stateful vs. stateless Edge applications
· Data needs for Edge analytics
· Defining Data Orchestration under the Edge Computing paradigm
We focused on the data analytics use case for edge computing in this research, but in all of these cases where an application is stateful, what emerges is a need for data management – tools or services that move data to where it is needed, when it is needed. Such tools or services need to automate the movement of data and workloads across distributed resources based on policies for security, performance, and cost.
As discussed in the paper, management of data is getting more difficult as the volume of data needed for insights and operations of an enterprise continues to grow exponentially. Edge computing is emerging as a way for enterprises to leverage data more efficiently. However, architecting infrastructure for edge computing is not without its own challenges – data orchestration being chief among those challenges.
For more information, you can download the report here.