The Hive Model: From now to Beyond Edge computing
There’s a slow but profound tectonic shift underway from centralized to distributed computing driven by the demands of IoT use cases. With each computing paradigm shift, the scale of usage increases by orders of magnitude. While mainframes supported hundreds of thousands of users, with PCs the number has increased to nearly 2 billion, and currently, the number of smartphones is approaching 7 billion. With the approach of the IoT era, the number of connected devices will increase to the tens of billions, possibly trillions.
With the explosion of Internet-connected devices that help us make sense of our world through data collection and analytics, we’re seeing a distributed model emerge with the rise of edge and mobile edge computing. Edge computing platforms operate closer to the network edge, and closer to things and data sources, integrating the capabilities of networks, storage, and applications. A report by IDC in 2015 predicted that by 2019, 45% of IoT created data will be stored, processed, analyzed and acted upon close to, or at the edge of the network.
In Connected Industry there’s constant evolution and innovation around architecture, connectivity and data analytics. We’ve seen the mainstream adoption of cloud computing to provide storage, processing and analytic capabilities for IoT generated data, yet mobile edge, fog, and edge computing and emerging as front-runners in response to the need to provide real-time data processing for manufacturing, transport and banking industries. As we prepare to usher in 5G and move towards IIoT 3.0, we are experiencing new challenges that require new solutions.
This presentation proposes that the solution is not edge vs cloud, but more of an aggregate model, encompassing the isolation of the edge, with the power of selective aggregation. I call these models Edge Hives- aggregations of processes that can operate autonomously and also can share/learn with other hives and clouds. They can be physical or logical aggregations and containers can be aggregated into hives. They provide a model around which one can build out solutions at the edge so that there’s a succession and each of them can have a different set of roles for the logical perspective.
This presentation will detail:
” How did we get to this point in technology?
” The incoming data deluge
” Edge and Cloud use cases
” The need for a new hive model and future predictions
Strategy Partner (Former CTO of IBM’s Watson IoT Platform ) at Momenta Partners
With extensive experience in executive management and technical leadership, Jim has held positions in areas of networking, IT systems management, and pervasive computing. He has a wealth of knowledge in IoT solution development including device connectivity, data collection, cognitive analytics, digital twin, and machine learning. He was most recently Distinguished Engineer and CTO for IBM’s Watson IoT Platform and is an IBM master inventor holding over 60 patents.
Enterprise, Small / Medium Enterprise, OEM
edge, IIoT, cloud, connected industry
CxO, VP / Director, Middle Management, Technical
Manufacturing, Industrials, Automotive
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