Project Technical Lead: Adnan Saleem. Elected 1/17/19.
Project Committers detail:
Initial Committers for a project will be specified at project creation. Committers have the right to commit code to the source code management system for that project.
A Contributor may be promoted to a Committer by the project’s Committers after demonstrating a history of contributions to that project.
Candidates for the project’s Project Technical Leader will be derived from the Committers of the Project. Candidates must self nominate by marking "Y" in the Self Nominate column below by Jan. 16th. Voting will take place January 17th.
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Please see Akraino Technical Community Document section 3.1.3 for more detailed information.
|Committer Contact Info|
Self Nominate for PTL (Y/N)
|Mohammad Sabir Hussain||Radisys||Mohammad.Hussain@radisys.com||N|
Use Case Details:
Telco Carrier Networks and Enterprises
Vast amounts of mobile/wireline data (predominantly video) is expected to continue to grow, particularly with 5G and IoT. Low latency, backhaul bandwidth restrictions/cost, and real time edge media analytics require media processing at network edges versus transporting all media to network core. Without the ability to process real time media at the network edges a number of new advanced applications would not be possible nor economically viable.
Business use cases
Business Cost - Initial Build Cost Target Objective
Initial build requires a small footprint POD with minimal fabric and management switch, 4+ compute nodes with optional GPU acceleration, local storage node(s), PSUs, rack, typically under $100K with SW
Business Cost – Target Operational Objective
POD platform SW and application level security vulnerability scanning and automated patching capabilities required
Media content security and user access authentication capabilities required
Depending on type of Edge Media application GDPR or other regulatory requirements may be applicable. NEBS may be required depending on deployment location and carrier network requirements
Depending on deployment location, a single half-height rack to multiple full-height racks at Edge DC or Edge CO locations may drive power and cooling requirements
Edge Media solution shall enable support for high density media processing via GPU or FPGA acceleration for advanced high density AI and ML applications and shall scale from single site to 100s in regional deployments to 1000s globally
Additional details on architecture and use cases documented in supplementary PPT
Blueprint Family - Proposed Name
Network Cloud, RT Cloud
Real Time Edge Media Processing
Initial POD Cost (capex)
Edge Virtual Function Applications (reference)
Preferred Infrastructure Orchestration
OS – CentOS or similar Linux, KVM
Under Cloud – Airship
OpenStack – VM Orchestration
Docker + K8S - Container Orchestration
VNF Orchestration - ONAP
Edge Media solution shall enable support for high density media processing via GPU or FPGA acceleration for advanced high density AI and ML applications.