Project Technical Lead: Adnan Saleem. Elected 1/17/19.
Project Committers detail:
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Committer | Committer Company | Committer Contact Info | Committer |
Bio | Committer Picture | Self Nominate for PTL (Y/N) |
Prakash Siva |
Radysis | |||||
Adnan Saleem | Radisys | adnan.saleem@radisys.com | Y | ||
Ind. | N | ||||
Mohammad Sabir Hussain | Radisys | Mohammad.Hussain@radisys.com | N |
Presentation:
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Use Case Details:
Attributes | Description | Informational |
Type | New |
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Industry Sector | Telco Carrier Networks and Enterprises |
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Business driver | 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. |
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Business use cases |
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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 |
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Business Cost – Target Operational Objective |
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Security need | POD platform SW and application level security vulnerability scanning and automated patching capabilities required Media content security and user access authentication capabilities required |
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Regulations | 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 |
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Other restrictions | 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 |
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Additional details | 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 |
Case Attributes | Description | Informational |
Type | New |
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Blueprint Family - Proposed Name | Network Cloud, RT Cloud |
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Use Case | Real Time Edge Media Processing |
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Blueprint proposed |
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Initial POD Cost (capex) | Estimates (TBD)
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Scale |
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Applications | Edge Virtual Function Applications (reference)
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Power Restrictions | TBD |
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Preferred Infrastructure Orchestration | OS – CentOS or similar Linux, KVM Under Cloud – Airship OpenStack – VM Orchestration Docker + K8S - Container Orchestration VNF Orchestration - ONAP |
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SDN | OVS-DPDK, SR-IOV |
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Workload Type | VMs, Containers |
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Additional Details | Edge Media solution shall enable support for high density media processing via GPU or FPGA acceleration for advanced high density AI and ML applications. |
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