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.

Only Committers for a project are eligible to vote for a project’s Project Technical Lead.


Please see Akraino Technical Community Document section 3.1.3 for more detailed information.


Committer

Committer

Company

 Committer Contact Info

Committer Bio

Committer Picture

Self Nominate for PTL (Y/N)

Prakash Siva

Radysis

psiva@radisys.com




Adnan SaleemRadisysadnan.saleem@radisys.com

Y

Bob Monkman

Ind.

bob.monkman@gmail.com



N
Mohammad Sabir HussainRadisysMohammad.Hussain@radisys.com

N

Presentation:

Use Case Details:

Attributes

Description

Informational

Type

New

 

Industry Sector

Telco Carrier Networks and Enterprises

 

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.

 

Business use cases

  1. Edge deployments at enterprises, entertainment venues, factory automation plants, public facilities where real time media processing required
  2. Edge media applications include multi-party conferencing, gaming, surveillance, IoT generated content, AR and VR applications
  3. Edge media applications requiring low latency and to overcome backhaul BW availability and costs being prohibitive 
  4. Real time media analytics with AI and ML based applications for high value and media monetization applications

 

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

  1. Low operation cost, with support for remote FCAPS management, and ONAP based zero-touch resource and service orchestration
  2. Typical 16U height OCP rack with similar power consumption, with minimal footprint of 2 compute nodes.
  1. Edge Media solution shall support POD level consolidated management (OSAM) and service level orchestration and LCM via ONAP.
  1. Zero touch provisioning, upgrades, fault and performance management KPI, and auto-scaling and auto-healing capabilities

 

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

 

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

 

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

 

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

 

Blueprint Family - Proposed   Name

Network Cloud, RT Cloud

 

Use Case

Real Time Edge Media Processing

 

Blueprint proposed

  1. Unicycle POD (4-6 servers, single 16U rack configurations)
  2. Tricycle POD (16U or 42U rack configurations, multi-rack)
  3. Cruiser POD (Multi-rack Core Network Configurations, with spine leaf fabric and ToR switch)

 

Initial POD Cost (capex)

Estimates (TBD)

  1. Unicycle POD (< 100K)
  2. Tricycle POD (< 200K)
  3. Cruiser POD (< 300K)

 

Scale

  1. Unicycle POD – 1 rack with < 6 servers
  2. Tricycle POD – Multiple racks, each with < 24 servers
  3. Cruiser POD – Multiple racks, each with < 96 servers

 

Applications

Edge Virtual Function Applications (reference)

  1. Edge deployments at enterprises, entertainment venues, factory automation plants, public facilities where real time media processing required
  2. Edge media applications include multi-party conferencing, gaming, surveillance, IoT generated content, AR and VR applications
  3. Edge media applications requiring low latency and to overcome backhaul BW availability and costs being prohibitive 
  4. Real time media analytics with AI and ML based applications for high value and media monetization applications

 

Power Restrictions

TBD

 

Preferred Infrastructure Orchestration

OS – CentOS or similar Linux, KVM

Under Cloud – Airship

OpenStack – VM Orchestration

Docker + K8S - Container Orchestration

VNF Orchestration - ONAP

 

SDN

OVS-DPDK, SR-IOV

 

Workload Type

VMs, Containers

 

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.