Model-driven & AI-Enabled Inter-Cloud Optimization Architecture - - PowerPoint PPT Presentation

Model-driven & AI-Enabled Inter-Cloud Optimization

Architecture and Benefjts Ramki Krishnan

Introduction

• What did we talk about so far?
• Model-driven & AI-Enabled Inter-Cloud Optjmizatjon
• 5G/Edge Computjng Use Cases – Dilip Krishnaswamy
• Let us talk about the architectural requirements

End-to-end Reference Architecture – ONAP Perspective

Discussion in Progress: Edge Automatjon Through ONAP WG (htups://wiki.onap.org/display/DW/Edge+Automatjon+through+ONAP)

*** This diagram is discussion in progress and not fjnal ***

Architecture - What do we need? (1)

• Centralized Resource Management/Optjmizatjon
• 1000’s of Clouds
• Probabilistjc Decisioning
• Multjple Solutjon Choices – Aggregate Data for scale, Data Collectjon tjme lag etc.
• Several Constraints, need fmexibility to easily add new constraints
• Cost (Partner Cloud, Private Cloud etc.), Service SLA (Latency etc.)
• Data Sources are ofuen Aggregates, examples below
• Partner/Public Cloud -- Cloud Region & Tenant Resource (Compute/Network/Storage) Available

Capacity & Utjlizatjon; Cloud Region Energy Utjlizatjon

• Private Cloud

– Above + Cluster Capacity/Utjlizatjon etc.

• Policies are ofuen sofu constraints, examples below
• Find Cloud Regions(s) with least resource/energy utjlizatjon, least cost etc.
• Automatjon Intelligence (AI) through Machine Learning (ML)
• Use ML (non-linear regression etc.) techniques on operatjonal data to predict the thresholds for

sofu/hard constraints

• Update the thresholds for sofu/hard constraints in a closed-loop operatjon

Discussion in Progress: Edge Automatjon Through ONAP WG (htups://wiki.onap.org/display/DW/Edge+Automatjon+through+ONAP)

Architecture - What do we need? (2)

• Edge Resource Management/Optjmizatjon
• 1-10 Clouds
• Accurate Decisioning
• Single Solutjon Choice
• Data Sources are Atomics, examples below
• Partner/Public Cloud -- Workload (VM/Container) Resource

(Compute/Network/Storage) Available Capacity & Utjlizatjon etc.

• Private Cloud

– Above + Host Capacity/Utjlizatjon etc.

• Inter-cloud latency, bandwidth etc.
• Policies are ofuen hard constraints, examples below
• Find Cloud Regions(s) with SR-IOV support
• Automatjon Intelligence (AI) through Machine Learning (ML)
• Same as Central Resource Management/Optjmizatjon
• Note: For some deployments, this functjon could be combined with the central component

Discussion in Progress: Edge Automatjon Through ONAP WG (htups://wiki.onap.org/display/DW/Edge+Automatjon+through+ONAP)

Resource Management/Optimization and Related Components

• Designer & Developer friendly Domain-Specifjc Modelling Language for Service Placement/Scheduling Policy
• Address Central/Edge Resource Management/Optjmizatjon Requirements
• Masks the Mathematjcal complexity of optjmizatjon algorithms through Modelling
• Flexibility to add Custom optjmizers especially for Edge Resource Management/Optjmizatjon
• Drive Service Creatjon Agility for 5G, Edge Computjng etc.

Discussion in Progress: ONAP Optjmizatjon Framework (OOF) -- htups://wiki.onap.org/pages/viewpage.actjon?pageId=3247288

Flexibility to add Custom Optjmizers

Model-driven Optjmizatjon Libraries – Minizinc etc. ML Component Use Operatjonal data to predict the thresholds for sofu/hard constraints Architectural Framework

Note: This is an exemplary architectural framework/implementatjon choice

Upcoming Talks

• “Recent Trends in Constraint Optjmizatjon and Satjsfactjon” -- Nina Narodytska
• “SCOR: Sofuware-defjned Constraint Optjmal Routjng platgorm for SDN” – Siamak

Layeghy

• Model-driven Minizinc applicatjon for constrained-based Routjng