Re-arranging content...and, cleaning up....
General Background
A broad set of transformations are taking place:
- Business transformation: OTT services, faster TTM, Monetization
- Technical transformation: QoE, ULL, SDN/NFV/OMEC integration, Edge Analytics, Big data, Virtualization, Automation, C->E, R->E
- Architectural transformation: 4 views “NORMA-like” Cloud, ECOMP, Flexible architecture (RAN, Core, CDN, Application delivery, Automation, IoT, fog,..)
- Industrial transformation: ICT&E
To efficiently and effectively deploy 5G network supporting ultra low latency and high bandwidth mobile network, we need to deploy variety of applications and workload at the edge and close to the mobile end user devices (UE or IoT). That would include various virtualized RAN and core network elements, content (video), various applications (AR / VR, industrial automation, connected cars, etc.). We might deploy near-real time network optimization, customer experience / UE performance enhancement applications at edge. Edge cloud must support deployment of third party application (e.g. Value added optional services, Marketing, Advertising, etc.). We must deploy mechanisms to collect real time radio network information, process them in real-time (e.g. Geo Location data), summarize, anonymize, etc. and make them available to third party applications deployed at the edge or central location or outside service provider environment. Edge data collection could also be used for training machine learning models and fully trained models can be deployed at the edge to support network optimization.
The need
End users and other devices, cyber-physical systems will benefit from a broad set of context information that can enhance and enrich the delivery of a broad set of applications.
Service Deployment Goal
Deliver Application SLAs while minimizing TCO.
Application Profiles
| No | Application Classification (based on required RTT) | Application Examples | Network / Service Behavior Type | Deployment Component/ APIs | ONAP Managed | Edge Deployment Hard /Soft Constraint (Based on RTT) | Potential Application Provider | Casablanca Candidate | Additional Information |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Real-time (20ms -100ms) | In service path optimization applications which run in open CU-CP platform (also known as RAN Intelligent Controller, or SD-RAN controller). | Real-Time Network State Control | Open 5G CU-CP (CU - Control Plane) – VNFC. | Yes | Hard | NF Vendor/Service Provider/3rd Party | Yes | These applications include load balancing, link set-up, policies for L1-3 functions, admission control and leverage standard interface defined by oRAN / xRAN between network information base (or context database) and third party applications. Data collection through is B1 and implemented using x technology. |
| 2 | Near-real-time (500ms and above) | Slice monitoring, performance analysis, fault analysis, root cause analysis, SON applications, Optimization (SON Drive Test Minimization etc.), ML methodologies for various apps. | Network Analytics & Optimization | DCAE | Yes | Soft | NF Vendor/Service Provider | Yes | |
| 3 | Near-real-time (500ms and above) | Video Analytics, Video Optimization, Customer geoLocation information, Anonymized customer data etc. | Workload Analytics, Optimization & Context processing | Cloud Edge or Cloud Central | No | Soft | 3rd Party | NA. Out of scope for ONAP | The apps are OTT and the service provider is offering their infrastrcture as a service to OTT providers. |
| 4 | Real-time (10-20 ms) | Third party applications that directly interacts with the UEs, like AR/VR, factory automation, drone control, etc. | Workload Automation / AR-VR / Content, etc. | UE or Cloud Edge | No | Hard | 3rd Party | NA. Out of scope for ONAP. | These are third party applications, developed by enterprise customers (e.g. factory automation) or content creators (AR/VR applications). In this case, messages or requests or measurements directly go from UE (via UPF or GWs) to the applications and applications respond back. |
| 5 | same as 3) | same as 3) | Value Added Services + same as 3) | same as 3) + MEC/Cloud APIs (Note 1) | Yes | same as 3) | same as 3) | Stretch | Service Provider could be oferring video surveillance (video analytics/optimization apps etc.) as a service to enterprises. |
| 6 | same as 4) | same as 4) | Value Added Services + same as 4) | same as 4) + MEC/Cloud APIs (Note 1) | Yes | same as 4) | same as 4) | Stretch | Service Provider could be oferring factory automation as a service to enterprises. |
Note 1: API Details
- e.g., MEC APIs - Location info, Radio control info etc.
- e.g., Cloud APIs - IaaS/PaaS + Context Awareness (time, places, activity, weather etc.)
Edge Infrastructure
This diverse work load will require somewhat heterogeneous cloud environment, including Graphical Processing Unit, highly programmable network accelerators, etc., in addition to traditional compute, storage, etc.
To support edge deployment, we need:
1) Rich information / data model to discover and capture hardware resources deployed at the edge and request right type of resource to meet unique application needs.
2) Must support workload deployment options such as VM, Container (e.g. Kubernetes) on VM or bare metal
3) Must support a very small foot print to an edge location supporting a metropolitan area with verity of workload deployment
4) Edge cloud could be on customer premises – Factory automation
5) Must provide efficient network infrastructure that support slicing and QoS configuration options to meet various mobility services need
6) Must support policy driven auto recovery / scale up scale down
Edge Infrastructure Profiles
( example based on Akraino Edge Stack..but, need to generalize)
| Profiles | Workloads | Compute | Networking | Storage | Control | Security | Edge Application Infrastructure |
|---|---|---|---|---|---|---|---|
| Large | Support for VMs and containers. Commentary:
| >50 Compute Servers Accelerators: SRIOV based QAT for Crypto and Compression acceleration. ML/DL Accelerators Compute profiles: Fixed number of profiles are expected to be supported. (Will add profiles) | SRIOV Networking for High performnace Data plane VNFs. vSwitch (OVS-DPDK) based networking for all other workloads Multiple leaf switches and two spine switches WAN - Underlay :
Underlay realization options
Overlay realization options
IPv4 and IPv6 support NAT44 with LSN (Large Scale NAT) support by providers. Support for dedicated public IP addresses Commentary: Network sharing among container and VM workloads will need to be supported. DVR (Distributed Virtual Routing) for forwarding packets locally among vSwitch based networks. Leaf/Spine switches for forwarding traffic among SRIOV based networks and for networks between vswtich and SRIOV based networks. Few fixed profiles for following:
| Block device support using Ceph Dedicated nodes for storage ( 3 nodes ) Storage profiles representing whether the nodes are dedicated for storage, use compute nodes for storage, Number of nodes for storage etc... Is support for Object storage required in Edges? | Dedicated nodes for control stack Automation Offload Platform (Offloading ONAP) at the Edge. Few control profiles
Automation Offload Platform profiles consists of following:
| Transport : TLS 1.2 and above between ONAP and Edge Services Infra Security: TPM 2.0/SGX for private key security and secret/password protection, Remote attestation to detect any software tampering of compute, storage and control nodes. | MEC Platform as a VNF to provide contextual information to Edge applications. |
| Medium | Same as above | Same as above. Number of compute nodes are >10 and < 50 | Same as above | Same as above, except that there is no dedication of nodes to Ceph cluster | Same as above with respect to control, but Automation Offload Platform is not part of the Edge. No dedicated control nodes. Control functionality is shared with compute nodes. Support for K8S profile as it can support both VMs and containers | Same as above | Same as above |
| Small | Same as above, but may support very less number of tenants | Same as above. Number of compute nodes are < 10 | Same as above, but no PE and CE at the Edge. Fabric itself acts as CE. | Same as above, no dedication of nodes to Ceph cluster | No control at the Edge No Automation offload platform at the Edge Regional sites are expected to provide control and AOP services. Support for K8S based control. | Same as above | Same as above |
Edge Infrastructure Profile Summary
ONAP Activity Goal #1: ONAP requires IaaS/PaaS attributes (see ongoing work – Distributed Edge Cloud Infrastructure Enablement in ONAP, 5G Items for Casablanca) from Cloud providers for Infrastructure profiles that allow Distributed, Highly-secure, Config/Cloud-diverse, Capacity-constrained and Peformance/Isolation-aware
- Distributed
- 1000's of edge locations of varying capacity
- Casablanca - Implementation
- 10-100 edge locations (simple starting point)
- Peformance-awareness
- GPU, FPGAs, SR-IOV etc.
- Casablanca - Implementation
- SR-IOV desired for Data Plane (5G CU-UP)
- NIC offload desired for tunnel encap/decap e.g. 5G CU-UP GTP tunnel
- Resource Isolation through fine-grained QoS
- Support both Latency-sensitive and General purpose applications
- Support ONAP Management plane components in the same cloud with Workloads
- Casablanca - Implementation
- Min/Max resource reservation model desired
- Security
- Workloads are often deployed in external (non-dc-type) locations and need HW security (TPM etc.)
- 3rd party applications which need additional HW security (VM, Containers in VM etc.) and SW security (Inter-component TLS etc.)
- Casablanca - Implementation
- Edge Clouds with private IP addresses, i.e. reachable via private connections
- For example, edge cloud in a public cloud provider reachable via AWS direct connect or Azure express route or Google partner interconnect
- Capacity constraints
- Very small footprint (few nodes per physical location), Medium footprint (10's of nodes per physical location), Large footprint (100's of nodes per physical location)
- Casablanca - Deployment
- Need number of cores per servers; Need storage capacity/pool
- Cloud Diversity
Private and Public Cloud Providers
- Casablanca - Implementation
- Note: ONAP currently supports private edge clouds based on VMware VIO, Wind River Titanium Cloud, Upstream OpenStack
- Desire to have at least one Public Cloud Provider (Azure, AWS, GCE etc.) as an Edge Cloud Provider
- ONAP central instantiates an Edge Cloud instance (blue cloud provider in gliffy) via a IaaS API to cloud provider
- ONAP central instantiates one or more ONAP edge components as need, e.g. DCAE
- ONAP central instantiates one or more NFs, e.g. 5G CU-UP/CP
- Configuration Diversity
- 5G Factory Automation, 5G General Mobility Services etc. – User Plane components (DU, CU-UP, UPF etc.)
ONAP Edge Automation
ONAP Activity Goal #2: Define hierarchical ONAP Central/Edge Architecture/functional interactions (API reference points) to support aforementioned Application/Infrastrcuture profile in Any "Cloud" (internal Business Unit or external Partner) at Any "Location" edge, regional or central.
(May 9th call / Ramki Krishnan attended OOM call and captured feedback) - Keep it Simple Stupid (KISS)
- Suggested Approach - Separate ONAP-edge Instance per 'edge domain', (ie., separate from onap-central instance, of course)
- Note: Independent of any Edge CP's Orchestration components.
- SP uses a central-OOM with a 'policy' for deployment of an onap-edge instance, e.g., xyz edge provider with abc components, etc.
- However, onap-edge instance can be 'lighter weight' with subset of components needed (per MVP discussed below)
- Desirable to managed as a separate K8s cluster (ie., separate from onap-central instance, of course) and, only for onap-edge use, ie., don't use for other 'workloads' like network apps or 3rd party apps
- Use External API framework to exchange requests/responses, e.g., summarize data over longer (such as 60-min) intervals vs detailed over shorter (such as 1-min) intervals, etc., between ONAP-central and ONAP-edge instances
Details:
- Optimal Distribution of Intelligence and Control, includes distributed data collection and localized processing of intelligence
- Support for various edge sizes
- Scaling needs - Hierarchical federation (over and beyond auto scale-out of ONAP services) - Distribution of orchestration, fabric control, stats/faults/log collection and distributed processing of same (Regional Controllers)
- Optimal placing of edge applications. For example placing edge applications in the best edge(s) considering various constraints (e.g Proximity to end user, Radio/BW availability, cost, accelerators availability - HPA, Geo-affinity regulations, trusted infrastrcture of edge, device characteristics and resource availability to take up load etc...), Auto creation of constraints is one requirement.
- Providing contextual information to application services after gathering information from 5G network functions.
- Autonomic Control, Management and Operations of distributed service chains
- Traffic steering to the right edge applications (e.g Programming UE classifier of UPF) and dynamic SFC within VNFCs of edge application.
- Supporting various workload types (VMs, Containers etc.)
- Deploying IoT specific infrastructure software in edges such as EdgeXFoundry.
- Supporting multi-tenancy to place workloads in Edges belonging to various organizations
- Performance determinism and high throughput edge
- Securing confidential information/keys/secrets and detecting any software tampering at edges
Few examples: on scaling - OOM based scaling may not be good enough and there may be a need to offload some ONAP functionality to regional level as the target number of edge clouds could be in tens of thousands. Also, to reduce amount of data to central ONAP services for analytics, there is a need for offloading DCAE functions to regional level, which could involve identifying real time data sources, collecting and analyzing the data and disseminating output data to central ONAP function. Controlling fabric (L2/L3 switches in edge-clouds and WAN links) is another function that may require offloading some ONAP SDNC functions to regional sites.
ONAP Hierarchical (Central/Edge) Architecture
ONAP Hierarchy - Single Provider - Architecture
- Cloud Provider Business Unit:
- Provides hosting of Workloads, ie., IaaS/PaaS
- SP installs and manages ONAP in separate 'Management Cloud' instances
- SP installs and manages Network Services + 3rd Party Apps in separate 'Services/Apps Cloud' instances
- Provides hosting of Workloads, ie., IaaS/PaaS
- Cloud Provider Business Unit:
- Provides SaaS, eg., Analytics/Closed Loop as a Service, LCM of Apps, etc.
- ONAP Edge may not be needed
- Cloud Provider Business Unit:
- Types of virtualized cloud resource tenant and their characteristics
- Virtualized Network Workload Cloud Resource Tenant Category
- Network Management Cloud Resource Tenant Category
- Virtualized Application Workload Cloud Resource Tenant Category
- Application Management Cloud Resource Tenant Category
- Physical Network Function and their characteristics
- Part of Edge Cloud Orchestrator
- Types of virtualized cloud resource tenant and their characteristics
- Immediate interest to ONAP for 5G use cases
- Virtualized Network Workload Cloud Resource Tenant Category
- Guaranteed
- Burstable (with minimum guarantee)
- Best Effort
- Network Management Cloud Resource Tenant Category
- Burstable (with minimum guarantee)
- Virtualized Network Workload Cloud Resource Tenant Category