DRAFT PROPOSAL FOR COMMENTS

The Beijing release is the first release of the OOF project and has the following key objectives in this release:

1. Support ONAP use cases for Homing/Placement of VNFs and Services via the Homing and Allocation Service (HAS).

2. Demonstrate the declarative, model-driven functionality of OSDF via representative applications aligned with ONAP use cases

3. Provide a library of OSDF components/adapters for building new optimization applications

4. Ensure compliance of OOF with platform maturity requirements. 

Additionally, the following stretch goals are also being pursued:

1. Provide foundations of components required to support broader use cases that are targeted for R2+

2. Exploratory efforts that are in the scope of near-term and longer-term OOF objectives

ONAP Optimization Framework (OOF) provides the ability to create and run a policy-driven optimization application in a declarative, model-driven matter via the Optimization Service Design Framework (OSDF). The OSDF builds upon seed code from AT&T by bringing in new optimization technologies identified via collaborative efforts to provide a declarative, model-driven optimization framework. The OSDF also supports integration with custom coded optimization applications. This release includes the Homing and Allocation Service (HAS), which again builds upon seed code from AT&T, and addresses a core ONAP requirement, Homing/Placement of VNFs and Services (VNF Homing Capability).

Additional details for the initial objectives are as follows:

1. Support minimum viable ONAP use cases for Homing/Placement of VNFs and Services via the Homing and Allocation Service
   Specific homing/placement use cases identified for this release are:

   1. Residential Broadband vCPE use case

   2. Hardware Platform Enablement (HPA) in ONAP

   3. VNF scale out use case

2. Demonstrate the declarative, model-driven functionality of OSDF via representative applications aligned with ONAP use cases
   This will be done through a simple example mimicking Change Management Scheduling Optimization use case (this effort is aligned with the requirements of the broader CMSO use case). Additional examples are also being pursued, but are slated as stretch goals. The example will also demonstrate the ability to inject new types of constraints at run-time via policy (thus, in many cases, be able to inject advanced functionality into the optimization application without changing the model or configuration).

3. Provide a library of OSDF components/adapters for building new optimization applications
   Beyond the adapters for Policy, AAI, MultiCloud, and SDC, additional focus will be on the incorporation of runtime metrics from MultiCloud for adaptive workload optimization

4. Ensure compliance with platform maturity requirements
   This will be done by meeting or exceeding minimum requirements for projects in Beijing release.

The following items constitute “stretch goals” and “foundations towards real goals”

1. Provide foundational components required to support broader use cases targeted for R2+
   The objective is build “foundations" of components that will be developed and be ready for integration with applications supporting broader use cases such as:

   1. VoLTE use case

   2. 5G family of use cases

   3. Broader CMSO use case

2. Pursue exploratory efforts that are in the scope of near-term and longer-term OOF objectives
   We have identified some general features that we believe will be required in the near-term to longer term (timeframe or two or three releases in the future). However, the core OOF group will be focusing on the ONAP release deliverables and will not be able to spend substantial development effort on these items. Fortunately, these appear to align well with recent advances pursued by the broader optimization community. So, initially, we will pursue these via engaging the broader optimization community to contribute ideas and models. Specific features of interest currently are: 

   1. General algorithms for addressing uncertain data

   2. Robust scheduling under uncertainty

   3. Optimization of resource allocation when only approximate/averaged metrics are available

• Targeted goal in R2:

  • Use Case: Residential Broadband vCPE (approved)

  • Hardware Platform Enablement In ONAP

  • Use Case: VNF scale out (and potentially service scale out)

• Stretch goal in R2:

  • Use Case: VoLTE (approved)

  • 5G family of use cases

  • Broader CMSO use case

• OOF-HAS - Homing Service (runtime) that can be provides optimized placement based on policy constraints, across multiple clouds and multiple sites

• OOF-OSDF - Optimization Design framework (runtime) that supports HAS, the Homing Optimizer

• Application demonstrating a declarative, model-driven, and policy-driven basic CMSO functionality (this effort is aligned with broader CMSO use case)

• HPA functionality integration into OOF

• Integration with Multi Cloud (incorporation of runtime metrics)

List the functionalities that this release is committing to deliver by providing a link to JIRA Epics and Stories. In the JIRA Priority field, specify the priority (either High, Medium, Low). The priority will be used in case de-scoping is required. Don't assign High priority to all functionalities.