Policy Unification and Organization

Because the policy framework is expandable and multipurpose, it might contain many types of policies. Policies can be organized according to whatever dimensions are useful.  Users can define attributes that specify the scope of policies, and these attributes can be extended to the policy-enabled functions and components. Attributes can be defined for each dimension. Useful policy organizing dimensions might include:

• Policy type or category (taxonomical)
• Policy ownership or administrative domain
• Geographic area or location, 
• Technology type  
• Policy language and version 
• Security level or other security-related values, specifiers, or limiters

Policy Use

At runtime, policies that were previously distributed to policy-enabled components will be used by those components to control or influence their functionality and behavior, including any actions that are taken. 

By then setting values for these attributes, Policy Scope can be used to specify the precise Policy “scope” of: (A) Policy events or requests/triggers to allow each event/request to self-indicate its scope, e.g., which can then be examined by a suitable function for specifics of routing/delivery, (B) Policy decision/enforcement functions or other Policy functions to allow each Policy function to self-indicate its scope of decision making, enforcement, or other capabilities, (C) Virtual Functions of any type for auto-attachment to the appropriate Policy Framework and distribution mechanism instances, and most importantly to (D) individual policies to aid in management and distribution of the policies.

7.5 Policy Technologies

D2 Policy will utilize rather than replace various technologies; examples of possible policy areas are shown in the following table. These will be used, e.g., via translation capabilities, to achieve the best possible solution that takes advantage of helpful technologies while still providing in effect a single D2.0 Policy “brain.”

many cases, those policies will be utilized to make decisions, where these decisions will often be conditional upon the current situation.

A major example of this approach is the feedback/control loop pattern driven by DCAE. Many specific control loops can be defined. In a particular control loop, each participant (e.g., orchestrator, controller, DCAE, virtual function) will have received policies determining how it should act as part of that loop. All of the policies for that loop will have been previously created together, ensuring proper coordinated closed-loop action. DCAE can receive specific policies for data collection (e.g., what data to collect, how to collect, and how often), data analysis (e.g., what type and depth of analysis to perform), and signature and event publishing (e.g., what analysis results to look for as well as the specifics of the event to be published upon detecting those results). Remaining components of the loop (e.g., orchestrators, controllers, etc.) can receive specific policies determining actions to be taken upon receiving the triggered event from DCAE. Each loop participant could also receive policies determining the specific events to which it subscribes.