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Policy Engine

Policy Engine. Research: Design & Language. Control Middleware. Design: Extensibility. Current Implementation Issue : D ifferent functions to communicate with different managers Objective: m odular extensible design. Design: Extensibility. Can we leverage MIH design ?.

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Policy Engine

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  1. Policy Engine Research: Design & Language IRT Lab, Columbia University

  2. Control Middleware IRT Lab, Columbia University

  3. Design: Extensibility Current Implementation Issue: Different functions to communicate with different managers Objective: modular extensible design IRT Lab, Columbia University

  4. Design: Extensibility Can we leverage MIH design ? IRT Lab, Columbia University

  5. Leverage MIH Registration Policy Engine(PE) is similar to MIH Function IRT Lab, Columbia University

  6. Events and Services • Manager provides events and services • Events: • Link change • Network change • Location change • Services • Subscriber can query the value of certain attributes (current location, current bandwidth, etc.) • Subscriber can send decisions/notifications • Policy engine subscribes to events and services (put in tables) IRT Lab, Columbia University

  7. Tables in Policy Engine • Event Table • Events from each connected manager • Each event will trigger a different evaluation • Service Table • Subscribed services from managers or other modules • Methods / API to access the service also recorded IRT Lab, Columbia University

  8. Policy Language Research: Design & Language IRT Lab, Columbia University

  9. Why ? • Environment • Mobile Devices • Heterogeneous networks/interfaces • Network selection and handover • Default rules • Manual switch • Control middleware • Unified policy language IRT Lab, Columbia University

  10. Use Cases • Different network usage for different user scenarios • Different places (office / home) • Different time (morning / evening) • Different activities(working / playing) • Different devices (monitors / phones) • Different security options • Application preferences • Some need high throughput • Some need lowest money cost IRT Lab, Columbia University

  11. Related Works • Policy expression framework • Standard schemas • P3P / SAML / … • Rule-based policies • Accountability in RDF (AIR) • Custom configurations • For particular applications IRT Lab, Columbia University

  12. Language Design Attributes Evaluation Decision Knowledge base IRT Lab, Columbia University

  13. Language Semantics • Attribute : Network / System • Term : Value (Binary evaluation) or range (T/F) • Score : Evaluate score from one attribute • Eval : Trigger evaluation and generate facts (each event will trigger one eval, which may evaluate several terms and scores) • Fact : Predicate Logic unit • Rule : Making decisions by forward chaining (facts => actions) IRT Lab, Columbia University

  14. Attributes • Deterministic • Location • Time • Scenario profiles (meeting / working / traveling) • Security • Devices • Non-Deterministic • Quality of Service (QoS) • System Resources • Expense/Cost IRT Lab, Columbia University

  15. Attribute Representation Example: derived attr { id: bp_ratio attr: bandwidth (x1) attr: price (x2) func: x1 / x2 } • Direct attributes • Corresponding entry in the service table • Derived attributes • Combination of attributes • Evaluated as a single attribute • Attribute table: • Name of the attribute • How to get the value of the attribute IRT Lab, Columbia University

  16. Deterministic Evaluation Example: term { id: at_home attr: location values: home } term { id: mid_bw attr: bandwidth low: 50 high: 200 } • Binary (Yes/No) • Range (In range or not) • Term • id • attr : evaluated attribute • values : expected values • low : lowest value • high : highest value IRT Lab, Columbia University

  17. Non-deterministic Evaluation 0.9 bp_ratio 0.7 signal 0 0.2 latency 1 Example: score { id: bw_score attr: bandwidth order: linear max: 1000 min: 0 priority: 0.7 } Example: score { id: price_score attr: price order: linear max: 0 min: 1000 priority: 0.9 } • Attribute value -> score (0 ~ 100) • Priority (used as weight when evaluating multiple scores) • Score • id: name of the score • attr: evaluated attr(s) • how: max, min, order • priority : 0 ~ 1 IRT Lab, Columbia University

  18. Eval Examples: eval { id: eval1 { attr: location => location(x) } { term: mid_bw => mid_bw(x) } { score: bw_score => highest_bw(x) } { score: price_score => cheapest(x) } { score: bw_score score: price_score => most_worth(x) } } • Event triggered • id: used by Event Table • Generate facts • attributes: directly turned • terms: which satisfies • scores: highest score • Only one score • Multiple scores: weighted sum • fact: predicate(params) IRT Lab, Columbia University

  19. Decision Example: rule { location(home) cheapest(x) => decide(1, x) history(home, x) } Attribute evaluations -> Facts Rule Engine:Facts -> Actions Actions: decisions/ facts Decisions: Leverage service table IRT Lab, Columbia University

  20. Knowledge Base Data Store Taking history into account IRT Lab, Columbia University

  21. Implementation • Policy Language • Syntax abstracted from the examples • LRM to be developed • Implementation language: C • More efficient • Inside system • Rule Engine can leverage logic language • Prolog IRT Lab, Columbia University

  22. Conclusion • Structures • attr, term, score, eval, fact, rule • Advantages • Configurable & Programmable • Configurations can be simple (XML/JSON) • Programmable rule chains can generate intelligent decisions • Extensible (attributes, evaluations, rules) IRT Lab, Columbia University

  23. Next Steps Implementation Evaluation IRT Lab, Columbia University

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