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Viva slides_secured objective programming

My viva slides on Jan 2014

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Viva slides_secured objective programming

  1. 1. Secured Objective Programming Support to Intention Driven Autonomic Cloud Computing Yasir A. Karam of Liverpool John Moores University for the degree of Doctor of Philosophy 8-1-2014
  2. 2. Contents • Introduction to Goal – Actor Model – Goal – Actor Model – Goal - Actor Society – Call Dispatching in Dynamic Actor Model • Research Problems & Resolutions – Annotating i* Goal Modelling Method over XML Intentions – Neptune Architecture with Automated Planning Support – Axioms Used as Predicate Propositions • Examples of Problem Domain Description – Adding Formal Logical Representation of XACML over Neptune using PAA + CA-SPA – Domain Description Language for XACML Problem • Publications – Results from published work
  3. 3. Goal Actor Model
  4. 4. Research Problems & Resolutions in Snapshot Modeling Secured Interoperable Architecture based on Capability Security Model and SOA Modelled XACML using PAA, CA-SPA Modeling problem domain for (a) Used STRIP style problem domain description language PDDL to write declaratives for goal propositions. Used Temporal Logic Planning as automated reasoning engine to solve PDDL problems Search algorithm that fit graph model representation of the problem. Several problem types were anticipated such as search heuristic problem Search algorithms used is breadth-first. All the above support was added and implemented inside Neptune Architecture Problem I Problem I
  5. 5. • Adding Goal Modeling support to Intention Model so that Goal expressions that fit best known goal modeling methods like i*, GRE, Tropos. – Goal modeling artifacts were annotated over Intention XML style language • Writing LTL specification to support Goal reasoning model using existing Neptune Scripting Language, PAA and CA-SPA – This task was solved through representation of LTL specifications for goal modeling using STRIP style problem domain description language., CA-SPA and PAA • Modeling of qualitative and quantitative capacities of goal modeling such that ++, -- are goal satisfiability axioms (soft-goals) to be used to test Assurance of how much is been achieved. – A stochastic aggregative model like Basian networks and MDP (Markov Decision Process model) was approached and analyzed (this task was under progression stage of 35% ) – A subsequent is anticipation for optimal points from non-determinism problem Problem II Problem II Sub- Problem Sub- Problem
  6. 6. • Re-designed Neptune (all the language ) over distributed concurrent logic programming paradigm, new enhanced features such as asynchronous call dispatching and others. • The use of “shared memory” is more effectively to comprehend in carrying immune state characteristics. • This helped us to add new language support to model Team Object Model and team guards (this is completed feature) in which is published in author’s paper. Problems cont. Problems cont. The below problem was tackled optionally as an engineering effort needed to enhance legacies in Neptune Architecture
  7. 7. • Re-designed Neptune (all the language ) over distributed constrained logic programming paradigm, new enhanced features such as asynchronous call dispatching and others. • The use of “shared memory” is more effectively comprehend in carrying immunized state characteristics. • This helped us to add new language support to model Team Object Model and team guards (this is completed feature) in which is published in one of authors papers. Problem III Problem III The below problem was tackled optionally as an engineering effort needed to enhance legacies in Neptune Architecture
  8. 8. Actor Society Agent Goals Capabilities … … … … … … … … + collaboration (through delegation) - competitive goals Society member’s objective: use others capabilities to achieve personal goals
  9. 9. Research Hypothesis • Identification, representation, expressing and classifying new type of atoms that support Goal Oriented Requirements, • With the aid of existed structured atoms of Intention Model, what we did is testing socio-communal interaction between multiple intention models and how to use distributed objectives/goals to identify recognition and resolution areas . • Defining, design and implement Reasoning Model for Neptune architecture, which adds capabilities of writing aided constructs of Strategies, Plans, Schedules Tasks and also Objective oriented attributes like objectives type, rules, situations and fluent’s • Model of performance based attributes like KPI objects, cost, performance targets, weights this is based on Capability Driven Actor Model (CDAM). • Define and model elements for Capability Actor Architecture like modeling of No cost will be paid unless farthest goal is evaluated • Value based dispatching through introspective delegation (exchange of accountability) • Planned invocation through stages between Early and Late binding
  10. 10. Capability Concepts with XACML persistent •Can-Permit-Read-IPO •Can-Deny-Read-IPO •Can-Delegate-Read-IPO Goal Concepts (strategic) •Identify-user •authenticate-request •authorize-delegation-request States (Goals) •Permitted-Read-IPO •Denied-Read-IPO •Delegated-Read-IPO Fluents •Pre-Permitted-Read-IPO •at-Denied-Read-IPO •Pre-Delegated-Read-IPO
  11. 11. (define (problem example) (:domain GORE-domain) (:objects Buyer Accounting_office - t_actor Go_to_conference Get_reimbursement Buy_ticket - t_goal ) (:goal (and (done Go_to_conference) ) ) (:init (can_do Accounting_office Get_reimbursement ) (can_do Buyer Buy_ticket ) (can_depend_on Buyer Accounting_office ) (wants Buyer Go_to_conference ) (and_subgoal2 Go_to_conference Buy_ticket Get_reimbursement ) ) Dependability Objective Capability Declarative Intentional Declarative Example of Problem Domain Description
  12. 12. Neptune Architecture with Automated Planning Support
  13. 13. Axioms Used as Predicate Propositions Capability Concepts with XACML persistent •Can-Permit-Read-IPO •Can-Deny-Read-IPO •Can-Delegate-Read-IPO Goal Concepts (strategic) •Identify-user •authenticate-request •authorize-delegation-request States (Goals) •Permitted-Read-IPO •Denied-Read-IPO •Delegated-Read-IPO Fluents •Pre-Permitted-Read-IPO •at-Denied-Read-IPO •Pre-Delegated-Read-IPO
  14. 14. CA-SPA for XACML Concepts Read-Test Situation Action Predicted Predicted Situation
  15. 15. Domain Description Language for XACML Problem Interpreter Solver
  16. 16. Contribution to Knowledge • Adding constructs to existing “Intention Model” that helps actors to specify “preference” or priority to their intentions, this will be compiled and linked with right composition level NBLO’s (High NBLO’s) that will in turn be used to constrain decomposing big computational coarse grained problems into smaller ones. • Adding support for concurrent transaction modelling to Neptune model using management of “shared memory” - Adding support for autonomic social behavior to runtime actors though dynamic adaptation of goal oriented requirements from intention model. • Using PAA style of modelling advices, we provided support for writing dynamic objective model to “Linear Temporal Logic” and use metricized constructed objects with the aid of CA-SPA in providing support to model “Propositional Logic” • The support for LTL makes it then easy to solve problems of situational predicates used to achieve goal modalities “achieve”, “avoid”, “maintain” and “avoid&maintain”.
  17. 17. Annotating i* Goal Modelling Method over XML Intentions Actors annotation over Intention Goal SD Constructs Intention Description
  18. 18. Actors annotation over Intention Goal SD Constructs Intention Descriptio
  19. 19. Cont. • On the other hand we used CA-SPA policies to design formalities for satisfiability properties. • Following formalisms above, we provided support to write STRIP style “Domain Specifications” and “Problem Domain Specifications”. The way writing specifications used is similar to PDDL language “Planning Domain Description Language” • In order to ensure better “Satisfiability Propagation” or “Value Proposition” between socially networked actors and objects, we provided code design support metric softgoal objects that used to measure quality of provisioning for problem main objects in order to achieve new hard goal states, this is by using PAA, Accounting and Auditing with CA-SPA, to count the number of violations to formal constrains. • We provided new design support to design patterns through situating and fulfilling of object role based requirements to new injected “concepts” at design and compile time. This is proved with the aid of “dynamic polymorphisms” and “object teams architecture” • Automation support for the above features is added to assist in performing feasible design decision dynamically, this through using multiple solvers depending of problem type.
  20. 20. Results from published work, illustrating competitive socio-economic case of PetAuction

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