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A Practical Process for Simulation Component Reuse
Dissertation Proposal Presentation by Robert G. Bartholet 27 May 2005 Introduce the title of this work.
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Paul F. Reynolds, Jr., Advisor
Committee Members Worthy N. Martin, Chair Paul F. Reynolds, Jr., Advisor John C. Knight David C. Brogan Harsha K. Chelliah Ernest H. Page 5 second slide...thank the committee, and take a second to introduce Ernie to the group and thank him for coming down from NoVa
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Thesis Statement Component selection, applied to reusable simulation components, can be enhanced significantly by including considerations for the utility of component adaptation, simulation specific attributes, and other features that have not been considered in traditional approaches to component selection. Just show it, give the committee a few seconds to read it, and tell them that we'll revisit it later in the presentation
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Thesis Statement Component selection, applied to reusable simulation components, can be enhanced significantly by including considerations for the utility of component adaptation, simulation specific attributes, and other features that have not been considered in traditional approaches to component selection. Just show it, give the committee a few seconds to read it, and tell them that we'll revisit it later in the presentation
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M&S Reuse in the Ideal World
REQUIREMENTS S1 SIMULATION COMPONENT DATABASE SIMULATION COMPONENT DEVELOPERS COMPONENT SELECTION TOOL BEST PRACTICES S2 THEORY . . . Sn Explain how an ideal M&S reuse system would work. IDEAL FEDERATION
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What is Available? State that these are some examples of state-of-the-art. Use this as a basis for why the state of the art is inadequate.
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APPLICATION GENERATORS VERY HIGH LEVEL LANGUAGES
Software Reuse TRANSFORMATION SCAVENGING S/W ARCHITECTURES COMPONENTS APPLICATION GENERATORS A very short background on reuse 7 types of reuse (Krueger 1992) S/W SCHEMAS VERY HIGH LEVEL LANGUAGES Krueger, 1992
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Related Work Metrics and Models Component Representation
Selection Techniques I have broken down previous s/w research relating to this work and categorized it in the proposal according to these 3 bullets...(state in a clause what each bullet means.) The number of works is too many to list here, but for the purpose of this presentation, let's just say this ground is well covered with respect to general software research.
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Reuse Exemplars The exemplars for reuse are gui widgets, unix, and math libraries....successful because conceptually simple components, well understood context for use, clearly defined interfaces
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Motivation Popular approach to reuse...these illustrate a couple of the reuse challenges. On the left, the pieces are close to fitting together, but not exactly so it won't work without adaptation....on the right, they fit together perfectly, but the composition is not meaningful.
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Simulation Composability Component Selection (CS)
REQUIREMENTS CS: Is there a subset of X of cardinality k or less that covers R? R X r4 x1 x2 r1 Example instance when k = 3. x4 x3 r2 x5 CS is NP-complete. Proof: reduction from SAT (Page and Opper 1999) and MSC (Petty et al. 2003). CS can be approximated using GREEDY (Fox et al. 2004). r3 r5 x6 Recently M&S community has focused on composability...this slides takes a formal look at composability component selection. r6 x7 x8 r7 r8
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Composability Assumptions
Component selection in the context of simulation composability is inflexible. Components are immutable. There exists a master set of components from which all possible sets of requirements can be satisfied. Requirements are known a priori and do not change. State the implied assumptions of traditional composability. Tell them up front that we need to consider adaptation to overcome the inflexibility.
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Thesis Statement Component selection, applied to reusable simulation components, can be enhanced significantly by including considerations for the utility of component adaptation, simulation specific attributes, and other features that have not been considered in traditional approaches to component selection. This completes the background and motivation for my work. Here's the thesis statement again.
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Applied Simulation Component Reuse (ASCR)
Exploiting simulation specific characteristics and adaptation changes component selection! Leverage simulation specific characteristics in component reuse Exploit adaptability of components to satisfy requirements. What have we gained? We no longer have to assume the existence of a master set of components. We can more flexibly react to changing requirements. We can pre-select components based on over-arching simulation specific requirements. But… I have defined a process called ASCR. Here are the key components of this process.
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ASCR Model BEHAVIOR DETERMINATION FUNCTION BEHAVIOR MAPPING FUNCTION
Currently no model exists that explains simulation component reuse from beginning to end. I have begun to build a model, with which we can reason about ASCR. Don't want to make this slide too busy defining all the notation, so I'll walk the committee through the functions verbally. I am assuming they have seen these in the proposal so I won't dive in deep here. UTILITY FUNCTION
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Assumptions and Notation
θ: Upper bound on the number of requirements that can be satisfied by any one component. β: scaling factor which captures the change in utility encountered when a component satisfies multiple requirements uxyz: the utility of the xth component satisfying requirement y while satisfying z-1 other requirements. Preparation for the next set of slides.
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Adapting Components to Satisfy Requirements
CS CS-ASCR θ=3 r1 r2 r3 r4 r5 r6 r7 x2 r1 r3 r4 r2 r5 r6 R x1 x3 x4 x8 x2 x6 x7 x5 r7 X Recall that we need to consider the utility of adaptation in selecting components. On the left is the composability CS problem seen earlier. Note that x1 only satisfies r1. In ASCR, theoretically we can adapt any component to meet any requirement. We can now consider components that are close but don't exactly satisfy requirements as candidates for satisfying those same requirements. In this example, we can consider x1 as a candidate for r4 and r5.
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Computing Utilities CS CS-ASCR r1 r2 r3 r4 r5 r6 r7 R X r4 x1 x2 r1 x4
Explain how once we decide which components are candidates for which requirements, we use the utility functions to get utility values for each possible component scenario. x7 θ=3 β reduces the number of computed utilities. x8 r7
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Building a Bin View θ=3 r1 r2 r3 r4 r5 r6 r7 r1 r2 r3 r4 r5 r6 r7 x1
COMPONENTS r1 r2 r3 r4 r5 r6 r7 x1 x2 x3 x4 r1 r2 r3 r4 r5 r6 r7 u111 u221 u112 u222 u223 u113 u321 u322 u323 u331 u332 u333 u432 u431 u433 u441 u442 u443 u251 u252 u253 u461 u462 u463 u261 u262 u263 u171 u172 u173 u371 u372 u373 x1 x2 x3 x4 X REQUIREMENTS r1 r2 r3 r4 r5 r6 r7 R Here's a complete example. Note that x1 is only a candidate for 2 requirements.
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Building a Set View Scenario for x2 r1 r2 r3 r4 r5 r6 r7 C u111 u221
We can transform the bin view in polynomial time into the set view. Each subset is a possible scenario for a given component.
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CS-ASCR Definition CS-ASCR (Informal): Is there an exact cover of R, constructed by choosing no more than 1 element from each Ci, with a total utility greater than k? CS-ASCR is NP-complete (Bartholet et al. submitted to ACM/IEEE WSC 2005). Proof: By reduction from X3C. Optimization problem is NP-hard. Now we have the component selection problem for ASCR. Here's a possible satisfying instance of CS-ASCR. Give the complexity result. C u112 u222 u171 u222 u111 u172 u221 u251 u261 u252 u262 r1 C1 R r2 C2 r7 u433 u462 u223 u252 u371 u331 u321 u442 u463 u263 r3 u443 u432 u432 u262 u253 r4 C3 C4 r5 u332 u323 u332 u372 u373 u442 u431 u461 u441 r6 u372 u322 u322 u333 u462
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Interesting Effects of θ and β
θ = 1, CS-ASCR is in P θ = 2, complexity of CS-ASCR is open θ >= 3 but bounded, CS-ASCR is NP-complete θ is unbounded, CS-ASCR is exponential β β=1, CS-ASCR is in P Discuss how I have begun an investigation into the effects of some of the CS-ASCR assumptions.
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Generalizing the Result
Modified definition of θ: utility can be dependent on selection of other components. r1 r2 r3 r4 r5 r6 r7 r8 θ=3 x1 x3 x6 CS-ASCR-X: ASCR component selection with the modified θ. Corollary: CS-ASCR-X when θ >= 3 is at least NP-complete (by reduction from X3C). x6 x7 x6 x4 x2 Discuss how I have generalized the result into one of dependencies in the face of optimizing weights while selecting components. Overall analysis: Adaptation brings flexibility to component selection, but the problem is still inherently intractable. Will search for efficient, good (non-optimal) solutions.
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Leveraging Simulation Properties
Stochastic sampling Time Event generation I will leverage simulation unique characteristics.
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Example of Leveraging Time
LOW UTILITY LANCHESTER ATTRITION CALCULATED EVERY HOUR OF LOGICAL TIME Requirement 1: Model ground combat. Requirement 2: Model air combat. Requirement A: Provide up-to-date conflict adjudication data no less than once per minute. XX MODEL GC1 X STOCHASTIC ATTRITION AGGREGATED EVERY 5 MINUTES OF LOGICAL TIME HIGH UTILITY HIGH UTILITY STOCHASTIC ATTRITION AGGREGATED EVERY 10 MINUTES OF LOGICAL TIME Gotta build the "home run" slide here giving an example of how to leverage sim specific characteristics with adaptation to do component selection. This slide must convince the committee that there is research potential here. MODEL GC2 MODEL AC1
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Example of Leveraging Time
TIME SCALE FACTORED INTO COMPONENT SELECTION PRE-SELECTION Gotta build the "home run" slide here giving an example of how to leverage sim specific characteristics with adaptation to do component selection. This slide must convince the committee that there is research potential here. MODEL GC2 MODEL AC1
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Research Areas of Focus
Define the problem Define the process Characterize complexity of ASCR Component selection Sim Specific Characteristics Adaptation Restate the research agenda.
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Measures of Success Accurately formalized ASCR problem
Defined a practical ASCR process Built practical methods for component selection Developed useful utility functions Analyzed complexity of critical algorithms in ASCR Here's how I'll know when I'm done. Remind the committee that the threads of leveraging adaptation and sim specific chars remain throughout these measures.
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Expected Contributions
Creation of a methodology that significantly improves state of simulation component reuse and provides practical methods for component selection Improved understanding of complexity of component selection Demonstration of how simulation specific properties can be leveraged in component selection Here are the expected contributions. Talk through each.
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Publication Efforts Bartholet, Brogan, Reynolds, Carnahan. In Search of the Philosopher's Stone: Simulation Composability Versus Component Based Software Design. Proceedings of the Fall 2004 Simulation Interoperability Workshop, Orlando, FL, September 2004. Brogan, Reynolds, Bartholet, Carnahan, Loitière. Semi-automated Simulation Transformation for DDDAS. Proceedings of the 5th International Conference on Computational Science, Atlanta, GA, May 2005. Bartholet, Reynolds, Brogan. The Computational Complexity of Component Selection in Simulation Reuse. Submitted to the ACM/IEEE 2005 Winter Simulation Conference, Orlando, FL, December 2005. Bartholet, Kuang, Son. Intelligent Decentralized Update Management in Real-Time Embedded Applications. Working Draft Completed. Submission in August 2005 to conference TBD.
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Conclusion Component selection, applied to reusable simulation components, can be enhanced significantly by including considerations for the utility of component adaptation, simulation specific attributes, and other features that have not been considered in traditional approaches to component selection. Restate my thesis in conclusion.
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Discussion
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