Interoperability of Multiple Autonomous Simulators       in Integrated Simulation Environments                            ...
Introduction  Simulation: the process of designing a model of a real      world system and conducting experiments with th...
Motivation for New SimulationPlatforms Many available simulators      Operate on specific domains        e.g fire simul...
Simulation Integration- historical viewDistributed Interactive Simulation (DIS)               High Level Architecture (HLA...
Limitations of current approaches    Existing Integrated platforms, define a standard model       and require the individ...
General Challenges     Managing Complexity of Interoperating Systems          Analysis of cause- effect relationships   ...
Reflective Architecture for IntegratedSimulation Environments (RAISE)                                                    C...
Using RAISE- step by step       Reification         Extract simulators’ meta-data from base-level simulators (using the ...
Reification Major challenge: the complexity associated with  reification Creole as an Eclipse plug in      Examine sour...
Metamodel                                                Making theBase level                                            ...
Prototype SystemImplementation  Analyzer and Adaptor: to provide data transfer between simulators using data      transla...
Synchronization in   metasimulation Ensuring causal correctness while preserving     simulators’ autonomy       A transa...
Modeling Metasimulation A metasimulation consists of a set of autonomous pre-   existing simulators S1, S2 , S3 ,…, Sn th...
Meta-synchronizer                                                                     Metasimulation                      ...
Metascheduling strategies Address the synchronization problem by     controlling the execution of the simulators actions ...
Relaxed Dependencies  Ideally, dependencies need to be reflected from one   simulator into another as soon as update in o...
A Case Study for simulationintegration To validate the proposed reflective architecture Using three disparate pre-existi...
Case study- simulators properties  Evacuation Simulator                 Communication                      Fire Simulator ...
An Examlpe: CFAST - Drillsim Interaction                         Interaction between Fire simulation and Drillsim         ...
MetamodelsUniversity of California, Irvine   2011 Spring SIW   Leila Jalali
Inter-dependencies extracted from metamodels   1. A harmful condition in CFAST can affect an individual’s health in       ...
Experiments   (a)                             (b)                     (c)     (a) Average synchronization overhead in dif...
Experiments- conclusionStrategy                  CS                   CSR                     OS                        OS...
Thanks                                           jalalil@uci.edu                                   http://www.ics.uci.edu/...
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Middleware Solutions for Simulation & Modeling

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  • This is a very interesting research proposal that will requireknowledge in various domains: simulation, middleware technology,software engineering. and databases!
  • e.g. update an agent’s health in Drillsim based on the harmful condition in CFASTGeometry Transformer: different representation of coordinate systems and resolutions, Using a set of guide points in multiple geographies and determine a coordinate transform matrix
  • We consider three types of deviations:
  • Middleware Solutions for Simulation & Modeling

    1. 1. Interoperability of Multiple Autonomous Simulators in Integrated Simulation Environments Leila Jalali jalalil@uci.edu http://www.ics.uci.edu/~ljalali/ Prof. Nalini Venkatasubramanian, Prof. Sharad Mehrotra University of California, IrvineUniversity of California, Irvine 2011 Spring SIW Leila Jalali
    2. 2. Introduction  Simulation: the process of designing a model of a real world system and conducting experiments with this model for our purpose: cheaper, safer, easier, and quicker  Planning and decision support- defence simulations, emergency response simulations  Domain specific Testing and Analysis - traffic analysis, human behaviour study: crowd dynamics or evacuation simulators, network simulators  Immersive synthetic platforms for trainingUniversity of California, Irvine 2011 Spring SIW Leila Jalali
    3. 3. Motivation for New SimulationPlatforms Many available simulators  Operate on specific domains  e.g fire simulators, transportation simulators Infeasible to build complex simulations entirely from scratch  Economic and organizational constraints  The increasingly complex requirements Need ability to:  Bring together simulators from various modeling domains: Metasimulations  Model and test larger and more complex scenarios  Study cause- effect relationships to integrate simulatorsUniversity of California, Irvine 2011 Spring SIW Leila Jalali
    4. 4. Simulation Integration- historical viewDistributed Interactive Simulation (DIS) High Level Architecture (HLA) (1990–today) Army Projects (1996-today) Defence1975 1980 1985 1990 1995 2000 SIMulator NETworking (SIMNET) (1983–1990) Combat SimulatorsDefense Community Aggregate Level Simulation Protocol (ALSP) (1991–1997ish) War-gaming models Dungeons and Dragons Adventure Board Games Multi-User Dungeon (MUD)(Xerox PARC) Games Multi-User Video GamesInternet & Gaming CommunityUniversity of California, Irvine 2011 Spring SIW Leila Jalali
    5. 5. Limitations of current approaches  Existing Integrated platforms, define a standard model and require the individual simulators to conform to the standard  It might not be always possible  The standard may not have designed to handle the new simulator needs  Current model registration needs a lot of manual work  The approaches are costly, time consuming, easily fail, difficult to HLA: maintain, difficult to scale from the practitioner ─ Low level knowledge needed ─ Cost issues ─ Complexity ─ No support for semantic interoperability ─ Transparency ─ HLA is too big and mainly applied in defense Most of other works on simulation integration provided specific services for interoperability in a small range of casesUniversity of California, Irvine 2011 Spring SIW Leila Jalali
    6. 6. General Challenges  Managing Complexity of Interoperating Systems  Analysis of cause- effect relationships  Reusability: e.g. components, models  We use meta models to describe simulator-related meta-data  Make the underlying simulator more understandable  Abstract of lower-level details of integration and interoperability  Correctness  Ensure the correctness of metasimulations  Time synchronization: timing issues and causality correctness  Data exchange: data transformations  Scalability  e.g multiple geographyUniversity of California, Irvine 2011 Spring SIW Leila Jalali
    7. 7. Reflective Architecture for IntegratedSimulation Environments (RAISE) Complex Applications Data Exchange Time Synchronization dependencies Ontology Consistency Translator Synchronizer Controller Meta level Pub/Sub Lock-table meta-actions Lock Manager External Analyzer & Adaptor Data Sources Meta models Structural specification: UML diagrams, metamodels Interactions: dependency sets, interdependent data Observe & Extract Reflect Base level INLET Drillsim Fire, Earthquake LTESim (Transportation Model) (Activity Model) (Crisis Model) (Communication Model)University of California, Irvine 2011 Spring SIW Leila Jalali
    8. 8. Using RAISE- step by step  Reification  Extract simulators’ meta-data from base-level simulators (using the source code, interfaces, and databases) result in metamodels/specifications and data structures at the meta-level  Analysis of metamodels  Extract the model elements and features that need to be integrated from metamodels  Discover inter-dependencies  Run Federation  Modified features of meta data structures that implement the integration are reflected to the base- level simulators  Ensuring the correctness  Time synchronization, Data management Parser Database meta-models Interfaces Source code meta-data inter-dependencies Run Federation: ye Reification: Extract Analysis of metamodels: end of Execute actions s simulators’ meta-data Discover inter-dependencies simulation? Communicate with metal-level Generate meta-actions no Generate wrapper-actions Ensure Correctness: Pre-processing Time synchronization Data Transformations Results AnalysisUniversity of California, Irvine 2011 Spring SIW Leila Jalali
    9. 9. Reification Major challenge: the complexity associated with reification Creole as an Eclipse plug in  Examine source code dependencies and to extract the simulator’s features.  Java simulators, not useful for complex and large simulators A parser using a tool for large scale code repositories Meta-level search  Extract the entities and attributes from a Java/Matlab Reification Reflection simulator  Simulator’s source code Base-level  Interfaces of DatabasesUniversity California, Irvine 2011 Spring SIW Leila Jalali
    10. 10. Metamodel  Making theBase level underlying simulatorsMeta level more understandable  Abstracting out lower- level details of integration and interoperability  Need to be comprehensive and extensible  UML and Eclipse Modeling FrameworkLeila Jalali University of California, Irvine 2011 Spring SIW
    11. 11. Prototype SystemImplementation  Analyzer and Adaptor: to provide data transfer between simulators using data translators  Synchronizer: to monitor and control concurrent execution of multiple simulations • Using concepts from serializability theory in transaction processing • Developed three techniques: conservative, optimistic, hybridUniversity of California, Irvine 2011 Spring SIW Leila Jalali
    12. 12. Synchronization in metasimulation Ensuring causal correctness while preserving simulators’ autonomy  A transaction-based approach to modeling the synchronization problem by mapping it to a problem similar to multidatabase concurrency  A novel Hybrid Scheduling strategy for metasimulation synchronization which adapts itself to the "right" level of pessimism/optimism based on the state of the execution and underlying dependencies  Relaxation model (motivated by divergence control mechanisms and weak consistency models) which guarantee bounded violation of consistency  Applying proposed techniques in a detailed case study using multiple real-world simulatorsUniversity of California, Irvine 2011 Spring SIW Leila Jalali
    13. 13. Modeling Metasimulation A metasimulation consists of a set of autonomous pre- existing simulators S1, S2 , S3 ,…, Sn that execute concurrently in an integrated environment Using a transaction-based approach to modeling metasimulations  Consider each simulator’s execution as a sequence of actions (time steps in time stepped simulators or events in event based simulators)  Scheduling multiple simulators actions such that dependencies be preserved a three tuple Si=<Ti, Di , Ai> where:  Ti : the type of the simulator  Time stepped or Event based  Di : The data items that the simulator reads or updates. For each data item, denotes the domain of d, which is a set of values that can beUniversity of California, Irvine 2011 Spring SIW Leila Jalali
    14. 14. Meta-synchronizer Metasimulation dependencies meta-actions MetaSynchronizer Meta level wrapper wrapper wrapper wrapper actions . . . actions Base level d . . . d’ Simulator i Simulator j Meta-synchronizer: Upon receiving an external action from For all dependant simulators generate meta- action Post to meta-action queue Upon receiving a request Find all meta-actions from the queue s.t. and Send the metactions to Simulator’s wrapper: At the beginning of each iteration: t=current-time Send a request to get meta-actions Receive meta-actions Generate wrapper-actions At the end of each iteration:University of California, Irvine Send all external action 2011 Spring SIW executed to meta- that have been Leila Jalali
    15. 15. Metascheduling strategies Address the synchronization problem by controlling the execution of the simulators actions to ensure the legality of resulting schedules  Conservative Scheduling: ensures the legality of schedules by delaying the actions such that the dependencies are preserved in the concurrent execution of actions of different simulators  Optimistic Scheduling: we accept the fact that violations occur, resolve the violation when it does occur; by aborting the actions that caused the violation  Hybrid Scheduling: Combines the benefits of both the optimistic and conservative strategiesUniversity of California, Irvine 2011 Spring SIW Leila Jalali
    16. 16. Relaxed Dependencies  Ideally, dependencies need to be reflected from one simulator into another as soon as update in one simulator becomes valid in another  In most of applications, ideal behavior results in unnecessary synchronization overhead and loss of concurrency among simulators.  Relax the dependencies that capture the extent to which simulators can deviate from ideal behavior  Time (t-bound): t-bound works as the delay condition which states how much time the consumer can use a value behind the new update of the supplier  Value (v-distance): Let be the value of updated by and be the value of updated by , we consider the difference between the values of two data item using a user defined distance functionUniversity Number of changes (n-update):2011 Spring SIW the maximum  of California, Irvine captures Leila Jalali
    17. 17. A Case Study for simulationintegration To validate the proposed reflective architecture Using three disparate pre-existing simulators: 1. CFAST (Consolidated Model of Fire and Smoke Transport): a fire simulator  Simulates the effects of fire and smoke inside a building and Calculates the evolving distribution of smoke, fire gases and temperature 2. Drillsim: an activity simulator  Multi-agent system that simulates human behavior in a crisis 3. LTESim: a communication simulator  Abstracts the physical layer and performs network level simulations of 3GPP Long Term EvolutionUniversity of California, Irvine 2011 Spring SIW Leila Jalali
    18. 18. Case study- simulators properties Evacuation Simulator Communication Fire Simulator Simulator DrillSim [9] LTESim [31] CFAST [10]  Simulates a Performs network level Simulates the effects of response activity simulations of 3GPP LTE fire and smoke inside a evacuation Event based building Time stepped Open source (in Time stepped Open source (in Matlab) Black-box (no access to Java) Parameters: num. of source) Agent based transmit and receive Parameters: building Parameters: health antennas, uplink delay, geometry, materials of profile, visual distance, network layout, channel construction, fire speed of walking, num. model, bandwidth, properties, etc. of ongoing call, etc. frequency, receiver noise, Output: temperatures, Output: num. of etc. pressure, gas evacuees, injuries, etc Output: pathloss, concentrations: CO2, etc. throughput, etc.University of California, Irvine 2011 Spring SIW Leila Jalali
    19. 19. An Examlpe: CFAST - Drillsim Interaction Interaction between Fire simulation and Drillsim smoke from fire can affect someone’s health Agents Profile : Health Harmful conditions in Agents Actions : Tell each space at any time PeopleCFAST Drillsim University of California, Irvine 2011 Spring SIW Leila Jalali
    20. 20. MetamodelsUniversity of California, Irvine 2011 Spring SIW Leila Jalali
    21. 21. Inter-dependencies extracted from metamodels 1. A harmful condition in CFAST can affect an individual’s health in Drillsim. 2. Agents in Drillsim can communicate information on the fire and its location –increase the number of ongoing calls (people talk about the crisis) in Drillsim. 3. Harmful conditions in CFAST can affect the evacuation process in Drillsim, e.g. increase walking speed which maps to user speed in LTEsim. 4. Smoke in CFAST can decrease an agent’s visual distance in Drillsim. 5. The number of ongoing communications in Drillsim can affect network pathloss and throughput in LTEsim. 6. Pathloss in LTEsim can be used to determine connectivity/coverage in Drillsim. 7. Information on building layout from CFAST and Drillsim can determine the number of transmit and receive antenna requiredUniversity of California, Irvine 2011 Spring SIW Leila Jalali
    22. 22. Experiments (a) (b) (c)  (a) Average synchronization overhead in different simulation phases  (b)Total execution time in different simulation phases  (c) Synchronization overhead vs. the number of dependencies. (in (a) and (b) no. of dependencies=100)University of California, Irvine 2011 Spring SIW Leila Jalali
    23. 23. Experiments- conclusionStrategy CS CSR OS OSR HS HSRMetric synch. time synch. time synch. Time synch. time synch. time synch. timeCFAST 425.374 2225.626 348.812 2149.945 340.273 2140.273 309.931 2111.844 498.283 2298.475 316.007 2118.918DrillSim 431.265 2232.235 331.192 2133.457 312.182 2113.165 252.011 2055.888 453.592 2253.698 288.555 2089.155LTEsim 156.035 1956.530 99.277 1901.371 4887.753 3378.743 749.009 2550.043 344.005 2144.187 221.079 2023.039Total 1012.674 6414.391 779.281 6188.723 2230.208 7632.181 1310.951 6717.755 1295.581 6696.360 816.641 6231.112  Hybrid Scheduling exhibits superior overall performance to other approaches  The choice of the approach is also dependent on the simulator, e.g. for event based simulators when the number of external events is large we need to avoid using OS  Relaxations always help into get better results in terms of synchronization overhead and total execution time University of California, Irvine 2011 Spring SIW Leila Jalali
    24. 24. Thanks jalalil@uci.edu http://www.ics.uci.edu/~ljalali/University of California, Irvine 2011 Spring SIW Leila Jalali

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