1. Replicators: Transformations to Address Model Scalability Jeff Gray, Yuehua Lin, Jing Zhang, Steve Nordstrom, Aniruddha Gokhale, Sandeep Neema, and Swapna Gokhale CIS Dept. – UAB, ISIS - Vanderbilt University, CS Dept. – U. Conn. Funded in part by DARPA-PCES and the NSF CSR-SMA.

2. Overview of Presentation Criteria for Scalability Background and Challenges Example Replicators Automated Approaches for Scaling Models mic.omg.org Case Studies Alternative Approaches Desiderata for Model Replication

3. Model Interpretation Model Interpreters Models Modeling Environment Application Domain App 1 App 2 App 3 Application Evolution Environment Evolution Metamodeling Interface Metamodel Definition Meta-Level Translation Model Builder Background: Model Integrated Computing (MIC) MetamodelMetamodel ModelModel InterpreterInterpreter void CComponent::InvokeEx(CBuilder &buil der,CBuilderObject *focus, CBui lderObjectList &selected, long param) { CString DMSRoot = ""; DMSRoot = SelectFolder("Please Select DMS Root Folder:"); if (DMSRoot != "") { DMSRulePath = DMSRoot + RULESPATH + "Rules\\"; MSRuleApplierPath = DMSRoot + RULESPATH + "RuleApplier\\"; AfxMessageBox("DMSRulePath = " + DMSRulePath, MB_OK); CString OEPRoot = ""; OEPRoot = SelectFolder("Please Selec DEFINE INTERPRET The Generic Modeling Environment (GME) adopts the MIC approach and provides a plug-in mechanism for extension.

4. Example DSMLs (not UML)

5. Ability to evolve models The size of system models will continue to grow  We have created models containing several thousand modeling elements  Others have reported similarly (Johann/Egyed – ASE 2004) A key benefit of modeling  Ability to explore various design alternatives (i.e., “knobs”) E.g., understanding tradeoff between battery consumption and memory size of an embedded device E.g., scaling a model to 800 nodes to examine performance implications; reduce to 500 nodes with same analysis… Reducing complexities of the modeling activity  Limit the amount of mouse clicking and typing required within a modeling tool to describe a change Improves productivity and reduces potential manual errors A general metric for determining the effectiveness of a modeling toolsuite comprises the degree of effort required to make a correct change to a set of models.

6. Multiple Levels of Hierarchy Replicated Structures Context Sensitive Previous Challenge: Crosscutting Constraints in Real-Time/Embedded Models Crosscutting in Models  Base models become constrained to capture a particular design  Concerns that are related to some global property are dispersed across the model A B cde B cde F B cde Changeability??? Crosscutting Constraints Solution first presented in Comm. ACM 2001 (AOP Issue) and AOSD book chapter

7. Implemented as a GME plug-in to assist in the rapid adaptation and evolution of models by weaving crosscutting changes into models. C-SAW: Model Transformation Engine ECL Interpreter ECL Parser Defines MetaModel Source Model M o d e l i n g A P I s Defines ECL Transformation Specifications Defines CopyAtom strategy CopyAtom M o d e l i n g A P I s Aspect Weaving Target Model

8. New Challenge: Replicating a Base Model to Address Scalability Issues Model Scalability  Base models must be replicated to explore alternative designs  Model elements need to be replicated, in addition to all required connections Single UAV Model Three UAV Model

9. Contribution and definition Core contribution: This paper makes a contribution to model scalability by describing a model transformation approach that enables automated replication to assist in rapidly evolving a model. Definition: replicator – a model transformation that expands the number of elements from a base model and makes the correct connections among those elements

10. Key Characteristics for a Replication Approach (C1) Retains the benefits of modeling (obvious!?)  Enabling analyses that are too difficult at the implementation level  Navigating through design alternatives Set of design alternatives Analysis tools: Cadena, Vest, Matlab RTE…

11. Key Characteristics for a Replication Approach (C2) General across multiple languages  Not fixed to one specific DSML T2 T1 T3 Replication Approach

12. Key Characteristics for a Replication Approach (C3) Flexible to support user extension of the replication parameters Replication Approach c(p)

13. Approach 1: Intermediate stage of model compilation Observations The result of replication not within the direct purview of modeler  Violates C1! Each translator is specific to a particular DSML  Violates C2 Scalability rules often hardcoded into translator  Violates C3

14. Approach 2: Domain-specific Replication Plug-in Observations The result of replication available for further refinement and analysis  C1 achieved Each plug-in is specific to a particular DSML  Violates C2 Plug-in may provide several knobs to configure a replication strategy; usually not a “language” but a wizard with checkboxes  Weak C3 Plug-in

15. Approach 3: Replication with a model transformation engine A scaled model may be the source model for further refinement Model compiler could be a code generator, or interface to analysis tools Preserves all three of the desired properties

16. Example applications Event QoS Aspect Language  Specify properties of event-based communication within a DRE (e.g., mission-computing avionics) System Integration Modeling Language  Specify properties of high-performance physics experiments UAV QoS Language (not described here)  Specify properties of video QoS in an Unmanned Aerial Vehicle Future: A language to address performance issues among distributed systems using network patterns

17. Event QoS Aspect Language (EQAL) Assists in specification of publish-subscriber event service configuration for large-scale DRE systems  Publishers generate events to be transmitted  Subscribers receive events via hook operations  Event channels accept events from publishers, and deliver events to subscribers Replication requirements  Add 5 CORBA_Gateways to each original site  Repeatedly replicate one site instance to add 5 more extra sites, each with 5 additional CORBA_Gateways  Create all required connections among replicated models

18. Scaling the Event QoS Aspect Language strategy traverseSites(n, i, m, j : integer) { declare id_str : string; if (i <= n) then id_str := intToString(i); rootFolder().findModel("NewGateway_Federation").findModel("Site " + id_str).addGateWay_r(m, j); traverseSites(n, i+1, m, j); endif; } //recursively add CORBA_Gateways to each existing site strategy addGateWay_r(m, j: integer) { if (j<=m) then addGateWay(j); addGateWay_r(m, j+1); endif; } //add one CORBA_Gateway and connect it to Event_Channel strategy addGateWay(j: integer) { declare id_str : string; declare ec, site_gw : object; id_str := intToString(j); addAtom("CORBA_Gateway", "CORBA_Gateway" + id_str); //create one CORBA_Gateway ec := findModel("Event_Channel"); site_gw := findAtom("CORBA_Gateway" + id_str); addConnection("LocalGateway_EC", site_gw, ec); }

19. System Integration Modeling Language (SIML) Assists in specification of configuration of large-scale fault tolerant data processing systems Used to model several thousand processing nodes for high- performance physics applications at Fermi Accelerator Lab  A system model may be composed of independent regions  Each region may be composed of local process groups  Each local process group may contain primitive application models  Each system, region, and local process group must have a manager that is responsible for mitigating failures in its area Replication requirements  Replication of local process group nodes  Replication of entire region models and their contents  Generation of communication connections between regional managers and newly created local managers  Generation of additional communication connections between the system manager and new regional manager processes

20. Scaling the System Integration Modeling Language aspect Start() { scaleUpNode("L2L3Node", 5); scaleUpRegion("Region", 8); } strategy scaleUpNode(node_name : string; max : integer) { rootFolder().findFolder("System").findModel("Region").addNode(node_name,max,1); } strategy addNode(node_name, max, idx : integer) { declare node, new_node, input_port, node_input_port : object; if (idx<=max) then node := rootFolder().findFolder("System").findModel(node_name); new_node := addInstance("Component", node_name, node); input_port := findAtom("fromITCH");node_input_port := new_node.findAtom("fromITCH"); addConnection("Interaction", input_port, node_input_port); addNode(node_name, max, idx+1); endif; } strategy scaleUpRegion(reg_name : string; max : integer) { rootFolder().findFolder("System").findModel("System").addRegion(reg_name,max,1); } strategy addRegion(region_name, max, idx : integer) { declare region, new_region, out_port, region_in_port, router, new_router : object; if (idx<=max) then region := rootFolder().findFolder("System").findModel(region_name); new_region := addInstance("Component", region_name, region); out_port := findModel("TheSource").findAtom("eventData"); region_in_port := new_region.findAtom("fromITCH"); addConnection("Interaction", out_port, region_in_port); router := findAtom("Router"); new_router := copyAtom(router, "Router"); addConnection("Router2Component", new_router, new_region); addRegion(region_name, max, idx+1); endif; }

21. Discussion Physical limits of manual replication  SIML models have been scaled by-hand to 32 and 64 nodes  After 64 nodes, the manual process deteriorated taking several days with multiple errors Benefits of automated replication  Replication is parameterized and can be evolved rapidly  Using a model transformation, SIML models have been scaled up to 2500 nodes  The time to create the model transformation by a user unfamiliar with the domain: < 1.5 hours

22. Conclusion Related work  Much related work in model transformation and supporting tools We believe the general idea is applicable to other MT tools  Not able to locate any literature on the general scalability issue as it applies to automated transformation (any ideas?) Future work  Transformations may be reused often and influence the correctness of the modeling process Improved capabilities to test and debug within C-SAW are currently under investigation  Layer a DSML on top of a performance analysis solver; DSML will abstract various networking design patterns (e.g., reactor pattern); base models will be scaled using replicators to explore performance implications

23. Conclusion Benefits of replicators as model transformations  Domain independence  Initial evidence that productivity (in terms of design exploration) is improved, as well as correctness of the resulting model Primary limitation of automated approach  Without the addition of screen layout information in the model transformation, the resulting view may be cluttered or unreadable

24. For More Information… http://www.cis.uab.edu/Research/C-SAW/ Contains papers, downloads, video demos Replicators and Two-Level Aspect Weaving http://www.isis.vanderbilt.edu/Projects/gme Generic Modeling Environment