1. Model-Driven Engineering of Component-based Distributed Real-time & Embedded Systems Vanderbilt University Nashville, Tennessee Institute for Software Integrated Systems STI Project 2007 Status Report Krishnakumar Balasubramanian, Amogh Kavimandan {kitty, amoghk}@dre.vanderbilt.edu

2. STI Project 2007 Status Report Vanderbilt’s contributions Model-Driven Engineering tool-chain Current year’s focus on two areas Model-driven Application Specific Optimizations (Kitty) Model-driven QoS Mapping (Amogh) 2

3. Distributed Real-time & Embedded (DRE) Systems Stringent Quality-of-Service (QoS) demands, e.g., real-time constraints Operate under limited resources e.g., avionics mission computing Enterprise DRE Systems Simultaneous execution of multiple applications with varying importance Highly heterogeneous platform, language & tool environments e.g., Total Shipboard Computing Environment (TSCE) Use COTS middleware technologies CORBA, RT-Java Use COTS Component/Service technologies CORBA Component Model (CCM), EJB, Web Services 3

4. 4 Motivation for Application Specific Optimizations Middleware tries to optimize execution for every application Collocated method invocations Optimize the (de-)marshaling costs by exploiting locality Specialization of request path by exploiting protocol properties Caching, Compression, various encoding schemes Reducing communication costs Moving data closer to the consumers by replication Reflection-based approaches Choosing appropriate alternate implementations at run-time

5. 5 Related Research CategoryRelated Research Component Middleware Wang, N. et al, Applying Reflective Techniques to Optimize a QoS-enabled CORBA Component Model Implementation, 24th Annual International Computer Software & Applications Conference Taipai, Taiwan, October 2000. Teiniker, E. et al, Local Components & Reuse of Legacy Code in the CORBA Component Model, EUROMICRO 2002, Dortmund, Germany (2002) Diaconescu, A. & Murphy, J., Automating the Performance Management of Component-based Enterprise Systems through the Use of Redundancy, Proceedings of the 20th IEEE/ACM international Conference on Automated Software Engineering (Long Beach, CA, USA, November 07 - 11, 2005). Gurdip Singh & Sanghamitra Das, Customizing Event Ordering Middleware for Component-Based Systems, pp. 359-362, Eighth IEEE International Symposium on Object-Oriented Real-Time Distributed Computing (ISORC'05), 2005. Web ServicesGao, L et al, 2003, Application specific Data Replication for Edge Services, In Proceedings of the 12th International Conference on World Wide Web (Budapest, Hungary, May 20 - 24, 2003). WWW '03. ACM Press, New York, NY, 449-460. Mukhi, N. K., 2004, Cooperative Middleware Specialization for Service Oriented Architectures, Proceedings of the 13th International World Wide Web Conference on Alternate Track Papers & Posters (New York, NY, USA, May 19 - 21, 2004).

6. 6 Related Research: What’s missing? Lack of a high-level notation to guide optimization frameworks Missing AST of application

7. 7 Related Research: What’s missing? Lack of a high-level notation to guide optimization frameworks Missing AST of application Detection at run-time (reflection) Additional overhead in the fast path Not suitable for DRE systems Intrusive optimizations, i.e., not completely application transparent Requires providing multiple implementations, e.g., EJB Optimization performed either Too early, or too late

8. 8 1.Overhead of platform mappings Blind adherence to platform semantics Inefficient middleware glue code generation per component Example: Creation of a factory object per component Servant glue- code generation for every component Need optimization techniques to build large-scale component systems! Application Specific Optimizations: Unresolved Challenges

9. Solution Approach: Physical Assembly Mapper (PAM) 9 Physical Assembly Mapper Uses the application model as the input Exploits knowledge about platform semantics to rewrite the input model to a functionally equivalent output model Generates middleware glue- code Generates deployment configuration files Operates just before deployment Can be viewed as a “deployment compiler”

10. PAM Inputs Set of 3 input graphs G 1 : (V 1, E 1 ), V 1 = { Application Components} E 1 = { Connections between application components} G 2 : (V 2, E 2 ), V 2 = V 1 U {QoS configuration options on components} E 2 = { Connections between the components and the QoS options} G 3 : (V 3, E 3 ), V 3 = V 1 U {nodes in the target deployment domain} E 3 = {Connections between the components and the nodes} 10

11. PAM Output 11 Produces an output graph G o : (V o, E o ), V o = {Physical assemblies created on a single node} E o = {Connections between the composites} Creation of physical assemblies subject to a number of constraints Average case |V o | < |V 1 | Worst case |V o | = |V 1 |, i.e., the optimizer couldn’t create any physical assemblies Equivalent to deployment of original application

12. Physical Assembly 12 Physical Assembly Created from the set of components that are deployed within a single process of a target node Subject to various constraints Example constraints include: No two ports of the set of components should have the same name No two components should have incompatible RT- CORBA policies No changes required to individual component implementations

13. Physical Assembly Generation 13 Given set of components deployed on a single process of a target node Compute pair-wise intersections of component port name sets If intersection is null, merge the two components into a physical assembly Additional RT-CCM constraint If intersection is null & RT- CORBA policies of the components are compatible e.g., PriorityPropagation Models, PriorityBands Physical Assembly indistinguishable to external clients All valid operations on individual components are still valid

14. Middleware Concepts Exploited by Physical Assemblies 14 Opacity of object references Components don’t rely on specific details of object references, e.g., location of type information Allows replacing references transparent to component implementations

15. Middleware Concepts Exploited by Physical Assemblies 15 Opacity of object references Components don’t rely on specific details of object references, e.g., location of type information Allows replacing references transparent to component implementations Presence of a component context Components access ports of other components using a context object Allows replacing context transparent to component implementations Container Servant Component Specific Context CCMContext Main Component Executor Executors POA EnterpriseComponent CCMContext Container Servant Component Specific Context CCMContext Main Component Executor Executors POA EnterpriseComponent CCMContext user implemented code Container CORBA Component POA E x t e r n a l I n t e r f a c e s Interna l Interface s

16. Middleware Concepts Exploited by Physical Assemblies 16 Opacity of object references Components don’t rely on specific details of object references, e.g., location of type information Allows replacing references transparent to component implementations Presence of a component context Components access ports of other components using a context object Allows replacing context transparent to component implementations Clean separation between glue-code & component implementation Allows modifications transparent to component implementations Technique can be applied to other middleware with these properties

17. 17 Physical Assembly Evaluation Criteria Footprint of physical assembly components Compared to vanilla Component- Integrated ACE ORB (CIAO) Different scenarios Simple scenarios (<= 10 components) ARMS GateTest scenarios (150+ components) Reduce static & dynamic footprint Reduce no. of homes by (n – 1) / n Reduce no. of objects registered with POA by (n – 1) / n Reduce no. of context objects created by (n – 1) / n n = no. of components deployed on a single process of a target node

18. Evaluating Physical Assemblies Boeing’s BoldStroke Basic Single Processor Scenario Consists of 4 components Timer – Periodically sends refresh signal to the GPS GPS – Calculates new co-ordinates of the aircraft in response to Timer signal Airframe – Processes new location inputs from GPS Display – Updates the new location of the aircraft in the navigation display 18

19. Applying Physical Assemblies to BasicSP Scenario Assumption: GPS, Airframe and Display components are deployed on a single processor board Applying physical assemblies Combines GPS, Airframe and Display components into a single physical assembly (BasicSPAsm) Maintains the same number of connections Timer component not combined (due to clash in port names) 19

20. Experimental Results: BasicSP Scenario Testbed Linux 2.6.20 FC6 Dual 2.4Mhz processor 1.5GB RAM Evaluation for larger applications (~150 components) is in progress 20 Physical assembly mapping reduces the footprint significantly Static footprint reduction of ~45% Dynamic footprint reduction of ~10%

21. 21 Application Specific Optimizations: Unresolved Challenges 2.Lack of application context Optimization decisions relegated to run-time e.g., every invocation performs check for locality Settle for near- optimal solutions Missed middleware optimization opportunities

22. 22 Application Specific Optimizations: Unresolved Challenges 2.Lack of application context Optimization decisions relegated to run-time e.g., every invocation performs check for locality Settle for near- optimal solutions Missed middleware optimization opportunities e.g., allocation of RT-CORBA thread pools and lanes Large overhead compared to collocated calls Cannot be solved efficiently at middleware level alone! Invocation within the same pool/lane (12µs) Invocation to a different pool/lane (150µs)

23. Solution Approach: Use Application Context from Models Use application structure & context available in models Create fast path within middleware for physical assemblies Cross-pool/lane proxy 23

24. Solution Approach: Use Application Context from Models Use application structure & context available in models Create fast path within middleware for physical assemblies Cross-pool/lane proxy Utilize available fast path in PAM e.g., Use matching real-time policies as additional constraint when creating physical assemblies 24

25. Solution Approach: Use Application Context from Models Use application structure & context available in models Create fast path within middleware for physical assemblies Cross-pool/lane proxy Utilize available fast path in PAM e.g., Use matching real-time policies as additional constraint when creating physical assemblies Configure middleware resources efficiently e.g., allocate physical assemblies with matching policies in the same thread pool or thread pool with lanes 25

26. 26 Context-driven Optimization Evaluation Criteria Improve performance t = no. of cross-pool/lane interactions between components within an assembly Transform t remote calls to t cross-pool/lane calls Eliminate mis-optimizations Check incompatible POA policies Incompatible invocation semantics (oneway or twoway) No changes to individual component implementations Eliminate need for a local vs. remote version Customizable & application transparent

27. Experimental Results: Cross pool/lane proxy 27 Standard deviation <= 3µs Max latency improved by ~50% 99% latency improved by 60-66% Average latency improved by 60 – 66% Significant performance benefits with cross pool/lane proxy!

28. 28 Summary of Research Contributions CategoryBenefits System Optimization Technologies Novel mechanism for mapping an assembly as a component to both reduce application footprint and increase performance Automatic discovery & realization of optimizations from models in an application transparent fashion Performs optimizations that are impossible to perform if operating at the middleware layer alone Optimized allocation & configuration of middleware resources using application context derived from models

29. QoS Mapping Context 29 Benefits of QoS- enabled middleware technologies Raise the level of abstraction Support many quality of service (QoS) configuration knobs

30. QoS Mapping Context 30 Container COMPONENT EXECUTORS Component Home POA Callback Interfaces I n t e r n a l I n t e r f a c e s E v e n t S i n k s F a c e t s R e c e p t a c l e s E v e n t S o u r c e s Component Reference C o m p o n e n t C o n t e x t COMPONENT SERVER Drawbacks of QoS-enabled middleware technologies Achieving desired QoS increasingly a system QoS configuration problem, not just an initial system functional design problem Benefits of QoS- enabled middleware technologies Raise the level of abstraction Support many quality of service (QoS) configuration knobs Lack of effective QoS configuration tools result in QoS policy mis- configurartions that are hard to analyze & debug ORB

31. Example Application: Satellite Mission Satellite mission consists of four identically instrumented spacecraft & a ground control system Collect mission data Send it to ground control at appropriate time instances 31

32. Example Application: Satellite Mission Satellite mission QoS requirements span two dimensions Multiple modes of operation Varying importance of data collection activity of satellite sensors based on mission phase Need to translate QoS policies into QoS configuration options & resolve QoS dependencies 32 Slow Survey Fast Survey Burst

33. Challenge 1: Translating QoS Policies to QoS Options 33 Prioritized service invocations (QoS Policy) must be mapped to Real-time CORBA Banded Connection (QoS configuration) Large gap between application QoS policies & middleware QoS configuration options Bridging this gap is necessary to realize the desired QoS policies The mapping between application-specific QoS policies & middleware- specific QoS configuration options is non-trivial, particular for large systems Policy for handling of dangling(ill- behaved) publishers(subscribers) (QoS Policy) must be mapped to control policy and control period (QoS configuration)

34. Challenge 1: Translating QoS Policies to QoS Options 34 Conventional mapping approach requires deep understanding of the middleware configuration space e.g., multiple types/levels of QoS policies require configuring appropriate number of thread pools, threadpool lanes (server) & banded connections (client) Protocol Properties Explicit Binding Client Propagation & Server Declared Priority Models Portable Priorities Thread Pools Static Scheduling Service Standard Synchonizers Request Buffering

35. Challenge 2: Choosing Appropriate QoS Option Values 35 Individually configuring component QoS options is tedious & error-prone e.g., ~10 distinct QoS options per component & ~140 total QoS options for entire satellite mission Manually choosing valid values for QoS options does not scale as size & complexity of applications increase

36. Challenge 3: Validating QoS Options 36 Each QoS option value chosen should be validated e.g., Filter priority model is CLIENT_PROPAGATED, whereas Comm priority model is SERVER_DECLARED Each system reconfiguration (at design time) should be validated e.g., reconfiguration of bands of Analysis should be validated such that the modified value corresponds to (some) lane priority of the Comm

37. Challenge 4: Resolving QoS Option Dependencies “QoS option dependency” is defined as: Dependency between QoS options of different components Manually tracking dependencies is hard – or in some cases infeasible Dependent components may belong to more than one assembly Dependency may span beyond immediate neighbors –e.g., dependency between Gizmo & Comm components Empirically validating configuration changes by hand is tedious, error- prone, & slows down development & QA process considerably Several iterations before desired QoS is achieved (if at all) 37 ThreadPool priorities of Comm should match priority bands defined at Gizmo

38. 38 Solution Approach: Model-Driven QoS Mapping QUality of service pICKER (QUICKER) Model-driven engineering (MDE) tools model application QoS policies Provides automatic mapping of QoS policies to QoS configuration options Validates the generated QoS options Automated QoS mapping & validation tools can be used iteratively throughout the development process

39. Enhanced Platform Independent Component Modeling Language (PICML), a DSML for modeling component-based applications QoS mapping uses Graph Rewriting & Transformation (GReAT) model transformation tool Customized Bogor model- checker used to define new types & primitives to validate QoS options 39 QUICKER Enabling MDE Technologies

40. Enhanced Platform Independent Component Modeling Language (PICML), a DSML for modeling component-based applications QoS mapping uses Graph Rewriting & Transformation (GReAT) model transformation tool Customized Bogor model- checker used to define new types & primitives to validate QoS options 40 QUICKER Enabling MDE Technologies CQML Model interpreter generates Bogor Input Representation (BIR) of DRE system from its CQML model CQML Model Interpreter Bogor Input Representation

41. 41 QUICKER Concepts: Transformation of QoS policies(1/2) RequirementProxy can be per component or assembly instance 1.Platform-Independent Modeling Language (PICML) represents application QoS policies PICML captures policies in a platform-independent manner Representation at multiple levels e.g., component- or assembly-level

42. 42 QUICKER Concepts: Transformation of QoS policies(1/2) 1.Platform-Independent Modeling Language (PICML) represents application QoS policies PICML captures policies in a platform- independent manner Representation at multiple levels e.g., component- or assembly-level 2.Component QoS Modeling Language (CQML) represents QoS options CQML captures QoS configuration options in a platform-specific manner

43. 43 3.Translation of application QoS policies into middleware QoS options Semantic translation rules specified in terms of input (PICML) & output (CQML) type graph e.g., rules that translate multiple application service requests & service level policies to corresponding middle- ware QoS options QUICKER transformation engine maps QoS policies (in PICML) to QoS configuration options (in CQML) QUICKER Concepts: Transformations of QoS policies(2/2) Provider Service Request Provider Service Levels Level 1 Level 2 Level 3 Multiple Service RequestsService Levels Priority Model Policy Thread Pool Lanes

44. 44 QUICKER Concepts: Validation of QoS Options (1/2) 1.Representation of middleware QoS options in Bogor model-checker BIR extensions allow representing domain-level concepts in a system model QUICKER defines new BIR extensions for QoS options Allows representing QoS options & domain entities directly in a Bogor input model –e.g., CCM components, Real- time CORBA lanes/bands are first-class Bogor data types Reduces size of system model by avoiding multiple low-level variables to represent domain concepts & QoS options

45. 45 2.Representation of properties (that a system should satisfy) in Bogor BIR primitives define language constructs to access & manipulate domain-level data types, e.g.: Used to define rules that validate QoS options & check if property is satisfied 3.Automatic generation of BIR of DRE system from CQML models QUICKER Concepts: Validation of QoS Options (2/2) Rule determines if ThreadPool priorities at Comm match priority bands at Analysis Model interpreters auto-generate Bogor Input Representation of a system from its CQML model

46. Resolving Challenge 1: Translating Policies to Options (1/2) 46 Expressing QoS policies PICML modes application-level QoS policies at high-level of abstraction e.g., multiple service levels support for Comm component, service execution at varying priority for Analysis component, prioritizing event dispatching at Analysis component Reduces modeling effort e.g., ~25 QoS policy elements for satellite mission vs. ~140 QoS options

47. Resolving Challenge 1: Translating Policies to Options (2/2) Mapping QoS policies to QoS options GReAT model transformations automate the tedious & error-prone translation process Transformations generate QoS configuration options as CQML models Allow further transformation by other tools e.g., code optimizers & generators Simplifies application development & enhances traceability 47

48. Resolving Challenges 2 & 3: Ensuring QoS Option Validity CQML model interpreter generates BIR specification from CQML models BIR primitives used to check whether a given set of QoS options satisfies a system property e.g., fixed priority service execution, a property of Comm component Supports iterative validation of QoS options during QoS configuration process 48 QUICKER

49. Resolving Challenge 4: Resolving QoS Option Dependencies Change(s) in QoS options of dependent component(s) triggers detection of potential mismatches e.g., dependency between Gizmo invocation priority & Comm lane priority 49 Dependency structure maintained in Bogor used to track dependencies between QoS options of components, e.g.: Analysis & Comm are connected Gizmo & Comm are dependent Detect mismatch if either values change Dependency Structure of Satellite Mission Components

50. Summary of Research Contributions QUICKER provides Model-Driven Engineering (MDE) for QoS-enabled component middleware Maps application-level QoS policies to middleware-specific QoS configuration options Model transformations automatically generate QoS options Model-checking extensions ensure validity of QoS options at component- & application- level 50