November 23, 2005 Egor Bondarev, Michel Chaudron, Peter de With Scenario-based PA Method for Dynamic Component-Based Systems Egor Bondarev, Michel Chaudron, Peter de With Eindhoven University of Technology, The Netherlands

November 23, 2005 Egor Bondarev, Michel Chaudron, Peter de With Problem Statement -To accurately predict the performance attributes -of software component-based systems -built on multiprocessor architectures Performance attributes Timing behaviour, Processor usage, Memory consumption, Bus load

November 23, 2005 Egor Bondarev, Michel Chaudron, Peter de With Example Problem: Car Navigation System (CNS)

November 23, 2005 Egor Bondarev, Michel Chaudron, Peter de With CNS: Architectural Alternatives 1. Predict performance for every alternative 2. Find optimal alternatives in terms of: Resource usage+ Performance + Robustness + …

November 23, 2005 Egor Bondarev, Michel Chaudron, Peter de With Proposed Solution 1.Models for both software- and hardware components. 2.Scenarios-based evaluation The designer can focus on critical run-time configurations. Allows trade-off between modeling effort and accuracy. 3.Simulation of scenarios + schedulability analysis Based on the following concepts

November 23, 2005 Egor Bondarev, Michel Chaudron, Peter de With Performance Prediction Approach (2/2) Input Application requirements Design (assemble) Real-time application Models Component Resource model Component Resource model Component Behaviour model Component Behaviour model has Compile models / reconstruct tasks Execution architecture (tasks) Simulate task execution Task execution timeline Validate Real-time and performance properties Analyze predicted for Construct Application Scenario model Application Scenario model ! ! ! Real-time aware components Real-time aware components Select

November 23, 2005 Egor Bondarev, Michel Chaudron, Peter de With Behaviour and Resource Models BehavourModel_MPEG4Decoder_Component behaviour operation IDecode.decodeFrame() calls IBufferAccess.getElement() passedBits = 0 returnedBits = 1024 synchronous = TRUE numberOfIterations = 1 calls IBufferAccess.storeElement() passedBits = 1024 returnedBits = 1 synchronous = TRUE numberOfIterations = 1 BehavourModel_MPEG4Decoder_Component behaviour operation IDecode.decodeFrame() calls IBufferAccess.getElement() passedBits = 0 returnedBits = 1024 synchronous = TRUE numberOfIterations = 1 calls IBufferAccess.storeElement() passedBits = 1024 returnedBits = 1 synchronous = TRUE numberOfIterations = 1 ResourceModel_MPEG4Decoder_Component resource use operation IDecode.decodeFrame() cpu claim max = 1E7 cycles (reference processor) aver = 1E5 cycles (reference processor) min = 1E4 cycles (reference processor) mem claim = 10 KB mem release = 3 KB ResourceModel_MPEG4Decoder_Component resource use operation IDecode.decodeFrame() cpu claim max = 1E7 cycles (reference processor) aver = 1E5 cycles (reference processor) min = 1E4 cycles (reference processor) mem claim = 10 KB mem release = 3 KB IDecodeIBufferAccess decodeFrame() getElement() storeElement()

November 23, 2005 Egor Bondarev, Michel Chaudron, Peter de With Composition of Software Components

November 23, 2005 Egor Bondarev, Michel Chaudron, Peter de With Composition of Hardware Components 1.Performance model specifies IP processing capabilities. For processing core frequency rate and scheduling policy; For memory a memory size and addressing type; For bus bit-size, frequency and arbitration policy

November 23, 2005 Egor Bondarev, Michel Chaudron, Peter de With SW / HW Mapping

November 23, 2005 Egor Bondarev, Michel Chaudron, Peter de With Application Scenario Modelling A scenario model defines environmental events or system interrupts application level stimuli (task triggers) for a specific hw/sw-configuration (composition structure)

November 23, 2005 Egor Bondarev, Michel Chaudron, Peter de With The generated task specifies sequence of constituent method invocations period, deadline, priority, synchronization constraints Composing the Models Service_A Service_B Operation_B Operation_C Service_CService_D Operation_D Operation_F Service_F Operation_E Task Trigger (period 40 ms) Operation_A TaskTrigger invoke InterfaceX.OperationA period 40 ms offset 0 ms deadline 40 ms Application Scenario Model: Component Resource Model: Operation_E CPU claim = 3ms 30 ms Component Behavior Model: OperationA() calls InterfaceZ.OperationB() InterfaceY.OperationC() nmbIterations = 1

November 23, 2005 Egor Bondarev, Michel Chaudron, Peter de With Scenario Simulation + Analysis Simulation time Mem load Simulation time Bus load Simulation or Schedulability analysis are performed with scheduling algorithms deployed on the target OS (RMA, EDF, CBS) Simulation results in task latencies, number of missed deadlines, CPU, memory and bus utilization

November 23, 2005 Egor Bondarev, Michel Chaudron, Peter de With Not mentioned Facilities and Benefits Modelling of parameter-dependent behaviour and resource usage Multiple-platform resource models Task synchronization aspects can be modeled Component mapping on multiprocessor architecture Multidimensional design space exploration robusteness vs cost, memory_load vs cpu_load, etc

November 23, 2005 Egor Bondarev, Michel Chaudron, Peter de With Framework Deployment Issues We have developed a tool chain supporting the design activities We have validated the prediction approach by MPEG4 Decoder case study: prediction accuracy of task latencies is > 90% Real-Time Prediction Framework