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SymTA/SSymbolic Timing Analysis for Systems
SymTASymTA/S/SSymbolicSymbolic Timing Analysis Timing Analysis forfor SystemsSystems
Razvan Racu
Arne Hamann
ARTIST2 PhD Course, June 12, DTU Copenhagen, Denmark
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 2
Day scheduleDay schedule
0900 – 0945 Introduction to system performance verification
1000 – 1045 Compositional performance analysis
1100 – 1200 Hands-on tutorial 1: Basics SymTA/S
1330 – 1415 Sensitivity analysis
1430 – 1515 Design space exploration and robustness optimization
1530 – 1630 Hand-on tutorial 2: Advanced SymTA/S features
1630 – 1700 Discussion
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System System designdesign challengeschallenges
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Functional vs. performance verificationFunctional vs. performance verification
Separate function verification from performance verification
functional verification/test determines functional correctness independent of the target architecture
performance verification/test determines platform adherence to
load conditions and response times (deadlines)jitter boundsbuffer sizes
This presentation is about performance verification !!
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IntroductionIntroduction
implementation languagearchitecture layer
application layer
subsystem 2Simulink
inputlanguage 2
subsystem 3
subsystem 1
IP
UML
application development
M
CoP
M
M
PDSP
M
P
core
RTOS
I/O Int Bus-CTRL
timertimer
drivers
RTOS-APIs
application
implementation
target platform
system function
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Embedded system platform propertiesEmbedded system platform properties
ES platforms are heterogeneouscomponentsnetworkscommunication scheduling (static, dynamic, event-, time-driven, ...)...
Heterogeneity results fromhardware and software component specialization (cost, power, dependability)HW/SW reuse
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CoPro
HeterogeneousHeterogeneous resourceresource sharingsharing
VLIW MEMIPIP IPIPMEM
RISC MEM DSP
comcom. . netwnetw..
static executionorder scheduling
static priorityschedulingFCFS scheduling
earliest deadlinefirst scheduling
TDMA scheduling
proprietary(abstract info)
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ExempleExemple 1 : MPSOC1 : MPSOC
Heterogeneity resulting fromhardware and software component specialization reuse
External Bus UnitExternal Bus Unit
SRAM (32 KB)I-Cache (1 KB)
ROM (4 KB)
SRAM (32 KB)I-Cache (1 KB)
ROM (4 KB)
FPI Bus
CAN BusInterface (2)CAN Bus
Interface (2)System Timer
System Timer
Data SRAM(40 KB)
Data SRAM(40 KB)
PeripheralCore
Processor
PeripheralCore
Processor
PortsPorts
RAM (4 KB)RAM (4 KB)
CodeRAM
(16 KB)
CodeRAM
(16 KB)
Bus InterfaceBus InterfaceASC(2)ASC(2)
SSC(2)SSC(2)
ADC(2)ADC(2)
GPTA(1)GPTA(1)
Tricore
External Bus UnitExternal Bus Unit
SRAM (32 KB)I-Cache (1 KB)
ROM (4 KB)
SRAM (32 KB)I-Cache (1 KB)
ROM (4 KB)
FPI Bus
CAN BusInterface (2)CAN Bus
Interface (2)System Timer
System Timer
Data SRAM(40 KB)
Data SRAM(40 KB)
PeripheralCore
Processor
PeripheralCore
Processor
PortsPorts
RAM (4 KB)RAM (4 KB)
CodeRAM
(16 KB)
CodeRAM
(16 KB)
Bus InterfaceBus InterfaceASC(2)ASC(2)
SSC(2)SSC(2)
ADC(2)ADC(2)
GPTA(1)GPTA(1)
TricoreTriCore 1775 (automotive)Philips VIPER (consumer)
Bus
corecore
RTOS
I/O Int Bus-CTRL
timertimer
I/O Int Bus-CTRL
timertimer
drivers
RTOS-APIs
application
cache
memprivate
private
private
private
shar
ed
architecture
application
architecture
application
ce1
pe1
API
multilayered SW
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ExampleExample 2: Automotive 2: Automotive PlatformPlatform
Heterogeneous50+ ECUsmany suppliersseveral RTOSes and protocolsstrongly networked
Complexend-to-end deadlineshidden dependenciesglobal memories
ACCABS
ESP ASR
enginecontrol powertrain
control
gateway
ECU1
diagnosis
CAN1 CAN2
FlexRay
ECU2
ECU3
ECU4
ECU5
ECU6
ECU8ECU7
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Function 2Function 1 Function 3
BSWRTE
Function 1
CAN
M2N7
BSWRTE
Function 2
M2N7
BSWRTE
Function 3
M2N7
EndEnd--toto--end times do not easily composeend times do not easily compose
endtondRFunctionRFunctionRFunction tttt −−≠++ Re321
endtondt −−Re
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Design as integration problemDesign as integration problem
M2IP2M3
M1
Bus
DSPIP1
HWCPU
IntegrationM2
IP2M3
DSP
IP1
subsystem 2
M1
HW
CPU
subsystem 1
P1P3
P2
Sens
Sens
subsystem 2
subsystem 1
System design is to a large extend an integration problem
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CouplingCoupling effectseffects –– a a closercloser looklook
Example: 3 periodic tasks on CPU send data over thebus
Static priority scheduling on CPU: P1 > P2 > P3
P1P2 P3
M2IP2M3
M1
Bus
DSPIP1
HWCPUSens
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CouplingCoupling effectseffects –– creationcreation of of burstsbursts
Complex execution traces with dynamic behavior
Burst events at the output
Consequences: transient overload, missed deadlines, data loss, ...
T1
T2
T2
T2 T2
P3
P 2
Prio
ritätperiodic
input
burstyoutput
P1
t
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SchedulingScheduling anomaliesanomalies
System corner-cases different of componentcorner-cases
minimumbus load
maximumexecution time
minimumexecution time
maximumbus loadP1
P2 P3
M2IP2M3
M1
Bus
DSPIP1
HWCPUSens
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Key platform design challengesKey platform design challenges
Increasing system complexity
from single processor to multi-processor (MpSoC)
from buses to networks (NoC)
Complex dependencies and modifications threaten design robustness
Global end-to-end constraints added for control applications
Integration under optimization requirements
cost (memory, power, …)
robustness
extendibility – consider upcoming features, SW updates, platform updates in product lines
Reliable system integration is key requirement
Performance verification required at every design stage
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Requirements
System Design System Test
Requirements Test
Module Design
Function Design Function Test
Module Test
ArchitectureExploration
Network TimingEstimation
Timing is everywhereTiming is everywhere
ECU TimingEstimation
ECU TimingVerification
Network TimingVerification
System-Timing
Verification
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Performance Performance verificationverification flowflow
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Target architecture performance Target architecture performance –– general view general view
process execution model
P1 P2
P1
M
IP
M P M P
M
global system execution model
activationcomponent and communication
execution model
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Process execution model
single process executionP1
then...
else {send(...);receive (...);... }
for { ...
..}
if ... b1 b2
b3
b4
P1
Influenced byexecution path
data dependent
execution path timingtarget architecture dependent
process communication (here: message passing)
execution path dependent
communication volumedata and type dependent
execution time analysis
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Process timing and communicationProcess timing and communication
State of industrial practice - simulation/performance monitoring
trigger points at process beginning and enddata dependent execution upper and lower timing bounds
simulation challenges coverage?cache and context switch overhead due to run-time scheduling with process preemptions
Alternative - formal analysis of individual process timing
provides conservative boundsserious progress in recent years
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Formal process execution time analysis Formal process execution time analysis
Active research area with dedicated events (e.g. Euromicro WS)
Formal analysis using simple processor modelsLi/Malik (Princeton) (95): Cinderella
Detailed execution models with abstract interpretationWilhelm/Ferdinand (97 ff.): commercial tool AbsInt
Combinations with simulation/measurement of program segments
Wolf/Ernst (99): SymTA/P
All tools provide (conservative) upper execution time bounds (WCET) or time intervals (WCET/BCET)
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Component and communication execution model
P1 P2
activation
P1
M
IP
M P M P
M Influenced byresource sharing strategyprocess activation
single component real-time analysis
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Component and communication execution modelComponent and communication execution model
Resource sharing strategy
process and communication schedulingstatic execution ordertime driven scheduling
fixed: TDMAdynamic: Round-Robin
priority driven schedulingstatic priority assignment: RMS, SPPdynamic priority assignment: EDF
Timing depends on environment modeldetermines frequency of process activations or communication
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CoPro
SchedulingScheduling Analysis Analysis TechniquesTechniques
VLIW MEMIPIP IPIPMEM
RISC MEM DSP
SYSTEM BUSSYSTEM BUS
Lee/Messerschmidt1989
Liu/Layland 1973Buttazzo 1993
Sha 1994
Kopetz 1993
from IP vendor
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Example: Rate Monotonic Scheduling (RMS)Example: Rate Monotonic Scheduling (RMS)
Very simple system modelperiodic tasks with deadlines equal to periodsfixed priorities according to task periodsno communication between tasks(theoretically) optimal solution for single processorsseveral practical limitations but good starting point
Schedulability tests for RMS guarantee correct timing behavior
processor utilization (load) approachresponse time approach (basis for many extensions)
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RMS RMS TheoryTheory –– TheThe responseresponse time time approachapproach
Critical instant:all tasks start at t=0 („synchronous assumption“ to ensuremaximum interference in the beginning of task execution)
when each task meets its first deadline, it will meet all otherfuture deadlines (proof exists!)
test by „unrolling the schedule“ (symbolic simulation)
deadline = period = 350 deadline is met
criticalinstant
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RMS RMS TheoryTheory –– TheThe responseresponse time time formulaformulafix-point problem
response time
core execution time
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T1
C2T2
T2
C2T2
C2T2
T 2
prio
rity C2
C2
C1 C1T1
ExampleExample: : StaticStatic prioritypriority w/ w/ arbitraryarbitrary deadlinesdeadlines
Assume: tasks with periods T, worst-case execution times Cstatic prioritiesdeadlines (arbitrary) larger than the period
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Analysis Analysis usesuses ““BusyBusy WindowWindow” ” approachapproach ((LehoczkyLehoczky))T1
C2T2 T2
T 2pr
iorit
yC2
C1 C1T1
C2T2 C2T2
w2(3)2 * T2 R2(3)
find fix point where
equationshold!
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OtherOther ExtensionsExtensions in in LiteratureLiterature
Jitter and burst activation
Static and dynamic offsets between task activations
Different task modes
Execution scenarios
Blocking and non-preemptiveness
Scheduling overhead context switch time
etc...
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Global system execution model
P1 P2
activation
P1
M
IP
M P M P
M
global real-time system analysis
influenced bycommunication patternshared memory accessenvironment model
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System System performanceperformance analysisanalysis
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System performance analysis System performance analysis -- state of the art 1/2state of the art 1/2
Current approach: target architecture co-simulation, performance simulation
Simulation challenges
identification of system performance corner casesdifferent from component performance corner casescomplex phase and data dependent “transient” run-time effects w. scheduling anomaliestarget architecture behavior unknown to the application functiondevelopertest case definition and selection?
simulation of incomplete application specifications ?how to do design space exploration before code implementation isavailable?
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System performance analysis System performance analysis -- state of the art 2/2state of the art 2/2
Load analysisExample: “all deadlines are met if the resource load is below 69%”Consider only average scenarios (no transient load)No performance metrics no constraint validation
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Popular as a system level technique for safety critical systems design
Strict separation of subsystemsfixed allocation of memoryfixed allocation of communication resourcesfixed allocation of computation resources
Spatial and temporal decoupling of resourcesnot-in-use allocated parts are lockedno coupling effects
Requires system synchronization …
… paid by timing overhead
ConservativeConservative designdesign
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 36
Bus
TDMA 1/2TDMA 1/2
Time Triggered System (TDMA)
periodic assignment of fixed time slots for communication and processing
unused slots remain empty
requires system synchronization
no coupling effects
time slot assigned to sender P1
context switching time
tTDMA
tP1 tP2 tP3 tP1 tP2 tP3 tP1 tP2 tP3
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 37
TDMA 2/2TDMA 2/2
Predictable, independent system capacity
Ri response time Pi, Ci core execution time Pi
Used in avionics and automotive (TTP, FlexRay)
Can be used at system level (Giotto - Berkeley)
⎥⎥
⎤⎢⎢
⎡×−+=
Pi
iPiTDMAii t
CttCR )(
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 38
Conservative design Conservative design -- SummarySummary
Limitationslow resource utilizationextended response times (problem for adaptive control engineering)requires general time base (scalability?)little flexibility (fixed time slots)not a general solutioninefficiency (performance, bandwidth, costs, power) increases with system size
Time-triggered systems are a good example for systematic integration, but…
… reliable integration does not necessarily requireconservative design style
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 39
System System levellevel performanceperformance analysisanalysis
Global approach („Holistic“)
local analysis scope extension to severalsubsystems
Compositional approach
global flow analysis combined with local schedulinganalysis
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Analysis Analysis scopescope extensionextension –– „„HolisticHolistic““
Coherent analysis („holistic“ approach)
Example: Tindell 94, Palencia/Harbour 98, Pop/Eles (DATE 2000, DAC 2002): TDMA + static priority – automotive applications
Problem: scalability
P2 P1
T
TTP businterface
P3 P4
D
TTP businterfacequeue
RTOSRTOS
TTP bus (TDMA)
static priorityprocess scheduling
static priorityqueueingT: Transmitter
process
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 41
Analysis Analysis scopescope extensionextension ((cont‘dcont‘d))
Benefit: scope extension can take global system knowledge intoaccount
Example: using dependency information to detect that P2 cansend in the same TDMA round as P1, if RP2 < tP3 + tP4, where RP2 isthe worst-case response time of P2
P1
TDMA bus P1 tP3 tP4 P2 tP1 tP3 tP4 tP2
tround
tP1
CPU1
P2HW1
tP2
P3CPU2
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CompositionalCompositional performanceperformance analysisanalysis
After the break!
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SymTA/SCompositional performance analysis
SymTASymTA/S/SCompositionalCompositional performanceperformance analysisanalysis
Razvan Racu
Arne Hamann
ARTIST2 PhD Course, June 12, DTU Copenhagen, Denmark
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 2
CoPro
Multiple Multiple SchedulingScheduling StrategiesStrategies
VLIW MEMIPIP IPIPMEM
RISC MEM DSP
SYSTEM BUSSYSTEM BUS
static executionorder scheduling
static priorityschedulingFCFS scheduling
earliest deadlinefirst scheduling
TDMA scheduling
proprietary(abstract info)
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 3
CoPro
CorrespondingCorresponding Analysis Analysis TechniquesTechniques
VLIW MEMIPIP IPIPMEM
RISC MEM DSP
SYSTEM BUSSYSTEM BUS
Lee/Messerschmidt1989
Liu/Layland 1973Buttazzo 1993
Sha 1994
Kopetz 1993
from IP vendor
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 4
CoPro
Integration ???Integration ???
VLIW MEMIPIP IPIPMEM
RISC MEM DSP
SYSTEM BUSSYSTEM BUS
Lee/Messerschmidt1989
Liu/Layland 1973Buttazzo 1993
Sha 1994
Kopetz 1993
from IP vendor
?? ?
? ?
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 5
BUS
TDMA
CompositionalCompositional approachapproach
Tasks are coupled by event sequences
Composition by means of event stream propagationapply local scheduling techniques at resource leveldetermine the behavior of the output streampropagate to the next component
DSP
static order
CPU
fixed priority
P1 P3C1
P2
C2
C4C3 P4
P5system inputsystem input system outputsystem output
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IdeaIdea
Use network calculus + additional information as intermediate mathematical formalism
Arrival curve functions of network calculus
η+(Δt) maximum number of activating eventsoccuring in time window Δt
η-(Δt) minimum number of activating eventsoccuring in time window Δt
d– minimum event distance - limits burst density
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Event Event specificationspecification
Derive event stream models with parametersindividual events replaced by stream variables (vectors) with stream parameters period, jitter, min. distance, …derive arrival curve functions from modelparameters
5
0Δt
12
3
4
TJt −Δ
0
TJt +Δ
–J +J
η(Δt)⎥⎥⎤
⎢⎢⎡ +Δ
=Δ+
TJtt)(η
⎥⎦⎥
⎢⎣⎢ −Δ
=Δ−
TJtt)(η
T: periodJ: jitter
5
0Δt
12
3
4
TJt −Δ
0
TJt +Δ
–J +J
η(Δt)⎥⎥⎤
⎢⎢⎡ +Δ
=Δ+
TJtt)(η
⎥⎦⎥
⎢⎣⎢ −Δ
=Δ−
TJtt)(η
T: periodJ: jitter
lower boundupper bound
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SymTASymTA/S /S standardstandard eventevent modelsmodels
Required by RTAPeriodic/sporadicPeriodic/sporadic with jitterPeriodic/sporadic with burst
increasing jitter due to execution/scheduling
Con
ditio
nalo
utpu
t
P P+J
S
P+B
S+J S+B
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Input Input –– outputoutput eventevent modelmodel relationrelation
Any scheduling increases jitter
Jitter grows along functional path
Increasing jitter leads to burst and transient overloadshigher memory requirementspower peaks
busy periodbusy period
T1T1
T2T2T2
T2T2T2
T2T2 T2T2
PE
scheduling PE
P2
P1
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environment model
local analysis
derive output event model
map to input event model
convergence?
schedulability?
YES
NO
NO
YES
infeasibleconfiguration
feasibleconfiguration
System System analysisanalysis looploop
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 11
ReducingReducing transienttransient loadload in in designdesign
Re-synchronization
Minimum event separation using „traffic shaping“
Requires memory and possibly increases latency
shaper
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 12
Traffic shaping Traffic shaping -- exampleexample
+J0
η(Δt)
34
5
6
5
TJt +Δ
12
Δt
TJt −Δ
)( tΔ+η
)( tΔ−η
−
Δdt
+J0
η(Δt)
34
5
6
5
TJt +Δ
12
Δt
TJt −Δ
)( tΔ+η
)( tΔ−η
−
Δdt
0
η(Δt)
34
5
6
5
TJt +Δ
12
−
Δdt
Δt
TJt −Δ
+J
)( tΔ+η
)( tΔ−η
d-
shaping
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Optimization potential of Traffic ShapingOptimization potential of Traffic Shaping
16 25 28
12 21 24
d=10
6
d=10
6
d=12
8
d=12
8
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RTA event models are not sufficientRTA event models are not sufficient
Event model transitions needed to couple different subsystems and scheduling domains
More complex activation models needed
OR activation typical in event driven systems
AND activation and loops typical for signal processing AND
OR
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Analysis Analysis extensionsextensions
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 16
environment model
local analysis
derive output event model
map to input event model
convergence?
schedulability?
YES
NO
NO
YES
System System analysisanalysis looploop
environment model
context-awareanalysis
derive output event model
map to input event model
convergence?
schedulability?
YES
NO
NO
YES
contextcontext infoinfo
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 17
Taking global dependencies into accountTaking global dependencies into account
„intra-context“ dependencies
different events in a single event stream often activate different task behaviors with different execution times or communication loads
„inter-context“ dependencies
activating events in different event streams are often time-correlated which rules out the simultaneous activation of all tasks
can be combined leading overall to less conservative analysis results
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T5
T3
T8
R4R3
T7
R5
T4
T9
Source
T6
T2
R2
R1
T1
CET = [2,2]Priority=Mid
CET = [2,2]Priority=High
CET = [2,2]Priority=Low
CET = [2,2]Priority=High
CET = [2,2]Priority=Low
CET = [0,2]Priority=High
CET = [10,10]Priority=High
CET = [2,2]Priority=Low
CET = [2,8]Priority=High
MotivatingMotivating ExampleExample
•Compositional performance analysis approach (Richter)
P = 50J = 0
P5 = 50J5 = 8
P3 = 50J3 = 8
P8 = 50J8 = 6
•Static priority preemptive scheduling on all resources
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 19
T5
T3
T8
R4
CET = [2,2]Priority=Mid
CET = [2,2]Priority=High
CET = [2,2]Priority=Low
R3
T7
CET = [2,2]Priority=High
R5
T4
T9
Source
T6
T2
R2
CET = [2,2]Priority=Low
CET = [0,2]Priority=High
CET = [10,10]Priority=High
CET = [2,2]Priority=Low
R1
T1
CET = [2,8]Priority=High
Lehoczky (1990)LehoczkyLehoczky (1990)(1990)
•Ignore correlation between tasks!
P = 50J = 0
P5 = 50J5 = 8
P3 = 50J3 = 8
P8 = 50J8 = 6
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 20
T5
T3
T8
R4
CET = [2,2]Priority=Mid
CET = [2,2]Priority=High
CET = [2,2]Priority=Low
R3
T7
CET = [2,2]Priority=High
R5
T4
T9
Source
T6
T2
R2
CET = [2,2]Priority=Low
CET = [0,2]Priority=High
CET = [10,10]Priority=High
CET = [2,2]Priority=Low
R1
T1
CET = [2,8]Priority=High
Lehoczky (1990)LehoczkyLehoczky (1990)(1990)
•Ignore correlation between tasks!
P = 50J = 0
P5 = 50J5 = 8
P3 = 50J3 = 8
P8 = 50J8 = 6
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 21
Lehoczky (1990)LehoczkyLehoczky (1990)(1990)
T5
T3
T8
R4
CET = [2,2]Priority=Mid
CET = [2,2]Priority=High
CET = [2,2]Priority=Low
P5 = 50J5 = 8
P3 = 50J3 = 8
P8 = 50J8 = 6
2
2
2T8
Prio
rity
T5
RW8 = 6
t
t
critical instant
T3t8
6
8
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T5
T3
T8
R4
CET = [2,2]Priority=Mid
CET = [2,2]Priority=High
CET = [2,2]Priority=Low
R3
T7
CET = [2,2]Priority=High
R5
T4
T9
T6
T2
R2
CET = [2,2]Priority=Low
CET = [0,2]Priority=High
CET = [10,10]Priority=High
CET = [2,2]Priority=Low
R1
T1
CET = [2,8]Priority=High
Tindell (1994)TindellTindell (1994)(1994)
•Periodic arrival of events at system inputs as timing-reference
P = 50J = 0
Source
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Global Offset =
T5
T3
T8
R4
CET = [2,2]Priority=Mid
CET = [2,2]Priority=High
CET = [2,2]Priority=Low
R3
T7
CET = [2,2]Priority=High
R5
T4
T9
Source
P = 50J = 0
T6
T2
R2
CET = [2,2]Priority=Low
CET = [0,2]Priority=High
CET = [10,10]Priority=High
CET = [2,2]Priority=Low
R1
T1
CET = [2,8]Priority=High
Tindell (1994)TindellTindell (1994)(1994)
iΦearliest activation time of Ti relative to the periodical arrival of an external event at the system input
7Φ
1Φ
2Φ3Φ
8Φ 9Φ
6Φ 5Φ
4Φ
14 Φ5 =
2 Φ3 =
4 Φ8 =
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14 Φ5 =
2 Φ3 =
4 Φ8 =
Tindell (1994)TindellTindell (1994)(1994)
T8
Prio
rity
T5t
t
T3t
T5
T3
T8
R4
CET = [2,2]Priority=Mid
CET = [2,2]Priority=High
CET = [2,2]Priority=Low
P5 = 50J5 = 8
P3 = 50J3 = 8
P8 = 50J8 = 6
5Φ
8Φ
external event arrival
3Φ
critical instant
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 25
14 Φ5 =
2 Φ3 =
4 Φ8 =
Tindell (1994)TindellTindell (1994)(1994)
T8
Prio
rity
T5t
t
T3t
T5
T3
T8
R4
CET = [2,2]Priority=Mid
CET = [2,2]Priority=High
CET = [2,2]Priority=Low
P5 = 50J5 = 8
P3 = 50J3 = 8
P8 = 50J8 = 6
5Φ
8Φ
external event arrival
3Φ
critical instant
2
2
2
RW8 = 4
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 26
FurtherFurther TechniquesTechniques
Relative offsets and relative jitterExtends idea of global offsetsDescribes the earliest activation time of a task relative to a timing-reference refReference is not necessarily a periodic external eventEnables tighter response time calculation
Precedence relationsExplicitly considers precedence relations between tasks (i.e. task i cannot start until task j has finished execution)Orthogonal to offset based techniques
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 27
SetSet--toptop boxbox
set top box: decript video + download file via IP
Encrypted MPEG-2
Decrypted MPEG-2
IP-traffic
hard-disk
RFRF
decryptiondecryptionunitunit
... I P B B I P … ... I P B B I P …
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 28
Intra context dependenciesIntra context dependencies
I I P
t
B
I I P BCencrypt
Cdecrypt
CIP
worst-case response time
t
I I I
Cencrypt
Cdecrypt
CIP
I
I I I I
worst-case response time
classical analysisclassical analysis::
each frame is assumed each frame is assumed to be an Ito be an I--FrameFrame
intra context analysisintra context analysis::
information about frame information about frame types allows accurate types allows accurate load calculationload calculation
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That‘sThat‘s all !all !HandsHands--onon SessionSession
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SymTASymTA/S Tool/S Tool
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 2
verified system
idea, specification,sketch, existing system
idea, specification,sketch, existing system
• SPP/DMA/RMA• EDF• TDMA• RRIndustry related:• OSEK flavours• CAN
3rd party
open
inte
rfac
es
analysis libraries for ECUs, buses, etc ...
ExplorationExplorationExploration
flow integration:• data bases• tools
AnalysisAnalysisEngineEngine
SensitivitySensitivityAnalysisAnalysis
SymTA/S Tool SuiteSymTA/S Tool Suite
utiliz
es
utiliz
es
utilizesutilizes
iterativeiterative
optimization
optimization
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SensitivitySensitivity AnalysisAnalysis
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 4
ChallengesChallenges
HeterogeneousHundreds of functions50+ ECUsSeveral RTOSesand protocolsStrongly networkedMany suppliers
Complex performance requirementsEnd-to-end deadlinesHidden timing dependencies
ACCABS
ESP ASR
enginecontrol powertrain
control
gateway
ECU1
diagnosis
CAN1 CAN2
FlexRay
ECU2
ECU3
ECU4
ECU5
ECU6
ECU8ECU7
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 5
MotivationMotivation
Modifications of design properties
During the design processRefinement of early design data estimationsRefinement and changes of specificationExchange of platform components: replace CPU or memory type
In the product lifecycleProduct updates (HW, firmware and SW)Integration of new components or subsystemsChange in the environment: applications (smart phone), technicalsystem (motor speed)
In the fieldDynamic systemsUnplanned environment situations (resilience)
Such changes introduce uncertainties and increase design risk
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 6
Domino effects due to parameter changesDomino effects due to parameter changes
gateway
ECU1
diagnosis
CAN1 CAN2
FlexRay
ECU2
ECU3
ECU4
ECU5
ECU6
ECU8
ECU7
T1
T2T1
T1
T1 T2
T2T1
T3 T4
T1 T2
T2
T1 T3
T1
T2overload loss
loss
loss
loss
loss
overload
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 7
MultiMulti--dimensional sensitivity analysisdimensional sensitivity analysis
gateway
ECU1
diagnosis
CAN1 CAN2
FlexRay
ECU2
ECU3
ECU4
ECU5
ECU6
ECU8
ECU7
T1
T2T1
T1
T1 T2
T2T1
T3 T4
T1 T2
T2
T1
T3
T1
T2
T3
T4
Integration of new applications
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 8
Example: WCET variationExample: WCET variation
11,5
12
12,5
13
13,5
14
14,5
15
0,8 0,9 1 1,1 1,2 1,3 1,4 1,5 1,6 1,7 1,8
Late
ncy
(T2-
>T1)
WCET (T1) WCET(T2)
end-to-end deadline
ECU6
gateway
ECU1
diagnosis
BUS1 BUS2
BUS3
ECU2
ECU3
ECU4
ECU5
ECU8
ECU7
T1
T2T1
T1
T1 T2
T2T1
T3 T4
T1 T2
T2
T1 T3
T1
T2
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 9
Sensitivity analysisSensitivity analysis
Sensitivity analysis identifies limits of feasible design
How far can system properties be changed before the system fails slack ?
What is the impact of property changes on the performance metrics?
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 10
SensitivitySensitivity analysisanalysis keykey featuresfeatures
Evaluates design risk linked with a specific component
helps to controls parameter changes
captures „domino“- effects
metric for design robustness
Assistance for system dimensioning/configuration
choose optimal bus bandwidth, CPU clock speed
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 11
Design properties consideredDesign properties consideredAll design data can be subject to changes complex issue
Here we assume Fixed architectureFixed mapping of functions to components
Modification of performance related SW and HW component properties
Platform component performance (processor and communication links)Execution times of individual processesProcess communication volumes
Considered performance metricsPredictable design worst case dataResponse timesEnd-to-end latencies
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 12
SensitivityAnalysis
Controller
SchedulingAnalysisEngine
p5 p6 p7 p8
p1 p2 p3 p4
System Properties
SelectedSensitivity Tuples
p6 p4 p2 p7
p1 p2p3 p4
p7 p8p1
p3
p5 p3 p8
SensitivityAlgorithms
VisualizationAlgorithms
3. Apply configuration
4. Feasible / Infeasible2. Send tuple
S(p6,p4,p2)
S(p7)S(p1,p2)S(p1)
S(p3)
1. Us
erse
lectio
n
Result database7.
Stor
e
8. Re
use
Sensitivity Analysis Loop
5. An
alysis
Resu
lt
6. Se
nd n
ewco
nfig
urat
ion
…
automatic selection
automatic selection
9. Display
SensitivitySensitivity analysisanalysis frameworkframework in in SymTASymTA/S/S
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 13
SensitivitySensitivity Analysis FrameworkAnalysis Framework
Based on SymTA/S analysis engine
Formally derived search space boundariesbased on load conditionsfinds discontinuity points (scheduling anomalies)
Binary search techniqueoptimal minimum number of search stepsbidirectional search space
feasible infeasibleInfeasbile feasible
transparent with respect to scheduling algorithmsapplicable only on monotonic search spaces
if non-monotonic behavior, then split search space in monotonic sub-spaces
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 14
Sensitivity Analysis Sensitivity Analysis -- AlgorithmsAlgorithms
One dimensional analysisFormally derived search space boundaries Binary search like search
Two dimensional analysisDivide-and-conquer like search algorithmParameter specific heuristics for search space reduction
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SymTA/SDesign space exploration
andSystem Robustness Optimization
SymTASymTA/S/SDesign Design spacespace explorationexploration
andandSystem System RobustnessRobustness OptimizationOptimization
Razvan Racu
Arne Hamann
ARTIST2 PhD Course, June 12, DTU Copenhagen, Denmark
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Design Design SpaceSpace Exploration FrameworkExploration Framework
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 3
OutlineOutline
SymTA/S design space exploration framework
Problem independent selector algorithms
Example application: Timing optimization in SymTA/S
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 4
Design Space Exploration FrameworkDesign Space Exploration Framework
Compositional search space encoding scheme
Dynamic search space modificationuser-controlled explorationautomatic search space adaptation
High flexibility and extensibility
Pareto-optimization of arbitrary optimization objectivesEvolutionary search techniques, PISA, ETH Zurich
Exploration speed-up through meta-heuristicsproblem independentproblem dependent
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 5
Exploration Exploration looploop
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 6
SelectorSelector exampleexample: FEMO: FEMO
Fair Evolutionary Multi-objective Optimizer (FEMO)
Developed by Zitzler and Thiele (~2002), ETH Zürich
Idea: Offspring based selectionCount for each individual the number of his offspringsSelect individuals with equal rate for procreation Fairness
Remove all dominated (i.e. not Pareto-optimal individuals) after each generation variable population size
All individuals in population are Pareto-optimal, none is “better” than anotherPossible problem: search space coverage
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 7
FEMO FEMO AlgorithmAlgorithm
Add random initial individuals to the population
Repeat until stop condition:Select individual i with the least offspringsCreate offspring i’ through crossover and mutationRemove all individuals from population that are Pareto-dominated by i’ Add i’ to the population if it is not Pareto-dominated by any other individual
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 8
FEMO: Evolutionary Search StrategyFEMO: Evolutionary Search Strategy
f1(x)
f2(x)
Elimination of dominated Individuals
VariationPareto-FrontLoss of Diversity!
Diversity vs. Convergence speed
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 9
Exploration controlExploration control
TDMA
C1
C2
SPP
T3
C3
T4
SPP
T2
T1 ES ES
ESES
ES ES
ES
ES
ES
CPU 1 BUS CPU 2
T5 ES
Src1
Src2
Chromosome 2Encoding
Variation op.
Chromosome 1Encoding
Variation op.
Chromosome 3Encoding
Variation op.
Chromosome 4Encoding
Variation op.
Search Space
C1
C5
C2 C3
C6
C7
C4
C8 C9
C1
C5
C2 C3
C6
C7
C4
C8 C9
C1
C5
C2 C3
C6
C7
C4
C8 C9
Step 1 Step 2 Step 3
search spacemodification
search spacemodification
•• Compositional encodingCompositional encoding
•• Search space adaptationSearch space adaptation
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 10
Application domainsApplication domains
System optimizationtiming (jitter, end-to-end deadlines)buffer sizespower dissipationmapping
Robustness optimization
Multi-dimensional sensitivity analysis
System generation
…
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 11
Example application: Timing optimization 1/2Example application: Timing optimization 1/2Search space
scheduling parameter for various policies: SPP, TDMA ,RR, EDFoptimization of parameters for real world RTOSes and bus protocols: ERCOSEK, CANoptimization through traffic shapingmapping optimization…
Optimization Objectivesend-to-end latencies, worst-case response timesbuffer sizespower consumptionsystem cost# parameter changes…
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 12
Pareto-front: end-to-enddeadline vs. # parameterchanges
Influence of Traffic Shapingon System Performance
Example application: Timing optimization 2/2Example application: Timing optimization 2/2
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RobustnessRobustness OptimizationOptimization
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 14
OutlineOutline
System property variations
Sensitivity Analysis
Stochastic Multi-dimensional Sensitivity Analysis
Robustness MetricsHypervolume calculationMinimum Guaranteed Robustness (MGR)Maximum Possible Robustness (MPR)
Experiments
![Page 100: SymTASymTA/S SymTA/S SymbolicSymbolic Timing · PDF fileSymbolicSymbolic Timing Analysis for Systems Razvan Racu Arne Hamann ... ESP ASR engine control powertrain ... 3 periodic tasks](https://reader031.vdocuments.us/reader031/viewer/2022030408/5a8a0c8f7f8b9a7f398b88c7/html5/thumbnails/100.jpg)
ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 15
System Property Variations (1)System Property Variations (1)
Two types of system property variationsVariations influencing the system load
Software execution path lengthCommunication volumesInput data rates
Variations influencing the system service capacityProcessor clock-rateCommunication link performance
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ARTIST2 PhD Course, June 12, DTU Copenhagen, DenmarkIDA, TU Braunschweig 16
System Property Variations (2)System Property Variations (2)
Why do system property variations occur?Specification changes, late feature requests, product variants, software updates, bug-fixes
Robustness to property variationsdecreases design risk, andincreases system maintainability and extensibility
Property variations can have severe unintuitive effects on system performance
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Example: WCET VariationExample: WCET Variation
End-to-End latency S3 T4 as a function of execution demand of T2
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System Property Variations (3)System Property Variations (3)
Property variations invalidate the assumption under which the system was dimensioned and configured
Correct function and performance of the system is put at risk
How can we increase the robustness of the system to property variations ?
Adaptivity: feedback-based scheduling, self-organizing systems,…Sensitivity analysis: achieve robustness without on-line parameter adaptation
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Problem FormulationProblem Formulation
Find parameter configuration that …
… maximizes the robustness of the given system w.r.t. changes of several properties
Robustness = the system can sustain a certain degree of property variations without severe performance degradation
Multi-dimensional optimization problem
Not included: dynamic parameter adaptations as a reaction to property variations
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Sensitivity Analysis (1)Sensitivity Analysis (1)
Calculates maximum/minimum admissible values for given system properties
Supported system propertiesWCETs / BCETsCommunication volumeCPU clock rateBus throughput, …
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Sensitivity Analysis (2)Sensitivity Analysis (2)
One-dimensional casemaximum/minimum feasible property value
Multi-dimensional casefront separating feasible and non-feasible system property combinations: sensitivity front
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Sensitivity Analysis (3)Sensitivity Analysis (3)
Recent results:One-dimensional sensitivity analysis
Calculates slack for a single system propertyVestal: Trans. on Software Engineering 1994Racu: RTAS 2005
Multi-dimensional sensitivity analysisConsiders interdependencies between multiple system propertiesRacu / Hamann: ECRTS 2006
Problem: computational effort grows exponentially with problem dimension
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Stochastic Sensitivity Analysis (1)Stochastic Sensitivity Analysis (1)
Solution: scalable stochastic analysis to bound system sensitivity
Sensitivity analysis formulated as multi-objective optimization problem
Search space: System properties including WCETs, Periods, Jitters, …
Optimization objectives: maximization / minimization of considered system properties
Pareto-optimization
Pareto-front of optimization task corresponds to sought-after sensitivity front
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Stochastic Sensitivity Analysis (2)Stochastic Sensitivity Analysis (2)
Uses multi-criteria evolutionary algorithms to approximate sensitivity front
responsible for sensitivity front coverageCurrently used SPEA2 (ETH Zurich): diversified sensitivity front approximation through Pareto-dominance based selection and density approximation
Can be used for system properties subject to maximization (e.g. WCETs) and minimization (e.g. Periods)
In the following: properties are subject to maximization
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Creation of the Initial PopulationCreation of the Initial Population
Creates a certain number of points representing a first approximation of sensitivity front
Uses 1-dim sensitivity analysisto bound the search space in each dimension (bounding hypercube)to generate points representing the extrema of the sought-after sensitivity front
Randomly place the rest of the initial points in bounding hypercube
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Initial Population Initial Population -- ExampleExample
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Bounding the Search Space (1)Bounding the Search Space (1)
Extension for stochastic sensitivity analysis for robustness optimization
Idea: bound search space containing the sought-after sensitivity front
Bounding working Pareto-front F n
evaluated Pareto-optimal working points
Bounding non-working Pareto-front F nw
evaluated Pareto-optimal non-working points
Bounding Pareto-fronts can be used to derive multi-dim. robustness metrics (later)
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Bounding the Search Space (2)Bounding the Search Space (2)
Space between bounding Pareto-fronts is called interesting region
Variation operators use algorithm ensuring that generated offsprings (points) are contained in interesting region
Below bounding non-working Pareto-frontAbove bounding working Pareto-front
Efficiently focuses exploration effort
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Bounding the Search Space (3)Bounding the Search Space (3)
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Random Crossover (1)Random Crossover (1)
Takes as input two parent points to create two offspring points
The two parent points define hypercube in which the created offspring points are randomly placed
Simple standard operator that locally refines the approximation of the sought-after sensitivity front
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Random Crossover (2)Random Crossover (2)
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Random Crossover (3)Random Crossover (3)
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Front Convergence Mutate (1)Front Convergence Mutate (1)
Takes as input one parent point to produce one offspring point
Heuristic operator adapted to optimization problem
Increases convergence speed
Directly supports the convergence of the bounding Pareto-fronts
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Front Convergence Mutate (2)Front Convergence Mutate (2)
Strategy:Determine X closest points on opposite Pareto-frontChoose randomly one of these pointsPlace offspring point randomly on straight line connecting the parent point and the chosen random point
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Front Convergence Mutate (3)Front Convergence Mutate (3)
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Front Convergence Mutate (4)Front Convergence Mutate (4)
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Hypervolume CalculationHypervolume Calculation
Hypervolume as basis of the proposed robustness metrics
Hypervolume is defined in a given hypercube and associated to a point set
Two different notions of hypervolumeinner hypervolume : Volume of space Pareto-dominated by the given points inside the given hypercubeouter hypervolume : Volume of space Pareto-dominated by all points not Pareto-dominating any of the given points
−λ
+λ
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Hypervolume Calculation (2)Hypervolume Calculation (2)
2D-caseinner hypervolume: lower step functionouter hypervolume: upper step function
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( )= 66λ-( )= 100λ+
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Robustness MetricsRobustness Metrics
Given a set of properties we want to achieve robustness for …
… use stochastic sensitivity analysis to derive upper and lower robustness bounds
Minimum Guaranteed Robustness (MGR)Defined as inner hypervolume of the bounding working Pareto-front F w
Maximum Possible Robustness (MPR)Defined as outer hypervolume of the bounding non-working Pareto-front F nw
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Robustness Metrics (2)Robustness Metrics (2)
Obviously: MGR <= Real Robustness <= MPR
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Robustness ExplorationRobustness Exploration
Idea: Pareto-optimize MGR and MPR
AdvantagesStochastic sensitivity analysis is scalableLittle computational effort necessary to reasonably bound robustness potential of given configurationIn-depth analysis can be performed once interesting configurations are identified (i.e. high MGR or high MPR) Perfectly suited for robustness optimization
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Example SystemExample System
Distributed embedded system
4 computational resource …
… connected via CAN bus
3 applications
Sens ActSin Sout
Cam Vout
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Approximation Approximation QualityQuality (1)(1)Approximation after 100 evaluations (20 sec)
MGR = 2447
MPR = 2937
Approximation after 200 evaluations (40 sec)
MGR = 2580
MPR = 2813
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Approximation Approximation QualityQuality (2)(2)Approximation after 300 evaluations (60 sec)
MGR = 2632
MPR = 2777
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3D 3D -- RobustnessRobustness MaximizationMaximization (1)(1)
Original configuration
Optimized configuration
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3D 3D -- RobustnessRobustness MaximizationMaximization (2)(2)
Original configuration
Optimized configuration
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ConclusionConclusion
Robustness to system property variations
Scalable stochastic sensitivity analysis perfectly suited for robustness optimization
Metrics expressing lower and upper system robustness bounds …
… enable efficient integration of robustness criteria into design space exploration