1. Performance Estimation of Distributed Real-time Embedded Systems by Discrete Event Simulations
Gabor Madl1, Nikil Dutt1, Sherif Abdelwahed2
1 University of California, Irvine
2 Mississippi State University
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EMSOFT 2007
November 9, 2018

2. Outline November 9, 2018 Motivation Modeling DRE systems
Outline Motivation Modeling Performance Estimation Real-time Verification Evaluation
Outline
Motivation
Distributed Real-time Embedded (DRE) systems
Need to combine various analysis methods for performance estimation
Modeling DRE systems
Formal performance estimation
Branching intervals, race conditions
Event order tree
Real-time Verification
On-the-fly construction of the event order tree
Evaluation
Comparison with random simulations
Comparison with timed automata model checking
November 9, 2018

3. Outline Motivation Modeling Performance Estimation Real-time Verification Evaluation
DRE Systems
Distributed real-time embedded (DRE) systems are often reactive and event-driven
Better latency than in synchronous/time-triggered systems
Easier to implement, no need for global synchronization
Computations are driven by events – complex model
Asynchrony, concurrency, race conditions
Hard to predict all behaviors
Performance estimation is a key challenge
Task execution times
Communication delays
Degree of parallelism
Throughput
November 9, 2018

4. Static Analysis Methods
Outline Motivation Modeling Performance Estimation Real-time Verification Evaluation
Static Analysis Methods
Classical scheduling theory [Liu, Layland 1973]
Symta/S [Henia et al. 2005]
Application of heterogeneous scheduling methods for component-based systems
Modular Performance Analysis [Wandeler et al. 2006]
Application of real-time calculus for performance estimation
Synchronous languages [Benveniste et al. 2003]
Alternative approach for defining real-time constraints in globally synchronous systems with deterministic concurrency
Giotto [Henzinger et al. 2003]
Time-triggered language for schedulability analysis using the concept of logical execution time
November 9, 2018

5. Outline Motivation Modeling Performance Estimation Real-time Verification Evaluation
Simulations
Several variations used by the industry (e.g. RTL languages, SystemC, etc.)
Various abstraction levels trade off performance and accuracy
Ptolemy II [Lee et al. 2001]
Complex framework for the heterogeneous modeling of embedded systems
Focuses on non-deterministic systems
Simulation-based performance estimation method for MPSoCs [Lahiri et al. 2001]
Execution traces represented as symbolic graphs
Analysis is based on simulations of the symbolic model
November 9, 2018

6. Model Checking November 9, 2018
Outline Motivation Modeling Performance Estimation Real-time Verification Evaluation
Model Checking
Task timed automata [Ericsson et al. 1999]
Approach for deciding non-preemptive schedulability of asynchronous event-driven task graphs
Task-level analysis of DRE systems [Madl et al. 2004, 2005, 2006]
Extends task timed automata with event channels, schedulers, to model distributed real-time embedded systems
Thread-level analysis of real-time middleware using timed automata [Subramonian et al. 2006]
Detailed analysis of middleware services
Low-level abstraction
November 9, 2018

7. Evaluation of Existing Methods
Outline Motivation Modeling Performance Estimation Real-time Verification Evaluation
Evaluation of Existing Methods
Static analysis methods
Often assume independent, periodic tasks
Cannot capture dynamic effects such as race conditions
Simulations
Can show the presence of an error, never its absence
Ad-hoc, hard to measure coverage
Limited design space exploration
Model checking
State space explosion problem
No partial results
Time consuming and costly
Each method has its advantage and disadvantage
November 9, 2018

8. Need to Combine Methods
Outline Motivation Modeling Performance Estimation Real-time Verification Evaluation
Need to Combine Methods
Providing formal guarantees on real-time properties is often infeasible in distributed asynchronous systems
Combine simulations and model checking to increase the coverage of simulations
Need to capture dynamic effects for accurate performance estimation
Race conditions, non-deterministic execution times, arbitration etc.
Need to capture execution intervals in continuous time as non-wcet deadline misses are possible in distributed asynchronous systems
November 9, 2018

9. Model-based Design and Analysis
Outline Motivation Modeling Performance Estimation Real-time Verification Evaluation
Model-based Design and Analysis
The domain-specific model (DSM) captures key properties of the design
The DSM is mapped to a formal executable model, that drives the real-time verification and performance estimation
The formal executable model can be mapped to heterogeneous models of computation (MoC)
The same formal model is used for both performance estimation and real-time verification
Parameters obtained by simulations
November 9, 2018

10. Model for Real-time Systems
Outline Motivation Modeling Performance Estimation Real-time Verification Evaluation
Model for Real-time Systems
DRE = {T, M, C, TR, D}
T is the set of tasks
M is the set of machines
C is the set of channels C  T
TR is the set of timers TR  T
D is the task dependency relationship D  T  T
machine(tk): T  M
Tasks’ execution interval given by [bcet, wcet]
Channels are special tasks that model FIFO buffers and delays
Deadlines defined for tasks
Timers trigger execution of tasks periodically
November 9, 2018

11. Events November 9, 2018 DRE MoC is a continuous-time extension of DES
Outline Motivation Modeling Performance Estimation Real-time Verification Evaluation
Events
DRE MoC is a continuous-time extension of DES
Can be mapped to timed automata
Event labels are time-stamped values from the domain of non-negative real numbers
Global time
During simulation, “jump” to event with the smallest timestamp
The tasks’ execution time is defined as a constraint between the timestamps of its input and output events
Tasks, channels, timers compose using events
Event flow follows dependencies
November 9, 2018

12. Task States, Schedulers
Outline Motivation Modeling Performance Estimation Real-time Verification Evaluation
Task States, Schedulers
Abstract model for scheduling
Fixed priority scheduler composes with tasks using events
Scheduler triggers transition from wait to run state
Channels are not triggered by scheduler
Scheduling policy is defined as priorities between transitions
Non-preemptive scheduling
November 9, 2018

13. Performance Estimation Problem
Outline Motivation Modeling Performance Estimation Real-time Verification Evaluation
Performance Estimation Problem
A run or execution trace of the DRE model is the chronological sequence of events occurred in the model
In the DRE MoC two execution traces are equivalent, if they contain the same events, and the chronological order of the events is the same
We define the end-to-end computation time between two events as the maximum possible difference between those events along all the possible runs of the model
DRE model implies a partial ordering on events
Enumerate equivalent execution traces to obtain end-to-end computation time
November 9, 2018

14. Branching Intervals November 9, 2018
Outline Motivation Modeling Performance Estimation Real-time Verification Evaluation
Branching Intervals
In the examples below, total orderings are as follows:
iA,oA,iB,oB,iC,oC and iA,iC,oC,oA,iB,oB and iA,oA,iB,iC,oB,oC
Scheduler events omitted to keep the example simple
More total orderings are possible
Total ordering of events implies time constraints on the execution intervals of tasks
In the example shown in the left, A’s execution time has to be less than 3, otherwise oB will not happen before iC
Anytime the order may change, the event order tree has to branch to capture these cases
Branching Interval
November 9, 2018

15. Race Conditions November 9, 2018 Race Condition
Outline Motivation Modeling Performance Estimation Real-time Verification Evaluation
Race Conditions
If two tasks receive input events at the same time from tasks that are assigned to different machine, race conditions may be present
In the example shown on the right, there can be a race condition between task_C and task_D
Task_B and task_E are assigned to different machines
Race conditions can affect the total ordering of events
This problem may result in priority inversion
Race Condition
November 9, 2018

16. Event Order Tree November 9, 2018 Branching Points Race Conditions
Outline Motivation Modeling Performance Estimation Real-time Verification Evaluation
Event Order Tree
Combination of branching intervals and race conditions are interesting corner cases
Extremely hard to find by simulations
Branching Points
Race Conditions
November 9, 2018

17. Real-time Verification by DES
Outline Motivation Modeling Performance Estimation Real-time Verification Evaluation
Real-time Verification by DES
Event order tree provides a way for the real-time verification of a large class of DRE models
Proposed method is not guaranteed to terminate
Restriction: the model has to return to the initial state
Otherwise finite horizon analysis, or use timed automata
If restriction above is satisfied, then the event order tree is repeatable; it repeats itself from all the leaves
There are finite number of nodes in the tree
One simulation only for equivalent execution traces
Timers generate events at discrete time steps
Each task generates finite number of output events for each input event
November 9, 2018

18. On-the-fly Detection of Branching
Outline Motivation Modeling Performance Estimation Real-time Verification Evaluation
On-the-fly Detection of Branching
Both the performance estimation and real-time verification is based on the event order tree
Event order tree can be extremely large; there is a need to obtain it on-the-fly
We utilize a DFS-based exploration of the tree
Based on repetitive simulations
Memory use is not significant
Each task detects its branching intervals during DES
Generate all permutations of branching intervals
November 9, 2018

19. Case Studies Used for Analysis
Outline Motivation Modeling Performance Estimation Real-time Verification Evaluation
Case Studies Used for Analysis
Set of examples based on real-time CORBA avionics applications
Software timers, asynchronous event propagation
Thread pool policy follows the half-sync half-async pattern
We have assumed non-preemptive scheduling for comparisons with other methods
November 9, 2018

20. Compared to Model Checking
Outline Motivation Modeling Performance Estimation Real-time Verification Evaluation
Compared to Model Checking
Model checking tailored to answer yes/no questions
Is the execution time less than x?
Storing timed states is a major contributor to memory use
Checking for previously visited states is inefficient
Realistic models are often too complex
Exhaustive analysis infeasible – state space explosion problem
Compared performance to UPPAAL and Verimag IF tool
Timed automata model checkers faster in general
On larger models they run out of memory
Proposed method is CPU-bound – partial results when exhaustive analysis is infeasible (most of the time)
Well suited for multicore/distributed implementation
November 9, 2018

21. Compared to Random Simulations
Outline Motivation Modeling Performance Estimation Real-time Verification Evaluation
Compared to Random Simulations
Random simulations often revisit the same total orderings of events, hard to find rare events
Symbolic simulation model – faster than naïve simulations
Simulation speed ~30ms for 100 tasks, ~1-5ms for 5-15 tasks
DES-based method increases coverage over time
Focuses on corner cases
Branching intervals
Race conditions
Their combinations
Improves the existing practice of the ad-hoc combination of directed simulations and random simulations for performance estimation
November 9, 2018

22. DREAM Analysis Framework
Outline Motivation Modeling Performance Estimation Real-time Verification Evaluation
DREAM Analysis Framework
ALDERIS model GME tool
Open-source DREAM Tool
Automatic timed automata model generation for the UPPAAL and Verimag IF tools
Simulation-based model checking
Performance Estimation using DES
Random testing
Schedulability optimizations
Verimag IF model checker
ALDERIS model XML representation
UPPAAL model checker
November 9, 2018

23. Work in Progress & Future Work
Outline Motivation Modeling Performance Estimation Real-time Verification Evaluation
Work in Progress & Future Work
DREAM was integrated in the Scenery project by Fujitsu Labs of America
Analyze real-time properties in embedded software and SoCs
Composing with other MoCs
System-level performance estimation
Wireless & multi-media case studies
Looking for domain-specific modeling languages with large-scale DRE examples (hard to find)
Possible optimizations by hierarchical model checking
Distributing model checker algorithm to increase scalability
Not the main focus at the moment
November 9, 2018

24. Questions?
EMSOFT 2007
November 9, 2018