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Lab Meeting

Lab Meeting. Performance Analysis of Distributed Embedded Systems Lothar Thiele and Ernesto Wandeler Presented by Alex Cameron 17 th August, 2012. One cause for end-to-end timing constraints is the fact that embedded

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Lab Meeting

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  1. Lab Meeting Performance Analysis of Distributed Embedded Systems Lothar Thiele and Ernesto Wandeler Presented by Alex Cameron 17th August, 2012

  2. One cause for end-to-end timing constraints is the fact that embedded systems are frequently connected to a physical environment through sensors and actuators. Typically, embedded systems are reactive systems that are in continuous interaction with their environment and they must execute at a pace determined by that environment. A real-time constraint is called hard, if not meeting that constraint could result in a catastrophic failure of the system, and it is called soft otherwise. As a consequence, time-predictability in the strong sense can not be guaranteed using statistical arguments. Failures of embedded systems very often relate to timing anomalies that happen infrequently and therefore, are almost impossible to discover by simulation…. Performance Analysis using Network Calculus presents an elegant methodology for offering performance guarantees in deterministic queuing systems. “It is the purpose of performance analysis to determine the timing and memory properties of such systems.” !!

  3. Consider an Embedded Real-Time System Comprising Two Applications Threat Detection Launch Actuator Sensor P3 P4 Data A1 A2 DSP CPU P5,P6 P1, P2 Interference between bus and Apps means competing BCET and WCET t WCET BCET

  4. Consider the System when Network Enabled Threat Detection Launch Sensor Actuator Data A1 A2 DSP CPU Service 1 Service 2 t WCET BCET

  5. Example Arrival Patterns Basic arrival functions for a set of arrival patterns that can be derived using Patterns (e.g. sensor), Trace (measure) or Specifications (Data Sheets)

  6. Effect of Deadline Variance for given Event Arrival Rate Figure 12: Graph shows the normalised rate of missed deadlines for the LLF scheduling algorithm plotted against the ratio of the deadline to the Poisson arrival mean for a range of variances (jitter) in the deadline. The graphs have been smoothed but were based on a sample of 200 arrivals for each measured point on the curve. These results are for a complex workflow comprising five services running on a single CPU.

  7. One Approach is to manage the Event Arrival pattern Bound to the implementation EMIF = Event Model Interfaces perform type conversions between arrival patterns. EAF = Event Adaption Functions: Making the systems analysable, e.g. adding buffers etc. when EMIF is not present

  8. The Concept The Network Calculus traffic characterisation model The guarantee – either regulator or leaky bucket

  9. Performance Network Approach Resource Modelling Service Functions Abstractions Abstractions Arrival Curves Resource Modelling: In comparison to functional validation, we need to model the resource capabilities and how they are changed by the workload of tasks or communication. Therefore, contrary to the approaches described before, we will model the resources explicitly as ‘first class citizens'.

  10. Primary Difficulty - Modelling the Workload WCET and BCET: The simplest possibility is to assume that each event of an event stream triggers the same task and that this task has a given worst case and best case execution time determined by other methods. Application Modelling: Take into account the characteristics of the application, e.g. (a) distinguishing between different event types each one triggering a different task and modelling various WCET (or BCET). This way, one can model correlations in event streams. Each incoming event, a subtask generates the associated workload and the program branches to one of its successors. Trace: As in the case of arrival curves, we can use a given trace and re- port the workloads associated to each event, e,g, by simulation. Based on this information, we can easily compute the upper and lower envelope.

  11. The Outcome Figure 12: Representation of the delay and accumulated buffer space computation in a performance network.

  12. discussions

  13. BACKUP SLIDES

  14. Applicability VMSA Threat Detection Launch Sensor Actuator Response Random Events Decision Variables Performance Measures Distributed Real-Time Event Driven Service Oriented Architectural Implementation Discrete Event Simulator Random Variate Generator Architecture to Petri Net Mapping Deterministic Measures Event Synchronised Petri Nets (CSPN) Model Predictive Measures

  15. And What about the Event Arrival Patterns? periodic T Periodic with Jitter J T Admissible occurrence of event

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