Probabilistic Study of End-To-End Constraints in Real-Time Systems Cristian Maxim
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Probabilistic study of end-to-end constraints in real-time systems Cristian Maxim To cite this version: Cristian Maxim. Probabilistic study of end-to-end constraints in real-time systems. Systems and Control [cs.SY]. Université Pierre et Marie Curie - Paris VI, 2017. English. NNT : 2017PA066479. tel-02003251 HAL Id: tel-02003251 https://tel.archives-ouvertes.fr/tel-02003251 Submitted on 1 Feb 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THESE` DE DOCTORAT DE l'UNIVERSITE´ PIERRE ET MARIE CURIE Sp´ecialit´e Informatique Ecole´ doctorale Informatique, T´el´ecommunications et Electronique´ (Paris) Pr´esent´eepar Cristian MAXIM Pour obtenir le grade de DOCTEUR de l'UNIVERSITE´ PIERRE ET MARIE CURIE Sujet de la th`ese: Etude´ probabiliste des contraintes de bout en bout dans les syst`emestemps r´eel soutenue le 11 dec´embre 2017 devant le jury compos´ede : Mme. Liliana CUCU-GROSJEAN Directeur de th`ese M. Benoit TRIQUET Coordinateur Industriel Mme. Christine ROCHANGE Rapporteur M. Thomas NOLTE Rapporteur Mme. Alix MUNIER Examinateur M. Victor JEGU Examinateur M. George LIMA Examinateur M. Sascha UHRIG Examinateur Contents Table of contents iii 1 Introduction1 1.1 General Introduction...........................1 1.1.1 Embedded systems........................2 1.1.2 Real-time domain........................4 1.1.3 Avionics industry.........................9 1.2 Context.................................. 14 1.2.1 Performance race......................... 15 1.2.2 Execution time.......................... 17 1.2.3 Probabilities and statistics.................... 21 1.3 Thesis motivation............................. 22 1.4 Model................................... 24 1.4.1 Useful notions from the probability theory........... 24 1.4.2 Probabilistic real-time system.................. 26 2 State Of The Art 29 2.1 Time analysis of probabilistic real-time systems............ 31 2.2 Probabilistic methods.......................... 38 2.3 Measurement based probabilistic analysis............... 41 2.4 Randomized architectures........................ 49 2.5 Timing analysis in avionics industry.................. 51 2.5.1 Integrated Modular Avionics (IMA).............. 52 2.5.2 Time analysis of avionics applications............. 54 2.5.3 Mixed-criticality systems.................... 56 i ii CONTENTS 3 Conditions for use of EVT in the real-time domain 59 3.1 System consistency............................ 60 3.1.1 Input............................... 61 3.1.2 Software.............................. 62 3.1.3 Platform.............................. 63 3.2 Identical distributed data........................ 64 3.3 Independence............................... 66 3.4 Reproducibility and representativity of measurement-based approaches 69 3.4.1 The reproducibility of the WCET estimation method..... 71 3.4.2 The reproducibility of the measurement protocol....... 72 3.4.3 Representativity of a measurement protocol.......... 72 3.4.4 Relations between reproducibility, representativity and con- vergence.............................. 74 3.5 Conclusion................................ 76 4 pWCET estimation methodology 77 4.1 Generalized extreme value distribution................. 78 4.2 Generalized Pareto distribution.................... 83 4.3 Validation of statistical results..................... 88 4.4 The pWCET estimation from dependent execution times....... 94 4.5 Small variability data.......................... 96 4.6 Conclusions................................ 97 5 Experimental results 99 5.1 Analysis of benchmarks on multiprocessor architectures....... 99 5.2 Avionics application analysis...................... 105 5.2.1 Application presentation..................... 107 5.2.2 Platform characteristics..................... 114 5.2.3 Timing analysis results...................... 127 5.3 Conclusions................................ 156 6 General conclusions 157 6.1 Contributions............................... 157 6.2 Future work................................ 158 CONTENTS iii A Statistical tests 161 A.1 Run test.................................. 161 A.2 Kolmogorov-Smirnov test........................ 162 A.3 Anderson-Darling Test.......................... 163 List of Figures 169 List of Tables 171 Nomenclature 177 Bibliography 196 Chapter 1 Introduction 1.1 General Introduction Human life in the 21st century is surrounded by technology. From household to transportation, from education to hobbies and from security to sports, informatics plays a major role in daily activities. Social interaction, education and health are only a few examples of domains in which the fast evolution of technology had a major positive impact on the quality of life. Businesses rely more and more on embedded systems to increase their productivity, efficiency and value. In factories, robot precision tends to replace the human versatility. Even though connected devices like drones, smart watches, or smart houses, are becoming more popular in the last years, in the industries that deal with user security, this kind of technology have been used for many years. Avionics industry has been using computers for their products since 1972 with the production of the first A300 airplane and has reached astonishing progress with the development of the first Concorde airplane in 1976, which was considered a miracle of technology, surpassing with many years the airplanes of its time. A considering number of innovations and knowledge acquired for the Concorde are still used in the recent models like A380 or A350. A slower start of technological evolution can be seen in the space or automotive industries, but with the start of OneWeb project [OneWeb, 2015] and the intro- duction of self-driving cars, these domains have encountered an acceleration phase that does not seem to take a break any time soon. In this section we present an overview on the technologies used in the aforementioned industries with an emphasis 1 2 CHAPTER 1. INTRODUCTION on the real-time domain as an important part critical real-time embedded systems (CRTES). 1.1.1 Embedded systems In our times, we are surrounded by technologies meant to improve our lives, to assure its security, or programmed to realize different functions and to respect a series of constraints. We consider them as embedded systems or often as parts of cyber-physical systems. An embedded system is a microprocessor-based system that is built to control a function or a range of functions and is not designed to be programmed by the end user in the same way that a PC is [Heath, 2002]. A user can make choices concern- ing functionality but cannot modify the functionality of the system by adding or replacing software. Embedded systems are managed by single or multiple process- ing cores in the form of micro-controllers or digital signal processors (DSP), field- programmable gate arrays (FPGA), application-specific integrated circuits (ASIC) and gate arrays. These processing components are integrated with components dedicated to handling electric and/or mechanical interfacing. An embedded sys- tem's key feature is the dedication to specific functions that typically require strong general-purpose processors. For example, router and switch systems are embed- ded systems, whereas a general-purpose computer uses a proper OS for routing functionality. However, embedded routers function more efficiently than OS-based computers for routing functionalities. Commercial embedded systems range from digital watches and MP3 players to giant routers and switches. Complexities vary from single processor chips to advanced units with multiple processing chips. Compared to an embedded system, which puts emphasis on the computational elements, a cyber-physical system is an integration of computation with phys- ical processes [Lee and Seshia, 2011]. Majority of cyber-physical systems contains at least one embedded system. The term of cyber-physical system is very broad and encompasses various research topics from software and modeling to systems, networking, and control in computer science and engineering. A detailed concept map of these systems can be seen in Figure 1.1[Cyber, 2010]. This taxonomy is an evolving one and new domains are added in a continuous manner. A real-time system is any information processing system which has to respond to externally generated input stimuli within a finite and specified period [Burns and 1.1. GENERAL INTRODUCTION 3 Figure 1.1: Cyber-physical systems - a Concept Map. Image made by Edward A. Lee after a taxonomy given by S. Shyam Sunder [Lee, Edward Ashford, 2012]. Wellings, 2001]. In this type of systems, the correctness depends not only on the logical result but also on the time it was delivered. A failure to respond is as bad as the wrong response. Real-time systems can be found in industries like aeronautics, aerospace, automotive or railways but also in sensor networks, image processing, multimedia applications, medical technologies, robotics, communications, computer games or household systems. According to their deadline miss importance, real-time systems can be