Software Technology for Self-Adaptive Systems

Transcription

1 Software Technology for Self-Adaptive Systems Danny Weyns, Mauro Caporuscio, Morgan Ericsson, Jesper Andersson, S. Pllana, Welf Löwe Department of Computer Science

2 Overview Self-adaptation in a nutshell (researcher s perspective) Three industrial cases Model-driven engineering applied to smart grids* Self-managing Internet-of-Things** Automatic maintenance of online telecom documentations* Conclusions * Part of KK Synergy project: Software Technology for Self-Adaptive Systems ** R&D project with VersaSense

3 Self-adaptation in a nutshell A researcher s perspective

4 Motivation Modern software systems often subject to various types of uncertainties Changing conditions in the environment (resources, services ) User behavior that is difficult to predict Goals may dynamically be changed Uncertainties affect qualities (reliability, efficiency ) May be difficult to anticipate at development Business continuity (24/7) requires handling uncertainties at runtime

5 What is Self-Adaptation? Key idea: let system gather new knowledge about uncertainties at runtime, reason about itself and its context and adapt to realize goals

6 What is Self-Adaptation? Software system input effect Environment Non-controllable software, hardware, network, physical context

7 What is Self-Adaptation? Self-adaptive software system input effect Environment Non-controllable software, hardware, network, physical context

8 What is Self-Adaptation? Self-adaptive software system Managing system monitor adapt monitor Managed system Controllable software input effect Environment Non-controllable software, hardware, network, physical context

9 Architecture-based self-adaptation Self-adaptive software system monitor monitor M A K P Managed system Controllable software E Managing system adapt MAPE-K - M: Monitor - A: Analyze - P: Plan - E: Execute - K: Knowledge input effect Environment Non-controllable software, hardware, network, physical context

10 Self-Adaptation: From Research to Practice Self-adaptation has successfully been applied in industry, for example for Dynamic management of client-server systems Elastic Cloud Yet there is a large unexplored potential In this talk we discuss three R&D efforts where we apply self-adaptation to solve problems in practice

11 Overview Self-adaptation in a nutshell (researcher s perspective) Three industrial cases Model-driven engineering applied to smart grids Self-managing Internet-of-Things Automatic maintenance of online telecom documentations Conclusions

12 MoDES: Model-driven Engineering applied to Smart Power Grids

13 Purpose and Organization Purpose To Guarantee the provision of stable and sustainable energy in an ever-changing environment Project Organization Technology Provider - Hughes Power Systems: access to the Power Grid system - IBM Sweden AB: access to IBM tools (Smarter Planet) Customer - Vattenfall Services Nordic AB Researchers - Linnaeus University

14 Handling Uncertainties?? Ever-changing environment -> Uncertainty External dynamics - e.g., interruptions in delivery - e.g., changes in the environmental conditions (solar/wind) Changes in consumption - e.g., user needs/habits Current practice Analysis: based on historical data - e.g., Seasonal and hourly generation/consumption, weather forecast, Planning: Multi-objective optimization problem solved off-line - e.g., min power loss, min voltage deviation, max active power from renewables, min number of switching Execution: Power distribution is configured manually Reliability? Efficiency?

15 Self-Adaptive Solution Monitor the actual data e.g., transformers, Distribution lines, generators/loads Analyze current data and detect anomalies/predict power flow trend e.g., energy loss, voltage drop Plan online reconfiguration actions e.g., switching distribution path, adjust the power generation/consumption Execute automatically the SPG reconfiguration K System Operator models@runtime

16 TAPPAS: CYber-PHysical development Framework Cosimulation Engine

17 Overview Self-adaptation in a nutshell (researcher s perspective) Three industrial cases Model-driven engineering applied to smart grids Self-managing Internet-of-Things Automatic maintenance of online telecom documentations Conclusions

18 Self-Managing Internet-of-Things

19 Purpose and Organization Purpose To automate management of IoT systems and guarantee quality of service under uncertain operating conditions Project Organization Technology Provider - VersaSense, provider of wireless IoT solutions Customers - Various companies (Chemical domain, smart farming, ) Researchers - KU Leuven Belgium and Linnaeus University

20 Handling Uncertainties?? Uncertainty Operating conditions: wireless communication subject to noice and interference, dynamic traffic, etc. Energy consumption critical: motes typically battery powered requiring careful use of communication Current practice Over-provision - e.g., Power settings maximal, duplication of messages Manual tuning based on observations Not efficient! Costly! Error prone!

21 Self-Adaptive Solution Self-Managing Internet of Things Managing System Analyzer Statistical Model Checker Planner Monitor Knowledge Executor Probes Network Engine Effectors Network Data Quality 7 model to predict average energy consumption for a possible configuration 6 D. Weyns, Gowri Sankar R Client Analytics RFID sensor Passive Infrared sensor Heat sensor Gateway [id] Mote ID [4] Wireless link [6] [2] Mote Mote [10] [1] Mote Managed System (IoT System) Mote [5] Gateway [11] [7] [13] [8] [9] [12] [14] [3] f-adaptation approach for the area security surveillance application [15] yzer uses a statistical model checker to predict the quality propfig. 2. Configuration area security surveillance application h possible configuration of the IoT application. A configuration is

22 Results Energy Consumption (Coulomb) Configuration Candidate configuration Selected configuration with latency Selected configuration without latency Energy Consumption (Coulomb) Energy Consumption Packet Loss (%) Packet Loss Latency (% of Cycle Time) Latency Packet Loss (%) ion option with three adaptation goals. Packet loss < 10% Latency < 5% cycle time; Minimum energy consumption Traditional Approach Self-Adaptation Approach Traditional Approach Self-Adaptation Approach Traditional Approach Self-Adaptation Approach Self-adaptation significantly improves on energy consumption and latency Tradeoff is a small increase of packet loss compared to over-provisioning 1 DSO = Distribution System Operator

23 Overview Self-adaptation in a nutshell (researcher s perspective) Three industrial cases Model-driven engineering applied to smart grids Self-managing Internet-of-Things Automatic maintenance of online telecom documentations Conclusions

24 Automatic Maintenance of Online Telecom Documentations

25 Purpose and Organization Purpose To automate the usability and maintenance of online documentation of telecommunication systems Project Organization Companies - Ericsson: provides software, services and infrastructure for communication - Sigma Technology: provides product information, software and embedded solutions - Softwerk: offers services and tools for quality assurance of software and technical documentation Researchers - Linnaeus University

26 Handling Uncertainties?? Uncertainty: documents linked by cross-references User behavior: users have changing requirements over time about the documentation they need and use Updating and refactoring documentation: documents can be rewritten and refactored at any time Current practice Lack of link semantics: difficult to use static tools to find incorrect links other than dead links Manual inspection by proofreaders Feedback from end users Expensive! Poor quality!

27 Self-Adaptive Solution - optimize document graph - remove dead documents Document Annotations Analytics Database update, use read, write track views, searches etc. Document Adapation track structure change docs, links Documentation Webstore User%1 User%2 search, read docs show docs User%n

28 Results Adaptation step of documentation graph after set of user sessions

29 Results Over 2500 user sessions in period of 2 months Adaptation allows Removing 330 documents (46%) that were not used Reducing the number of links to 1477 (49%) Refining 547 links Self-adaptation significantly enhanced usability (searching through documentation) maintenance (complexity of documentation graph) Evaluation ongoing when we speak

30 Overview Self-adaptation in a nutshell (researcher s perspective) Three industrial cases Model-driven engineering applied to smart grids Self-managing Internet-of-Things Automatic maintenance of online telecom documentations Conclusions

31 Conclusions Self-adaptation offers a viable solution to provide the required quality goals for 24/7 systems that are subject to uncertain operation conditions We demonstrated the use of self-adaptation for three problems in practice A challenging problem is the evaluation of such systems (real conditions versus rolling out in the field) We thank the KK foundation and our industry partners!

32 Bibliography R. de Lemos, H. Giese, H. Muller, M. Shaw, J.Andersson, M. Litoiu, B. Schmerl, G. Tamura, N. Villegas, T. Vogel, D. Weyns, et al., Software Engineering for Self-Adaptive Systems: A Second Research Roadmap, Software Engineering for Self-Adaptive Systems II Lecture Notes in Computer Science vo. 7475, Springer 2013 D. Weyns, Software Engineering of Self-Adaptive Systems: An Organised Tour and Future Challenges, Chapter for Handbook of Software Engineering, Editors: Richard Taylor, Kyo Chul Kang and Sungdeok Cha, Springer 2018 M. D'Angelo, M. Caporuscio and A. Napolitano, Model-Driven Engineering of Decentralized Control in Cyber-Physical Systems, 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems 2017 U. Iftikhar, Gowri Sankar Ramachandran, P. Bollansee, D. Weyns, D. Hughes, DeltaIoT: A Self-Adaptive Internet of Things Exemplar, International Symposium of Software Engineering for Adaptive and Self-Managing Systems 2017