Achieving autonomic Web service compositions with models at runtime

[EN] Several exceptional situations may arise in the complex, heterogeneous, and changing contexts where Web service operations run. For instance, a Web service operation may have greatly increased its execution time or may have become unavailable. The contribution of this article is to provide a tool-supported framework to guide autonomic adjustments of context-aware service compositions using models at runtime. During execution, when problematic events arise in the context, models are used by an autonomic architecture to guide changes of the service composition. Under the closed-world assumption, the possible context events are fully known at design time. Nevertheless, it is difficult to foresee all the possible situations arising in uncertain contexts where service compositions run. Therefore, the proposed framework also covers the dynamic evolution of service compositions to deal with unexpected events in the open world. An evaluation demonstrates that our framework is efficient during dynamic adjustments.Alférez-Salinas, GH.; Pelechano Ferragud, V. (2017). Achieving autonomic Web service compositions with models at runtime. Computers & Electrical Engineering. 63:332-352. doi:10.1016/j.compeleceng.2017.08.004S3323526

SACRE: Supporting contextual requirements' adaptation in modern self-adaptive systems in the presence of uncertainty at runtime

Runtime uncertainty such as unpredictable resource unavailability, changing environmental conditions and user needs, as well as system intrusions or faults represents one of the main current challenges of self-adaptive systems. Moreover, today's systems are increasingly more complex, distributed, decentralized, etc. and therefore have to reason about and cope with more and more unpredictable events. Approaches to deal with such changing requirements in complex today's systems are still missing. This work presents SACRE (Smart Adaptation through Contextual REquirements), our approach leveraging an adaptation feedback loop to detect self-adaptive systems' contextual requirements affected by uncertainty and to integrate machine learning techniques to determine the best operationalization of context based on sensed data at runtime. SACRE is a step forward of our former approach ACon which focus had been on adapting the context in contextual requirements, as well as their basic implementation. SACRE primarily focuses on architectural decisions, addressing self-adaptive systems' engineering challenges. Furthering the work on ACon, in this paper, we perform an evaluation of the entire approach in different uncertainty scenarios in real-time in the extremely demanding domain of smart vehicles. The real-time evaluation is conducted in a simulated environment in which the smart vehicle is implemented through software components. The evaluation results provide empirical evidence about the applicability of SACRE in real and complex software system domains.Comment: 45 pages, journal article, 14 figures, 9 tables, CC-BY-NC-ND 4.0 licens

SACRE: Supporting contextual requirements’ adaptation in modern self-adaptive systems in the presence of uncertainty at runtime

Runtime uncertainty such as unpredictable resource unavailability, changing environmental conditions and user needs, as well as system intrusions or faults represents one of the main current challenges of self-adaptive systems. Moreover, today’s systems are increasingly more complex, distributed, decentralized, etc. and therefore have to reason about and cope with more and more unpredictable events. Approaches to deal with such changing requirements in complex today’s systems are still missing. This work presents SACRE (Smart Adaptation through Contextual REquirements), our approach leveraging an adaptation feedback loop to detect self-adaptive systems’ contextual requirements affected by uncertainty and to integrate machine learning techniques to determine the best operationalization of context based on sensed data at runtime. SACRE is a step forward of our former approach ACon which focus had been on adapting the context in contextual requirements, as well as their basic implementation. SACRE primarily focuses on architectural decisions, addressing selfadaptive systems’ engineering challenges. Furthering the work on ACon, in this paper, we perform an evaluation of the entire approach in different uncertainty scenarios in real-time in the extremely demanding domain of smart vehicles. The real-time evaluation is conducted in a simulated environment in which the smart vehicle is implemented through software components. The evaluation results provide empirical evidence about the applicability of SACRE in real and complex software system domains.Peer ReviewedPostprint (author's final draft

[email protected] (Dagstuhl Seminar 11481)

This report documents the program and the outcomes of Dagstuhl Seminar 11481 ``[email protected]\u27\u27. Research on [email protected] seeks to extend the applicability of models and abstractions to the runtime environment, with the goal of providing effective technologies for managing the complexity of evolving software behaviour while it is executing. The Dagstuhl Seminar ``[email protected]\u27\u27 brought together a diverse set of researchers and practitioners with a broad range of expertise, including MDE, software architectures, reflection, self-adaptive systems, validation and verification, middleware, robotics and requirements engineering

Constructing runtime models with bigraphs to address ubiquitous computing service composition volatility

In this thesis, we explore the appropriateness of the language abstractions provided by Bigraphs to construct a model at runtime to tackle the problem of volatility in a service composition running on a mobile device. Our contributions to knowledge are as follows: 1) We have shown that Bigraphs (Milner, 2009) are suitable for expressing models at runtime. 2) We have offered Bigraph language abstractions as an appropriate solution to some of the research problems posed by the models at runtime community (Aßmann et al., 2012). 3) We have discussed the general lessons learnt from using Bigraphs for a practical application such as a model at runtime. 4) We have discussed the general lessons learnt from our experiences of designing models at runtime. 5) We have implemented the model at runtime using the BPL Tool (ITU, 2011) and have experimentally studied the response times of our Bigraphical model. We have suggested appropriate enhancements for the tool based on our experiences. We present techniques to parameterize the reaction rules so that the matching algorithm of the BPL Tool returns a single match giving us the ability to dynamically program the model at runtime. We also show how to query the Bigraph structure

Improving Priority-Awareness Of Non-Functional Requirements During Decision-Making In Self-Adaptive Systems

Self-adaptive systems (SASs) exhibit autonomous decision-making to deal with uncertainty in their operating environments. A fundamental problem with SASs is to ensure that their requirements remain satisfied as they adapt. Trade-off analysis of the non-functional re-quirements (NFRs), based on their satisfaction priorities, is a key to establishing a balance among them. Such trade-off analysis is often based on optimization techniques comprising decision analysis and utility theory. A problem with these techniques is that they use a single-scalar utility value to specify a combined priority for all the NFRs. Nevertheless, this combined priority does not give any information about the impacts of the environmental contexts on the individual priorities of NFRs. Moreover, these separate NFR priorities may change according to the runtime environmental contexts. Therefore, there is a need to have an approach that supports the runtime, autonomous reasoning with the distinct priorities of NFRs during the decision-making process. This PhD thesis addresses this problem by presenting Pri-AwaRE, a self-adaptive architecture for decision-making in SASs. The approach uses Multi-Reward Partially Observable Markov Decision Process (MR-POMDP) as a runtime specification model to support the modelling of the individual NFRs’ priorities. The MR-POMDP model also provides runtime reasoning and autonomous tuning of these separate priorities. Therefore, it underpins priority-aware decisions. The approach has been evaluated using two substantial case studies from the different networking domains. A comparison with other state-of-the-art approaches has also been carried out. The results have shown that the priority-aware decisions offered by Pri-AwaRE provide compliance with the requirements for both the case studies even under the changing environmental contexts at runtim

Testing Self-Adaptive Systems

Autonomy is the most demanded yet hard-to-achieve feature of recent and future software systems. Self-driving cars, mail-delivering drones, automated guided vehicles in production sites, and housekeeping robots need to decide autonomously during most of their operation time. As soon as human intervention becomes necessary, the cost of ownership increases, and this must be avoided. Although the algorithms controlling autonomous systems become more and more intelligent, their hardest opponent is their inflexibility. The more environmental situations such a system is confronted with, the more complexity the control of the autonomous system will have to master. To cope with this challenge, engineers have approached a system design, which adopts feedback loops from nature. The resulting architectural principle, which they call self-adaptive systems, follows the idea of iteratively gathering sensor data, analyzing it, planning new adaptations of the system, and finally executing the plan. Often, adaptation means to alter the system setup, re-wire components, or even exchange control algorithms to keep meeting goals and requirements in the newly appeared situation. Although self-adaptivity helps engineers to organize the vast amount of information in a self-deciding system, it remains hard to deal with the variety of contexts, which involve both environmental influences and knowledge about the system\'s internals. This challenge not only holds for the construction phase but also for verification and validation, including software test. To assure sufficient quality of a system, it must be tested under an enormous and, thus, unmanageable, number of different contextual situations and manual test-cases. This thesis proposes a novel set of methods and model types, which help test engineers to specify precisely what they expect from a self-adaptive system under test. The formal nature of the introduced artifacts allows for automatically generating test-suites or running simulations in the loop so that a qualitative verdict on the system\'s correctness can be gained. Additional to these conceptional contributions, the thesis describes a model-based adaptivity test environment, which test engineers can use for testing actual self-adaptive systems. The implementation includes comprehensive tooling for creating the introduced types of models, generating test-cases, simulating them in the loop, automating tests, and reporting. Composing all enabling components for these tasks constitutes a reference architecture of integrated test environments for self-adaptive systems. We demonstrate the completeness and accuracy of the technical approach together with the underlying concepts by evaluating them in an experimental case study where an autonomous robot interacts with human co-workers. In summary, this thesis proposes concepts for automatically and, thus, efficiently testing self-adaptive systems. The quality, which is fostered by this novel approach, is resilience: the ability of a system to maintain its promises while facing changing environments.:1 Introduction 1 1.1 Problem Description 1 1.2 Overview of Adopted Methods 3 1.3 Hypothesis and Main Contributions 4 1.4 Organization of This Thesis 5 I Foundations 7 2 Background 9 2.1 Self-adaptive Software and Autonomic Computing 9 2.1.1 Common Principles and Components of SAS 10 2.1.2 Concrete Implementations and Applications of SAS 12 2.2 Model-based Testing 13 2.2.1 Testing for Dependability 14 2.2.2 The Basics of Testing 15 2.2.3 Automated Test Design 18 2.3 Dynamic Variability Management 22 2.3.1 Software Product Lines 23 2.3.2 Dynamic Software Product Lines 25 3 Related Work: Existing Research on Testing Self-Adaptive Systems 29 3.1 Testing Context-Aware Applications 30 3.2 The SimSOTA Project 31 3.3 Dynamic Variability in Complex Adaptive Systems (DiVA) 33 3.4 Other Early-Stage Research 34 3.5 Taxonomy of Requirements of Model-based SAS Testing 36 II Methods 39 4 Model-driven SAS Testing 41 4.1 Problem/Solution Fit 41 4.2 Example: Surveillance Drone 43 4.3 Concepts and Models for Testing Self-Adaptive Systems 44 4.3.1 Test Case Generation vs. Simulation in the Loop 44 4.3.2 Incremental Modeling Process 45 4.3.3 Basic Representation Format: Petri Nets 46 4.3.4 Context Variation 50 4.3.5 Modeling Adaptive Behavior 53 4.3.6 Dynamic Context Change 57 4.3.7 Interfacing Context from Behavioral Representation 62 4.3.8 Adaptation Mode Variation 64 4.3.9 Context-Dependent Recon guration 67 4.4 Adequacy Criteria for SAS Test Models 71 4.5 Discussion on the Viability of the Employed Models 71 4.6 Comparison to Related Work 73 4.7 Summary and Discussion 74 5 Model-based Adaptivity Test Environment 75 5.1 Technological Foundation 76 5.2 MATE Base Components 77 5.3 Metamodel Implementation 78 5.3.1 Feature-based Variability Model 79 5.3.2 Abstract and Concrete Syntax for Textual Notations 80 5.3.3 Adaptive Petri Nets 86 5.3.4 Stimulus and Recon guration Automata 87 5.3.5 Test Suite and Report Model 87 5.4 Test Generation Framework 87 5.5 Test Automation Framework 91 5.6 MATE Tooling and the SAS Test Process 93 5.6.1 Test Modeling 94 5.6.2 Test Case Generation 95 5.6.3 Test Case Execution and Test Reporting 96 5.6.4 Interactive Simulation Frontend 96 5.7 Summary and Discussion 97 III Evaluation 99 6 Experimental Study: Self-Adaptive Co-Working Robots 101 6.1 Robot Teaching and Co-Working with WEIR 103 6.1.1 WEIR Hardware Components 104 6.1.2 WEIR Software Infrastructure 105 6.1.3 KUKA LBR iiwa as WEIR Manipulator 106 6.1.4 Self-Adaptation Capabilities of WEIR 107 6.2 Cinderella as Testable Co-Working Application 109 6.2.1 Cinderella Setup and Basic Functionality 109 6.2.2 Co-Working with Cinderella 110 6.3 Testing Cinderella with MATE 112 6.3.1 Automating Test Execution 112 6.3.2 Modeling Cinderella in MATE 113 6.3.3 Testing Cinderella in the Loop 121 6.4 Evaluation Verdict and Summary 123 7 Summary and Discussion 125 7.1 Summary of Contributions 126 7.2 Open Research Questions 127 Bibliography 129 Appendices 137 Appendix Cinderella De nitions 139 1 Cinderella Adaptation Bounds 139 2 Cinderella Self-adaptive Workflow 14