Made available in DSpace on 2014-12-17T15:48:09Z (GMT). No. of bitstreams: 1 FernandoHLS_DISSERT.pdf: 2616798 bytes, checksum: 22d85c8d781aecef20ff0906635a692b (MD5) Previous issue date: 2013-08-05 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / One way to deal with the high complexity of current software systems is through selfadaptive systems. Self-adaptive system must be able to monitor themselves and their environment, analyzing the monitored data to determine the need for adaptation, decide how the adaptation will be performed, and finally, make the necessary adjustments. One way to perform the adaptation of a system is generating, at runtime, the process that will perform the adaptation. One advantage of this approach is the possibility to take into account features that can only be evaluated at runtime, such as the emergence of new components that allow new architectural arrangements which were not foreseen at design time. In this work we have as main objective the use of a framework for dynamic generation of processes to generate architectural adaptation plans on OSGi environment. Our main interest is evaluate how this framework for dynamic generation of processes behave in new environments / Uma forma de lidar com a elevada complexidade dos sistemas de software atuais ? atrav?s de sistemas autoadaptativos. Sistema autoadaptativos devem ser capazes de monitorar a si mesmos e ao seu ambiente, analisar os dados monitorados para determinar a necessidade de adapta??o, decidir como a adapta??o ser? realizada e, por fim, realizar as adapta??es necess?rias. Uma das maneiras de realizar a adapta??o de um sistema ? gerar, em tempo de execu??o, o processo que ir? executar a adapta??o. Uma vantagem dessa abordagem ? a possibilidade de levar em considera??o caracter?sticas que s? podem ser avaliadas em tempo de execu??o, como por exemplo, o surgimento de novos componentes que possibilitem novos arranjos arquiteturais que n?o foram previstos em tempo de projeto. Neste trabalho, temos como principal objetivo a utiliza??o de um framework de gera??o din?mica de processos para gerar planos de adapta??o arquiteturais sobre o ambiente OSGi. Nosso principal interesse consiste em verificar como esse framework de gera??o din?mica de processos se comporta em novos ambientes

Self-adaptive systems are capable of autonomously adjusting their behavior at runtime to accomplish particular adaptation goals. The most common way to realize self-adaption is using a feedback loop(s) which contains four actions: collect runtime data from the system and its environment, analyze the collected data, decide if an adaptation plan is required, and act according to the adaptation plan for achieving the adaptation goals. Existing approaches achieve the adaptation goals by using formal methods, and exhaustively verify all the available adaptation options, i.e., adaptation space. However, verifying the entire adaptation space is often not feasible since it requires time and resources. In this thesis, we present an approach which uses machine learning to reduce the adaptation space in self-adaptive systems. The approach integrates with the feedback loop and selects a subset of the adaptation options that are valid in the current situation. The approach is applied on the simulator of a self-adaptive Internet of Things application which is deployed in KU Leuven, Belgium. We compare our results with a formal model based self-adaptation approach called ActivFORMS. The results show that on average the adaptation space is reduced by 81.2% and the adaptation time by 85% compared to ActivFORMS while achieving the same quality guarantees.

Future applications will run in a highly heterogeneous and dynamic execution environment that forces them to adapt their behavior and offered functionality depending on the user's or the system's current situation. Since application components in such heterogeneous multi-device systems will be distributed over multiple interconnected devices and cooperate to achieve a common goal, a coordinated adaptation is required to ensure a consistent system behavior. In this paper we present a decentralized adaptation middleware to adapt a distributed software system. Our approach supports the reliable execution of multiple adaptation operations that depend on each other and are performed transactionally even in unsteady environments coined by message loss or node failures. We implemented our approach in a search-and-rescue robot scenario to show its feasibility and conduct first performance evaluations.

The complexity of computer networks has been rising over the last decades. Increasing interconnectivity between multiple devices, growing complexity of performed tasks and a strong collaboration between nodes are drivers for this phenomenon. An example is represented by Internet-of-Things devices, whose relevance has been rising in recent years. The increasing number of devices requiring updates and supervision makes maintenance more difficult. Human interaction, in this case, is costly and requires a lot of time. To overcome this, self-adaptive software systems (SAS) can be used. SAS are a subset of autonomous systems which can monitor themselves and their environment to adapt to changes without human interaction. In the literature, different approaches for engineering SAS were proposed, including techniques for executing adaptations on multiple devices based on generated plans for reacting to changes. Among those solutions, also decentralised approaches can be found. To the best of our knowledge, no approach for engineering a SAS exists which tolerates errors during the execution of adaptation in a decentralised setting. While some approaches for role-based execution reset the application in case of a single failure during the adaptation process, others do not make assumptions about errors or do not consider an erroneous environment. In a real-world environment, errors will likely occur during run-time, and the adaptation process could be disturbed. This work aims to perform adaptations in a decentralised way on role-based systems with a relaxed consistency constraint, i.e., errors during the adaptation phase are tolerated. This increases the availability of nodes since no rollbacks are required in case of a failure. Moreover, a subset of applications, such as drone swarms, would benefit from an approach with a relaxed consistency model since parts of the system that adapted successfully can already operate in an adapted configuration instead of waiting for other peers to apply the changes in a later iteration. Moreover, if we eliminate the need for an atomic adaptation execution, asynchronous execution of adaptation would be possible. In that case, we can supervise the adaptation process for a long time and ensure that every peer takes the planned actions as soon as the internal task execution allows it. To allow for a relaxed consistent way of adaptation execution, we develop a decentralised adaptation execution protocol, which supports the notion of eventual consistency. As soon as devices reconnect after network congestion or restore their internal state after local failures, our protocol can coordinate the recovery process among multiple devices to attempt recovery of a globally consistent state after errors occur. By superseding the need for a central instance, every peer who received information about failing peers can start the recovery process. The developed approach can restore a consistent global configuration if almost all peers fail. Moreover, the approach supports asynchronous adaptations, i.e., the peers can execute planned adaptations as soon as they are ready, which increases overall availability in case of delayed adaptation of single nodes. The developed protocol is evaluated with the help of a proof-of-concept implementation. The approach was run in five different experiments with thousands of iterations to show the applicability and reliability of this novel approach. The time for execution of the protocol and the number of exchanged messages has been measured to compare the protocol for different error cases and system sizes, as well as to show the scalability of the approach. The developed solution has been compared to a blocking approach to show the feasibility compared to an atomic approach. The applicability in a real-world scenario has been described in an empirical study using an example of a fire-extinguishing drone swarm. The results show that an optimistic approach to adaptation is suitable and specific scenarios can benefit from the improved availability since no rollbacks are required. Systems can continue their work regardless of the failures of participating nodes in large-scale systems.:Abstract VI 1. Introduction 1 1.1. Motivational Use-Case 2 1.2. Problem Definition 3 1.3. Objectives 4 1.4. Research Questions 5 1.5. Contributions 5 1.6. Outline 6 2. Foundation 7 2.1. Role Concept 7 2.2. Self-Adaptive Software Systems 13 2.3. Terminology for Role-Based Self-Adaptation 15 2.4. Consistency Preservation and Consistency Models 17 2.5. Summary 20 3. Related Work 21 3.1. Role-Based Approaches 22 3.2. Actor Model of Computation and Akka 23 3.3. Adaptation Execution in Self-Adaptive Software Systems 24 3.4. Change Consistency in Distributed Systems 33 3.5. Comparison of the Evaluated Approaches 40 4. The Decentralised Consistency Compensation Protocol 43 4.1. System and Error Model 43 4.2. Requirements to the Concept 44 4.3. The Usage of Roles in Adaptations 45 4.4. Protocol Overview 47 4.5. Protocol Description 51 4.6. Protocol Corner- and Error Cases 64 4.7. Summary 66 5. Prototypical Implementation 67 5.1. Technology Overview 67 5.2. Reused Artifacts 68 5.3. Implementation Details 70 5.4. Setup of the Prototypical Implementation 76 5.5. Summary 77 6. Evaluation 79 6.1. Evaluation Methodology 79 6.2. Evaluation Setup 80 6.3. Experiment Overview 81 6.4. Default Case: Successful Adaptation 84 6.5. Compensation on Disconnection of Peers 85 6.6. Recovery from Failed Adaptation 88 6.7. Impact of Early Activation of Adaptations 91 6.8. Comparison with a Blocking Approach 92 6.9. Empirical Study: Fire Extinguishing Drones 95 6.10. Summary 97 7. Conclusion and Future Work 99 7.1. Recap of the Research Questions 99 7.2. Discussion 101 7.3. Future Work 101 A. Protocol Buffer Definition 103 Acronyms 108 Bibliography 109

L'élasticité est une propriété qui permet aux systèmes Cloud de s'auto-adapter à leur charge de travail en provisionnant et en libérant des ressources informatiques, de manière autonomique, lorsque la demande augmente et diminue. En raison de la nature imprévisible de la charge de travail et des nombreux facteurs déterminant l'élasticité, fournir des plans d'action précis pour gérer l'élasticité d'un système cloud, tout en respectant des politiques de haut niveau (performances, cout, etc.) est une tâche particulièrement difficile.Les travaux de cette thèse visent à proposer, en utilisant le formalisme des bigraphes comme modèle formel, une spécification et une implémentation des systèmes Cloud Computing élastiques sur deux aspects : structurel et comportemental.Du point de vue structurel, le but est de définir et de modéliser une structure correcte des systèmes Cloud du côté " backend ". Cette partie est supportée par les capacités de spécification fournies par le formalisme des Bigraphes, à savoir : le principe de "sorting" et de règles de construction permettant de définir les desiderata du concepteur. Concernant l'aspect comportemental, il s'agit de modéliser, valider et implémenter des stratégies génériques de mise à l'échelle automatique en vue de décrire les différents mécanismes d'auto-adaptation élastiques des systèmes cloud (mise à l'échelle horizontale, verticale, migration, etc.), en multi-couches (i.e., aux niveaux service et infrastructure). Ces tâches sont prises en charge par les aspects dynamiques propres aux Systèmes Réactifs Bigraphiques (BRS) notamment par le biais des règles de réaction.Les stratégies d'élasticité introduites visent à guider le déclenchement conditionnel des différentes règles de réaction définies, afin de décrire les comportements d'auto-adaptation des systèmes Cloud au niveau service et infrastructure. L'encodage de ces spécifications et leurs implémentations sont définis en logique de réécriture via le langage Maude. Leur bon fonctionnement est vérifié formellement à travers une technique de model-checking supportée par la logique temporelle linéaire LTL.Afin de valider ces contributions d'un point de vue quantitatif, nous proposons une approche à base de file d'attente pour analyser, évaluer et discuter les stratégies d'élasticité d'un système Cloud à travers différents scénarios simulés. Dans nos travaux, nous explorons la définition d'une "bonne" stratégie en prenant en compte une étude de cas qui repose sur la nature changeante de la charge de travail. Nous proposons une manière originale de composer plusieurs stratégies d'élasticité à plusieurs niveaux afin de garantir différentes politiques de haut-niveau. / Elasticity property allows Cloud systems to adapt to their incoming workload by provisioning and de-provisioning computing resources in an autonomic manner, as the demand rises and drops. Due to the unpredictable nature of the workload and the numerous factors that impact elasticity, providing accurate action plans to insure a Cloud system's elasticity while preserving high level policies (performance, costs, etc.) is a particularly challenging task. This thesis aims at providing a thorough specification and implementation of Cloud systems, by relying on bigraphs as a formal model, over two aspects: structural and behavioral.Structurally, the goal is to define a correct modeling of Cloud systems' "back-end" structure. This part is supported by the specification capabilities of Bigraph formalism. Specifically, via "sorting" mechanisms and construction rules that allow defining the designer's desiderata. As for the behavioral part, it consists of model, implement and validate generic elasticity strategies in order to describe Cloud systems' auto-adaptive behaviors (i.e., horizontal and vertical scaling, migration, etc.) in a cross-layer manner (i.e., at service and infrastructure levels). These tasks are supported by the dynamic aspects of Bigraphical Reactive Systems (BRS) formalism (through reaction rules).The introduced elasticity strategies aim at guiding the conditional triggering of the defined reaction rules, to describe Cloud systems' auto-scaling behaviors in a cross-layered manner. The encoding of these specifications and their implementation are defined in Rewrite Logic via Maude language. Their correctness is formally verified through a model-checking technique supported by the linear temporal logic LTL.In order to quantitatively validate these contributions, we propose a queuing-based approach in order to evaluate, analyze and discuss elasticity strategies in Cloud systems through different simulated execution scenarios. In this work, we explore the definition of a “good” strategy through a case study which considers the changing nature of the input workload. We propose an original way de compose different cross-layer elasticity strategies to guarantee different high-level policies.

Cette étude s’inscrit dans le domaine de l’intelligence artificielle, plus précisément au croisement des deux domaines que sont la planification autonome en environnement probabiliste et la vérification formelle probabiliste. Dans ce contexte, elle pose la question de la maîtrise de la complexité face à l’intégration de nouvelles technologies dans les systèmes critiques : comment garantir que l’ajout d’une intelligence à un système, sous la forme d’une autonomie, ne se fasse pas au détriment de la sécurité ? Pour répondre à cette problématique, cette étude a pour enjeu de développer un processus outillé, permettant de concevoir des systèmes auto-adaptatifs critiques, ce qui met en œuvre à la fois des méthodes de modélisation formelle des connaissances d’ingénierie, ainsi que des algorithmes de planification sûre et optimale des décisions du système. / This study takes place in the broad field of Artificial Intelligence, specifically at the intersection of two domains : Automated Planning and Formal Verification in probabilistic environment. In this context, it raises the question of the integration of new technologies in critical systems, and the complexity it entails : How to ensure that adding intelligence to a system, in the form of autonomy, is not done at the expense of safety ? To address this issue, this study aims to develop a tool-supported process for designing critical, self-adaptive systems. Throughout this document, innovations are therefore proposed in methods of formal modeling and in algorithms for safe and optimal planning.

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 140

[pt] Nos últimos anos, foi desenvolvido um número significativo de sistemas autoadaptativos (i.e.: sistemas capazes de saber o que está acontecendo sobre si mesmo e que, consequentemente, implementam parcialmente a qualidade de consciência). A literatura tem pesquisado extensivamente o uso da engenharia de requisitos orientada a metas e o uso da arquitetura de referência MAPE (Monitor-Analyze-Plan-Execute) para o desenvolvimento de sistemas autoadaptativos. Entretanto, construir tais sistemas com base em estratégias de referência não é trivial, podendo resultar em problemas estruturais que impactam negativamente alguns atributos de qualidade do produto final (e.g.: reusabilidade, modularidade, modificabilidade e entendibilidade). Neste contexto, estratégias de reengenharia para a reorganização de tais sistemas são pouco exploradas, limitando-se a recuperar e a reestruturar a lógica da adaptação em modelos de baixo nível. Esta prática mantém a dificuldade do tratamento da qualidade de consciência como um requisito não funcional (RNF) de primeira classe, impactando diretamente na seleção da arquite-tura e implementação do sistema. Nossa pesquisa visa mitigar esse problema atra-vés de uma estratégia de reengenharia de sistemas autoadaptativos, centrada no RNF de consciência de software, com vistas a auxiliar na remoção de alguns problemas recorrentes na implementação do MAPE conforme a literatura. A estratégia de reengenharia está organizada em quatro subprocessos: (A) recuperar a intencio-nalidade do sistema com ênfase em suas metas de consciência, gerando um modelo de metas AS-IS; (B) especificar o modelo de metas TO-BE reutilizando um conjunto de SRconstructs para operacionalizar o RNF de consciência de software conforme o padrão MAPE; (C) redesenhar o sistema revisando as operacionalizações de consciência e selecionando as tecnologias para implementar o MAPE, e; (D) finalmente, reimplementar o sistema conforme nova estrutura, adicionando metainformações de código para manter a rastreabilidade para o mecanismo de autoadaptação visando facilitar novas evoluções. O escopo da nossa pesquisa são sistemas autoadaptativos orientados a objetos (OO), utilizando o framework i como linguagem para os modelos orientados a metas. Nossos resultados de avaliações em sistemas auto-adaptativos OO desenvolvidos em Java para dispositivos móveis com Android demonstram que a estratégia auxilia no realinhamento do sistema com as boas práticas recomendadas pela literatura facilitando futuras evoluções. / [en] In recent years, a significant number of self-adaptive systems (i.e.: systems capable of knowing what is happening about themselves, and consequently partially implementing the quality of awareness) have been developed. The literature has extensively researched the use of goal oriented requirements engineering and the use of the MAPE (Monitor-Analyze-Plan-Execute) reference architecture for the development of self-adaptive systems. However, building such systems based on reference strategies is not trivial, it can result in structural problems that negatively impact some quality attributes of the final product (e.g.: reusability, modularity, modifiability and understandability). In this context, reengineering strategies for the reorganization of such systems are poor explored, and they are limited to recovering and restructuring the logic of adaptation in low-level models. This approach keeps the difficulty of treating the awareness quality as a first-class non-functional re-quirement (NFR) directly affecting architecture selection and implementation of the system. Our research aims to mitigate this problem through a strategy of reengi-neering self-adaptive systems, centered on software awareness as an NFR. This strategy will assist in the removal of some recurring problems in the implementation of MAPE according to the literature. The reengineering strategy is organized into four sub-processes: (A) recover the intentionality of the system with an emphasis on its awareness goals, generating an AS-IS goal model; (B) specify the TO-BE goal model by reusing a set of SRconstructs to operationalize the software awareness NFR according to the MAPE standard; (C) redesign the system by reviewing the operationalizations of awareness and selecting the technologies to implement the MAPE, and; (D) finally, reimplement the system according to a new structure, add-ing code metadata to maintain traceability for the self-adaptation mechanism in or-der to facilitate new evolutions. The scope of our research is object-oriented (OO) self-adaptive systems using the i framework as a language for goal-oriented models. Our results of evaluations, for OO self-adaptive systems developed in Java for mobile devices with Android, show that the strategy helps in realigning the system with the best practices recommended by the, facilitating future developments.

Self-adaptive systems have limited time to adjust their configurations whenever their adaptation goals, i.e., quality requirements, are violated due to some runtime uncertainties. Within the available time, they need to analyze their adaptation space, i.e., a set of configurations, to find the best adaptation option, i.e., configuration, that can achieve their adaptation goals. Existing formal analysis approaches find the best adaptation option by analyzing the entire adaptation space. However, exhaustive analysis requires time and resources and is therefore only efficient when the adaptation space is small. The size of the adaptation space is often in hundreds or thousands, which makes formal analysis approaches inefficient in large-scale self-adaptive systems. In this thesis, we tackle this problem by presenting an online learning approach that enables formal analysis approaches to analyze large adaptation spaces efficiently. The approach integrates with the standard feedback loop and reduces the adaptation space to a subset of adaptation options that are relevant to the current runtime uncertainties. The subset is then analyzed by the formal analysis approaches, which allows them to complete the analysis faster and efficiently within the available time. We evaluate our approach on two different instances of an Internet of Things application. The evaluation shows that our approach dramatically reduces the adaptation space and analysis time without compromising the adaptation goals.