Self-adaptive Systems Research Articles

Modeling Autonomic Systems

Today software systems are very complex and changing. Therefore, preserving their functionality and the quality of their service seems to be a difficult. Consequently, the concept of self-adaptation was introduced to handle the problem of the increasing complexity and dynamism of modern software systems. In this context, researchers have been studying self-adaptation for over a decade, which has resulted in a vast body of knowledge. Nevertheless, as technology progresses and software systems are increasingly integrated, new challenges emerge. Among these challenges are the need for new engineering approaches and methods to model and verify self-adaptive systems.In this paper, we provide a survey on modeling and verifying self-adaptive systems through presenting and classifying the main existing approaches that have made significant progress in this area.

Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-adaptive Systems

Many software systems today face uncertain operating conditions, such as sudden changes in the availability of resources or unexpected user behavior. Without proper mitigation these uncertainties can jeopardize the system goals. Self-adaptation is a common approach to tackle such uncertainties. When the system goals may be compromised, the self-adaptive system has to select the best adaptation option to reconfigure by analyzing the possible adaptation options, i.e., the adaptation space. Yet, analyzing large adaptation spaces using rigorous methods can be resource- and time-consuming, or even be infeasible. One approach to tackle this problem is by using online machine learning to reduce adaptation spaces. However, existing approaches require domain expertise to perform feature engineering to define the learner and support online adaptation space reduction only for specific goals. To tackle these limitations, we present “Deep Learning for Adaptation Space Reduction Plus”—DLASeR+ for short. DLASeR+ offers an extendable learning framework for online adaptation space reduction that does not require feature engineering, while supporting three common types of adaptation goals: threshold, optimization, and set-point goals. We evaluate DLASeR+ on two instances of an Internet-of-Things application with increasing sizes of adaptation spaces for different combinations of adaptation goals. We compare DLASeR+ with a baseline that applies exhaustive analysis and two state-of-the-art approaches for adaptation space reduction that rely on learning. Results show that DLASeR+ is effective with a negligible effect on the realization of the adaptation goals compared to an exhaustive analysis approach and supports three common types of adaptation goals beyond the state-of-the-art approaches.

AUTOMATED ADAPTIVE LIGHT CONTROL FOR AUTOMOBILES

The purpose of this paper is to discuss the design and operation of self-adaptive lights systems in automobiles. The primary goal of this approach is to present an inexpensive methodology for illuminating blind spots and to make the objects visible in dark while driving and during periods of significant diminished visibility in dark locations, thereby avoiding accidents. The reasons for most road accidents without the control of a driver would be due to high beam lights approaching from the vehicles in the opposite direction and the fog causing invisibility of road depending on weather condition and since the beams are focused in a particular direction, it would be difficult in curves to handle the worst situations in driving. To address this problem, motorized headlights that turn according to the steering wheel of a car with adjustable brightness, and depending on atmospheric moisture, fog light is controlled and automatic dim and dip option is used on the basis of opposite vehicle's beam are being proposed as a solution.

Energy Management Model for a Standalone Hybrid Microgrid through a Particle Swarm Optimization and Artificial Neural Networks Approach

Energy management systems are usually used to integrate different energy sources into a coordinated microgrid system. However, given the variability of renewable sources and the complexity of calculating renewable resource availability and managing energy, it is not easy to incorporate efficient energy management models in a microgrid. This work focuses on developing a methodology to incorporate optimized artificial networks into a self-adaptable energy management system to improve microgrids performance. The proposed model consists of a set of artificial neural networks organized into a cascade configuration. A Particle Swarm Optimization algorithm optimizes each artificial neural network; the proposed model aims to estimate and provide information to the energy management system. The model is implemented in MATLAB/Simulink environment and fed with experimental data. Correlation analysis of system variables between the different artificial neural networks is performed to validate the proposed model. Simulated tests are performed with scenarios using experimental data, and an analysis of the system's response is performed in terms of the root mean squared error and linear regression. The results showed that, compared to related works, the proposed model reduced errors by 59% and 56% for single and multiple-step prediction of energy parameter estimators. Regarding the fitness of the power estimator from the EMM for the test scenarios, an 0.1245 RMSE was obtained.

Simultaneous in situ Direction Finding and Field Manipulation Based on Space-Time-Coding Digital Metasurface

The smart radio environment has become an eye-catching topic and is believed to own great potential in future wireless networks. Its essential demand lies in sensing and manipulating the electromagnetic (EM) fields accurately and simultaneously with the same aperture, which is hard to achieve using conventional methods due to the difficulty in integrating different systems. We propose a millimeter-wave space–time-coding digital metasurface (STCM) to realize simultaneous <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> direction finding and field manipulation. An analytic method of direction finding is first developed using the STCM modulated by a space–time-coding (STC) matrix while maintaining its original capability of EM field manipulations. In addition, three STC matrices corresponding to different EM functions are applied to the STCM to verify the feasibility and robustness of direction findings experimentally. Finally, a proof-of-concept prototype is built to exhibit different EM functions according to the incident angle detected simultaneously. The proposed concept sets the stage for the integrated implementation of the perception and response of the radio environment, which may find promising applications in future intelligent wireless networks and self-adaptive systems.

An efficient diagnosis approach for bearing faults using sound quality metrics

The perception of sound quality is an important source of information and can be used as a promising indicator to diagnose various faults in rotating machines. This work presented a methodology involving the detection of bearing faults using sound quality metrics. Head and Torso Simulator (HATS) was used to acquire the sound signals of different bearing fault conditions with a minimal background noise using a semi-anechoic chamber. After that, various sound quality features were extracted from the acquired signals, and descriptive analysis for five bearing conditions was presented based on these features. Moreover, these features were used to develop a self-adaptive fault diagnosis system using a Support Vector Machine (SVM). Experimental validation of the proposed method was also done with different background conditions and with different microphones. The results showed that the proposed methodology is reliable and effective for accurate fault diagnosis of bearings in a non-invasive manner.

State-Constraint Transition: A Language for the Formal Specification of Dynamic Cyber-System Requirements

Existing formal languages for the specification of self-adaptive cyber-physical systems focus on re-configuring the system-to-be depending on its current context, to satisfy the user’s requirements, that is by dynamically composing the software’s structure and behavior. While these approaches specify context-sensitive requirements, they rarely consider their run-time dynamic and scalable nature. The State-Constraint Transition (SCT) modeling language, introduced in this paper, provides an answer to the problems linked to the specification of dynamic requirements by introducing the concept of configuration states, in which requirements are translated into constraints. The expressiveness of existing approaches is thus extended, combining the ease of use of well-established notations, notably those based on characteristics, and those based on Finite-state Machines (FSM), with the computational power and expressiveness of the constraint programming approach. The paper briefly presents the results of the preliminary evaluation, which assesses the expressiveness, scalability, and domain independence of the SCT language.

SRE_BBC: A Self-Adaptive Security Enabled Requirements Engineering Approach for SLA Smart Contracts in Blockchain-Based Cloud Systems.

Current blockchain-based cloud (BBC) systems have several security vulnerabilities regarding smart contracts (SC), and several attacks have been reported recently. The SC development lacks standard design processes that follow software lifecycle principles to model secure SC. Secondly, the security mechanisms in the SC are not constantly evolved to resist evolving adversary attacks. BBC systems lack self-adaptive security capability to make spontaneous decisions when adversarial attacks are encountered. To build a self-adaptive secure BBC system that follows standard software development lifecycle principles to model secure SC, we propose the so-called self-adaptive security RE_BBC framework. The framework would utilize the MAPE-BBC adaptation loop to make decisions internally based on the threat models, goal models, and service level agreement (SLA) SC security specifications. The framework identifies vulnerabilities and threats and takes precautionary measures using self-adaptive SC agents. We validated the proposed methodology theoretically and empirically, and statistically proved the research questions and hypothesis using the t-test and Mann–Whitney U test. Subsequently, we compare our proposed approach with the Security Quality Requirements Engineering approach (SQUARE). The feasibility results and the replicated study results indicate that the proposed approach outperformed the SQUARE approach in terms of artifacts quality, self-adaptive security evaluation quality, efficiency in response time, complexity, and usefulness of the proposed approach for the Healthcare Data Management (HDM) system. SC security developers can immensely benefit from our proposed methodology. They need not reengineer SC from scratch; depending on their security needs and plan, the contract can be adapted to execute a new plan.

Nonequilibrium regulation of interfacial chemistry for transient macroscopic supramolecular assembly

HypothesisMacroscopic nonequilibrium assembly helps to interpret the interfacial assembly mechanism and promotes the creation of self-adaptive chemical systems for potential applications such as controlled release and mass transfer. We propose that the precise macroscopic nonequilibrium assembly can be achieved by rationally integrating pH-responsive hybrid polymer hydrogels with the urea-urease clock reaction. ExperimentsHerein, pH-responsive and urease-containing polyelectrolyte hydrogels were fabricated for achieving macroscopic nonequilibrium assembly. Optical microscopy was used to visualize the interface of the macroscopic assemblies. Tensile tests were performed to investigate the adhesion strength of the assemblies and the modulus of the hydrogels. The charges and electric field distribution at the hydrogel surface were estimated by zeta potential measurements. Furthermore, an in situ sewing-growing method for fabricating hybrid polymer hydrogels with modularized surface chemistry was presented to achieve precise nonequilibrium assembly. FindingsThe realization of precise macroscopic nonequilibrium assembly relies on the transient electrostatic attraction between the hybrid polymer hydrogels, which lays the foundation for yielding transient macroscopic supramolecular devices potentially useful for timed release. The stability and lifetime of the transient assemblies can be controlled by adjusting the hydrogel synthesis parameters and fuel compositions, and the macroscopic nonequilibrium assembly can be repeated by refueling the system.

Ontological Approach to Support the Horizontal and Vertical Information Integration in Smart Manufacturing Systems: An Experimental Case in a Long-Life Packaging Factory

Digital manufacturing has been challenged by the manufacturing industry to rationalize different ways to connect and exchange information and knowledge across different phases of manufacturing systems. One of the Industry 4.0 pillars is the horizontal and vertical integration with intelligent and self-adaptive systems. For this to be possible, the manufacturing industry applies an extensive range of software tools, such as GRAI, CIMOSA, MO2GO, ARIS, SCADA, MES, ERP, CAD, and CAM. Individually, each one performs its function to support the manufacturing process. However, when these multiple tools operate together using technical standards, some misinterpretation and mistake gaps are identified due to a lack of machine-to-machine (M2M) communication and users’ interpretation. This is recognized as a semantic interoperability problem. Semantic technologies, such as ontologies, have been proven to be a promising way to overcome semantic interoperability obstacles. Based on this context, this study is proposing a conceptual framework based on semantic technologies to create a solution to the horizontal and vertical integration and semantic interoperability obstacle. MANUMATE is the framework proposed, and it consists of three artifacts, 1) reference ontologies, 2) requirements, and 3) application ontology, and two processes, 1) ontology specialization and 2) information application. The MANUMATE framework is applied to two experimental case studies to validate the conceptual solution in two different applications, in the context of a long-life package for the beverages industry. These case studies help elucidate how the application of the framework could improve the information and knowledge exchange by providing a standard way to represent information among different stakeholders in the productive process. A discussion about the results is presented, revealing the benefits and limitations of the solution.

Proactive hybrid learning and optimisation in self-adaptive systems: The swarm-fleet infrastructure scenario

Smart and adaptive Systems, such as self-adaptive and self-organising (SASO) systems, typically consist of a large set of highly autonomous and heterogeneous subsystems that are able to adapt their behaviour to the requirements of ever-changing, dynamic environments. Their successful operation is based on appropriate modelling of the internal and external conditions. The control problem for establishing a near-to-optimal coordinated behaviour of systems with multiple, potentially conflicting objectives is either approached in a distributed (i.e., fully autonomous by the autonomous subsystems) or in a centralised way (i.e. one instance controlling the optimisation and planning process). In the distributed approach, selfish behaviour and being limited to local knowledge may lead to sub-optimal system behaviour, while the centralised approach ignores the autonomy and the coordination efforts of parts of the system. In this article, we present a concept for a hybrid (i.e., integrating a central optimisation with a distributed decision-making process) system management that combines local reinforcement learning and self-awareness mechanisms of fully autonomous subsystems with external system-wide planning and optimisation of adaptation freedom that steers the behaviour dynamically by issuing plans and guidelines augmented with incentivisation schemes. This work addresses the inherent uncertainty of the dynamic system behaviour, the local autonomous and context-aware learning of subsystems, and proactive control based on adaptiveness. We provide the ‘swarm-fleet infrastructure’ – a self-organised taxi service established by autonomous, privately-owned cars – as a testbed for structured comparison of systems. The ‘swarm-fleet infrastructure’ supports the advantages of a proactive hybrid self-adaptive and self-organising system operation. Further, we provide a system model to combine the system-wide optimisation while ensuring local decision-making through reinforcement learning for individualised configurations. • Our concept combines local reinforcement learning and system-wide planning • A mechanism for steering the local behaviour through degrees of freedom • The mechanism combines global plans and guidelines with incentivisation schemes • The ‘swarm-fleet infrastructure’ testbed - a self-organised taxi service scenario

Technology Focus: Cementing and Zonal Isolation (May 2022)

Over the past year, the number of cementing and zonal-isolation papers was much lower than usual because of the cancellation and postponement of several conferences. The overall impact, however, was mitigated by the transformation of several conferences to online events. I attended one virtual event and was pleasantly surprised with the value of the technical discussions that immediately followed each paper presentation and the willingness of authors to follow up on discussions after the conference. The development of new technology can take a significant amount of time and resources, and full worldwide introduction may take many years, giving rise to a commercial risk for the technology developer. One paper published last year described the first case history of self-healing cement technology in one geographical area more than 10 years after the publication of the first field application of the technology. In the October 2021 issue of JPT, SPE President Kamel Ben-Naceur discussed the importance of collaboration in solving technical problems quickly and efficiently. Three of the papers selected in this section are from collaborations between multiple companies pooling their expertise and resources to develop solutions for complex problems that could not be addressed efficiently by individual companies. Papers SPE 204015 and SPE 208782 issue from consortia whose goals are to improve the efficiency and reliability of plugging-and-abandonment operations. Paper SPE 204103 is a jointly funded collaboration evaluating CO2-resistant cement with a new experimental methodology to accelerate chemical interactions between the cement and CO2, thus shortening the validation time. The commercial risks associated with the incremental developments of existing technology can be acceptable more readily to individual companies, although this does not mean that efficient internal teamwork is not required. The three other papers selected are all supported and authored by individual companies, but each is a collaboration between multiple people with different expertise—experimental and poromechanical modeling expertise in paper SPE 206144, chemical synthesis and microfluidics expertise in paper SPE 207472, and cement chemistry and experimental expertise in paper SPE 208679. Recommended additional reading at OnePetro: www.onepetro.org. SPE 204103 Evaluation of New Innovative Cement Blend for Enhanced CO2 and H2S Resistance by Gunnar Lende, Halliburton, et al. SPE 206144 Advanced Cement Mechanical Integrity for Thermal Wells by Manh-Huyen Vu, Curistec, et al. SPE 207472 Associative Microgels, New Self-Adaptive Systems To Control Fluid Loss in Well Cementing by Arnaud Cadix, Solvay, et al.

Real-Time Adaptive Modulation Schemes for Underwater Acoustic OFDM Communication.

Adaptive modulation received significant attention for underwater acoustic (UA) communication systems with the aim of increasing the system efficiency. It is challenging to attain a high data rate in UA communication, as UA channels vary fast, along with the environmental factors. For a time-varying UA channel, a self-adaptive system is an attractive option, which can choose the best method according to the channel condition to guarantee the continuous connectivity and high performance constantly. A real-time orthogonal frequency-division multiplexing (OFDM)-based adaptive UA communication system is presented in this paper, employing the National Instruments (NI) LabVIEW software and NI CompactDAQ device. In this paper, the received SNR is considered as a performance metric to select the transmission parameters, which are sent back to the transmitter for data transmission. In this research, a UA OFDM communication system is developed, employing adaptive modulation schemes for a nonstationary UA environment which allows to select subcarriers, modulation size, and allocate power adaptively to enhance the reliability of communication, guarantee continuous connectivity, and boost data rate. The recent UA communication experiments carried out in the Canning River, Western Australia, verify the performance of the proposed adaptive UA OFDM system, and the experimental results confirm the superiority of the proposed adaptive scheme.

Reducing large adaptation spaces in self-adaptive systems using classical machine learning

Modern software systems often have to cope with uncertain operation conditions, such as changing workloads or fluctuating interference in a wireless network. To ensure that these systems meet their goals these uncertainties have to be mitigated. One approach to realize this is self-adaptation that equips a system with a feedback loop. The feedback loop implements four core functions – monitor, analyze, plan, and execute – that share knowledge in the form of runtime models. For systems with a large number of adaptation options, i.e., large adaptation spaces, deciding which option to select for adaptation may be time consuming or even infeasible within the available time window to make an adaptation decision. This is particularly the case when rigorous analysis techniques are used to select adaptation options, such as formal verification at runtime, which is widely adopted. One technique to deal with the analysis of a large number of adaptation options is reducing the adaptation space using machine learning. State of the art has showed the effectiveness of this technique, yet, a systematic solution that is able to handle different types of goals is lacking. In this paper, we present ML2ASR+, short for Machine Learning to Adaptation Space Reduction Plus. Central to ML2ASR+ is a configurable machine learning pipeline that supports effective analysis of large adaptation spaces for threshold, optimization, and setpoint goals. We evaluate ML2ASR+ for two applications with different sizes of adaptation spaces: an Internet-of-Things application and a service-based system. The results demonstrate that ML2ASR+ can be applied to deal with different types of goals and is able to reduce the adaptation space and hence the time to make adaptation decisions with over 90%, with negligible effect on the realization of the adaptation goals.

MROS: runtime adaptation for robot control architectures

Known attempts to build autonomous robots rely on complex control architectures, usually implemented with the Robot Operating System (ROS). Runtime adaptation is needed in these systems, to cope with component failures and with contingencies arising from dynamic environments – otherwise these affect the reliability and quality of the mission execution. Existing proposals on how to build self-adaptive systems in robotics usually require a major re-design of the control architecture and rely on complex tools unfamiliar to the robotics community. Moreover, they are hard to reuse across applications. This paper presents MROS: a model-based framework for runtime adaptation of robot control architectures based on ROS. MROS uses a combination of domain-specific languages to model architectural variants and capture mission quality concerns, and an ontology-based implementation of the MAPE-K and meta-control visions for runtime adaptation. The experiment results obtained applying MROS in two realistic ROS-based robotic demonstrators show the benefits of our approach in terms of the quality of the mission execution, and MROS's extensibility and reusability across robotic applications.

Realizing self-adaptive systems via online reinforcement learning and feature-model-guided exploration

AbstractA self-adaptive system can automatically maintain its quality requirements in the presence of dynamic environment changes. Developing a self-adaptive system may be difficult due to design time uncertainty; e.g., anticipating all potential environment changes at design time is in most cases infeasible. To realize self-adaptive systems in the presence of design time uncertainty, online machine learning, i.e., machine learning at runtime, is increasingly used. In particular, online reinforcement learning is proposed, which learns suitable adaptation actions through interactions with the environment at runtime. To learn about its environment, online reinforcement learning has to select actions that were not selected before, which is known as exploration. How exploration happens impacts the performance of the learning process. We focus on two problems related to how adaptation actions are explored. First, existing solutions randomly explore adaptation actions and thus may exhibit slow learning if there are many possible adaptation actions. Second, they are unaware of system evolution, and thus may explore new adaptation actions introduced during evolution rather late. We propose novel exploration strategies that use feature models (from software product line engineering) to guide exploration in the presence of many adaptation actions and system evolution. Experimental results for two realistic self-adaptive systems indicate an average speed-up of the learning process of 33.7% in the presence of many adaptation actions, and of 50.6% in the presence of evolution.

Programming language implementations for context-oriented self-adaptive systems

The context-oriented programming paradigm is designed to enable self-adaptation, or dynamic behavior modification of software systems, in response to changes in their surrounding environment. Contextoriented programming offers an adaptation model, from a programming language perspective, that maintains a clean modularisation between the application and adaptation logic, as well as between the components providing adaptations.

Decentralized Self-adaptation in the Presence of Partial Knowledge with Reduced Coordination Overhead

Decentralized self-adaptive systems consist of multiple control loops that adapt some local and system-level global goals of each locally managed system or component in a decentralized setting. As each component works together in a decentralized environment, a control loop cannot take adaptation decisions independently. Therefore, all the control loops need to exchange their adaptation decisions to infer a global knowledge about the system. Decentralized self-adaptation approaches in the literature uses the global knowledge to take decisions that optimize both local and global goals. However, coordinating in such an unbounded manner impairs scalability. This paper proposes a decentralized self-adaptation technique using reinforcement learning that incorporates partial knowledge in order to reduce coordination overhead. The Q-learning algorithm based on Interaction Driven Markov Games is utilized to take adaptation decisions as it enables coordination only when it is beneficial. Rather than using unbounded number of peers, the adaptation control loop coordinates with a single peer control loop. The proposed approach was evaluated on a service-based Tele Assistance System. It was compared to random, independent and multiagent learners that assume global knowledge. It was observed that, in all cases, the proposed approach conformed to both local and global goals while maintaining comparatively lower coordination overhead.

Guest Editors’ Introduction: Special Issue on Autonomous Systems Design

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fueled by the</b> progress of artificial intelligence, autonomous systems become more and more integral parts of many Internet-of-Things (IoT) and cyber–physical systems (CPSs) applications, such as automated driving, robotics, avionics, and industrial automation. Autonomous systems are self-governed and self-adaptive systems that are designed to operate in an open and evolving environment that has not been completely defined at design time. However, this does not come for free. The advance from established systems technology to autonomous systems is not an issue of developing and introducing new functions in existing design processes. We firmly believe, and will elaborate, that it will have a deep impact on engineering, on the way we design, verify, test, and operate systems. We must find ways to keep high engineering standards, such as dependability and controllability, when allowing the system to change its behavior in the field. In other words, the future of autonomous systems is not only an issue of systems intelligence, but depends on our capability to design them responsibly.

Meaningful Update and Repair of Markov Decision Processes for Self-Adaptive Systems

Meaningful Update and Repair of Markov Decision Processes for Self-Adaptive Systems