The increasing complexity and variety of IoT systems require the integration of multiple services to meet a wide range of user needs. This paper addresses the challenge of multi-objective IoT service composition with replication problem by considering multiple Quality of Service (QoS) metrics such as response time and the number of selected service instances. We propose a new Memetic NSGA-II algorithm with Bottleneck-driven Local Search (MNSGA2-BLS) to effectively solve this difficult problem. By integrating genetic operations, clustering-based refinement, and a bottleneck-driven Estimation of Distribution Algorithm for local search, MNSGA2-BLS identifies and optimizes critical service instances causing QoS bottlenecks. This method leverages Pareto-optimal solutions to guide the local search refinement process, enhancing convergence and solution quality. Experimental results across various benchmark cases demonstrate that MNSGA2-BLS can outperform NSGA-II and several state-of-the-art algorithms, achieving superior results in both the hyper-volume and inverse generational distance metrics. This highlights the potential of MNSGA2-BLS to provide efficient and effective composite IoT services while addressing trade-offs between competing QoS objectives.

Microservice architecture has gained popularity in application development due to it can develop, deploy and maintain lightweight microservices independently. Microservice application deployment in clouds has emerged as a challenging problem for cloud providers. This is because of the fine-grained resource requirements and the dynamic arrival of microservice applications in cloud data centers. Meanwhile, a cloud provider faces multiple different but related microservices application deployment tasks. For example, a cloud provider focuses on reducing the energy consumption in a cloud data center for deploying microservice applications that require huge computational resources, while focusing on reducing the communication overhead for those communication-intensive microservice applications. These different optimization objectives give rise to different microservices application deployment tasks. However, existing microservice deployment approaches solve each task separately. In this article, we propose a novel multi-tasking genetic programming approach, named MGPBS, to not only solve multiple microservice application deployment tasks simultaneously but also utilize useful knowledge of solving one task to improve the performance of solving others. Simulation experiments based on real-world traces are conducted to evaluate MGPBS. The results show that MGPBS outperforms several state-of-the-art microservice application deployment approaches.

Budget-constrained IoT service composition seeks composite services that optimize Quality of Service (QoS), such as response time, while operating within a fixed budget, which is a challenge of practical importance since real-world IoT systems often face strict financial limits. Traditional constraint-handling techniques, such as penalty methods, and random repair operators, often struggle to effectively balance budget feasibility and response time optimization. To address this gap, we propose Cost-Aware Repair-based Genetic Optimization (CARGO-IoT), which combines priority-based selection of infeasible solutions with a two-stage repair strategy. CARGO-IoT focuses on repairing recent solutions with smaller violations and faster response times, using a replace-based method guided by learned probabilities from past solutions and a reduce-based method that eliminates redundant services to meet budget limits. Experiments demonstrate that our approach significantly outperforms state-of-the-art methods in this field, achieving lower response time while maintaining budget feasibility.

The validation and verification of real-time embedded systems (RTES) in aviation demand rigorous testing frameworks capable of addressing safety, performance, and reliability under diverse operational conditions. The intricacy of dynamic operating contexts and the requirement for a thorough analysis of edge situations are frequently challenging for current testing methodologies, such as manual testing, static analysis, and conventional Hardware-in-the-Loop (HiL) simulations. To address these limitations, there is a growing need for more advanced testing techniques to push the boundaries of HiL simulation and ensure the highest levels of system integrity. This paper explores the integration of scenario-based testing (SBT) and hardware-in-the-loop simulation using the Operational Domain Modelling and Environment (ODME) tool and dSPACE's suite of testing tools, including SCALEXIO, ControlDesk, AutomationDesk, and ControlDesk. Scenarios were modeled in detail in ODME, covering a whole range of operational contexts and edge cases. Executing the scenarios on SCALEXIO through the Automation Desk enabled precise simulation and output delivery to the system under test (SUT). ControlDesk and the SUT ensured real-time monitoring and validation to maximize assessment of system behavior. The results show that this integrated approach can potentially enhance testing automation and efficiency while providing a powerful platform for improving RTES testing processes in aviation.

Safety-critical domains require a rigorous assessment before deployment. AI further exacerbates the issue. Several organizations are currently working on recommended practices to ensure the development and safety of AI-based systems. These practices often recommend identifying the high-level functioning of the system first, e.g., in the form of a Concept of Operations (ConOps), and then deriving certain artifacts. These artifacts, such as the Operational Design Domain and scenarios are used to specify data sets. In the computational space, the Operational Domain Model (ODM) can be represented using XML. The ODM is a single point of knowledge from which all subsequent specifications are generated, and, thus, must be designed carefully by a domain expert. For complex systems, the process can be complicated, manually intensive, and prone to errors. An automation of this step would greatly aid to achieve consistency and correctness of the ODM. This paper leverages the use of Large Language Models (LLMs) to transform the ConOps defined in natural language into an ODM representation in the form of XML. The later will capture all the critical elements of an ODM and will also be validated using the LLMs process, thereby helping to achieve traceability with the operational requirements in ConOps. Automated ODM generation significantly reduces the effort required to generate datasets for AI systems, ensuring consistency and correctness. This would also amplify the potential for automating the entire process, from the ConOps to systematic data generation. Properties like traceability will significantly enhance the provision of safety guarantees for AI systems.

This study proposes a new approach to creating a virtual flight test environment by integrating a digital twin of an iron bird test rig with an engineering flight simulator. Generating digital twins for test rigs in aviation is a rare and innovative approach. The engineering flight simulator used in this study incorporates real hands-on throttle and stick to provide pilots with a realistic experience. A validated iron bird of a trainer aircraft, a fully equipped test rig encompassing hydraulic, mechanical, flight control, and landing gear systems, was used as the basis for generating its digital twin. This digital twin was then used to update the mathematical model of an actuator, which is a critical component of the flight model operating within the engineering flight simulator. This method produced a highly accurate simulation of flight characteristics for the selected case, and the Flight Simulation Training Device Guideline of the European Union Aviation Safety Agency has been used to ensure the accuracy of this flight model. To demonstrate this concept, a real-world certification test scenario was selected, and the results of the simulator flight tests were compared with the real flight tests. Finally, the study presents a preliminary roadmap for implementing flight test concepts in virtual environments, emphasizing their potential to revolutionize the certification process.

Loosely coupled microservices have emerged as a new paradigm for efficiently deploying applications in clouds. However, dynamic resource allocation in clouds introduces significant challenges to microservice application deployment. On the one hand, frequent invocations between microservices may lead to substantial communication overhead if microservices are not allocated properly. On the other hand, the increasing number of microservices in modern applications makes it very challenging to minimize the energy consumption of a cloud data center as it introduces a bi-level optimization problem with an extremely large search space. In this paper, we propose a new communication-aware and energy-efficient genetic programming based method that automatically learns heuristics for dynamic resource allocation to jointly minimize the communication overhead and the energy consumption. Comprehensive experiments using real-world datasets show that our proposed method can evolve effective heuristics that noticeably outperform existing approaches for dynamic microservice deployment in clouds.

Dynamic workflow scheduling (DWS) is a challenging and important optimization problem in cloud computing, aiming to execute multiple heterogeneous workflows on dynamically leased virtual machine resources to satisfy user-defined Quality of Service requirements. For the popular deadline-constrained DWS in cloud problem, a virtual machine selection rule (VMSR) and a task selection rule (TSR) need to be designed simultaneously to minimize the rental fee and deadline violation penalty. For this purpose, Dual-Tree Genetic Programming (DTGP) has been previously developed to automatically evolve effective VMSRs and TSRs. However, existing DTGP approaches assume that VMSR and TSR, as well as terminals used by VMSRs and TSRs are equally important and evolve both VMSRs and TSRs in a opaque manner, i.e., without using any knowledge about different impacts of trees and terminals. Several recent studies clearly indicate that different trees or terminals have varied performance impacts, making it critical to develop adaptive mutation mechanisms for effective DTGP. Driven by this motivation, this article proposes two new levels of adaptive mutation mechanisms, contributing to the development of a new DTGP algorithm, which features the use of three new probability vectors for adaptive tree selection of VMSR and TSR at the first level and adaptive terminal selection at the second level while mutating any existing dual-tree individuals. Extensive experimental results demonstrate that the proposed two adaptive mechanisms can improve the effectiveness of DTGP compared to four baseline algorithms.

Monitoring of internal exposure to short-lived alpha-emitting radionuclides such as actinium-225 (225Ac), which are becoming increasingly important in nuclear medicine, plays an important role in the radiation protection of occupationally exposed persons. After having tested gamma spectrometry, liquid scintillation counting and alpha spectrometry for monitoring of internal exposure, the focus of the present study was on solid phase extraction of 225Ac from urine in combination with alpha spectrometry. The development of the method was based on recent findings from the literature on this topic. The method was used in a pilot phase to monitor internal exposure of four workers who were directly or indirectly involved in the manufacture and/or use of 225Ac. The monitoring protocol allowed a relatively short 24-hour urine sample analysis with excellent recovery of the internal standard, but it did not allow for a detection limit of less than 1 mBq nor a sufficient yield of 225Ac. Based on these results it is concluded that an in vitro excretion analysis alone is not appropriate for monitoring internal exposure to 225Ac. Instead, different radiation monitoring techniques have to be combined to ensure the radiation protection of employees.

The use of data-driven modeling for digital twins (DTs) is growing in popularity. However, many models do not make it into production. Those that do quickly become outdated. Streamlined model update is still a major challenge. There is a need to establish methods and techniques for managing data-driven digital twins throughout their entire life cycle. Machine learning operations (MLOps) recently emerged as an effective means to foster the integration of Machine Learning (ML) models and their operational workflows. In this paper, we exploit MLOps for development and operation of data-driven digital twins. To validate our approach, a case study is conducted in which an ML model of a physical test rig is trained in accordance with the MLOps principles. The work aims to demonstrate how MLOps practices can contribute to overcoming issues related to scalability, accuracy, and adaptability in the context of digital twin training.

Microservice deployment in cloud computing is a challenging combinatorial optimization problem due to the complex dependencies among microservices and the intricate trade-offs among different QoS requirements, e.g., minimizing Energy Consumption (EC) vs. minimizing Communication Overhead (CO). Recently, some hyper-heuristics methods, particularly Genetic Programming Hyper-Heuristics (GPHH), have been proposed to automatically generate heuristics for solving dynamic microservice deployment problems. Meanwhile, Large Language Models (LLMs) are becoming popular for solving various domain-specific problems thanks to their strong ability to learn problem-related knowledge. However, hybridizing GPHH with LLM by combining their abilities in solving complex optimization problems remains unexplored. In this paper, we propose an LLM-enhanced Genetic Programming Hyper-Heuristic (LLM-GPHH) algorithm to evolve heuristics for the dynamic deployment of applications composed of microservices, to jointly optimize EC and CO. Our experiments on real-world datasets demonstrate the effectiveness of the newly proposed LLM-GPHH.

The rapid proliferation of Application Programming Interfaces (APIs) enhances data exchange. Still, it introduces significant privacy and security risks, especially in the Internet of Things (IoT), where APIs often lack mechanisms to manage privacy and security, leading to vulnerabilities. Hippocratic Databases (HDBs) provide mechanisms, e.g., purpose-based access, to control database use. However, to effectively manage data access to the HDB, proper API design is crucial. This paper proposes a conceptual framework for a Hippocratic API (HAPI), revising traditional API design aiming to protect data subjects' rights and enhance security. By embedding data protection and ethical standards into API operations, HAPIs rectify inadequacies in consent mechanisms and mitigate privacy risks. We identify non-functional requirements, design objectives, and techniques through extensive research of recent literature, informed by the ethical principles of the GDPR, ISO/IEC 27001, and HDBs. We present our findings by knowledge graphs, providing a comprehensive conceptual view of the relevant design knowledge.

Genetic Programming Hyper-heuristics (GPHH) have been successfully used to evolve scheduling rules for Dynamic Workflow Scheduling (DWS) as well as other challenging combinatorial optimization problems. The method of sampling training instances has a significant impact on the generalization ability of GPHH, yet they are rarely addressed in existing research. This article aims to fill this gap by proposing a GPHH algorithm with a sampling strategy to thoroughly investigate the impact of six instance sampling strategies on algorithmic generalization, including one rotation strategy, three mini-batch strategies, and two hybrid strategies. Experiments across four scenarios with varying settings reveal that: (1) mini-batch with random sampling can outperform rotation in generalizing to unseen workflow scheduling problems under the same computational cost; (2) employing a hybrid strategy that combines rotation and mini-batch further enhances the generalization ability of GPHH; and (3) mini-batch and hybrid strategies can effectively enable heuristics trained on small-scale training instances generalizing well to large-scale unseen ones. These findings highlight the potential of mini-batch strategies in GPHH, offering improved generalization performance while maintaining diversity and suggesting promising avenues for further exploration in GPHH domains.

Internet of Things Service Composition (SCIoT) aims to find the best composite IoT service to fulfil users' requirements. Given the NP-hard complexity of SCIoT, Evolutionary Computation methods, especially Estimation of Distribution Algorithms (EDAs), have received increasing attention to solve SCIoT problems. As one of the most popular EDA methods, Population-Based Incremental Learning (PBIL) has demonstrated its strong competency in optimising composed services in SCIoT. However, conventional PBIL does not explicitly utilize problem knowledge such as QoS and service replication, limiting its effectiveness for IoT service composition. In this paper, we propose a new PBIL based approach, named Population-Based Incremental Learning to Improve Service Composition (PBILISC), to solve the SCIoT problem. Different from traditional PBIL, PBILISC seamlessly integrates PBIL with QoS-aware local search (QLS) to effectively handle replicated services in the SCIoT problem. Specifically, PBILISC evolves a series of populations of solutions jointly through PBIL and QLS. PBIL leverages a probability distribution for population updates, while QLS focuses on improving the best evolved solution by searching promising neighboring solutions under the guidance of QoS. Experimental results show that PBILISC can outperform PBIL and several state-of-the-art methods on multiple benchmark SCIoT problems.

Scenario-based approaches have recently been widely adopted in the automotive and aviation industries.They aim to define and manage the test cases in a better way, thereby significantly reducing the risks and defining the safety argument for the system.To improve and complement the safety conditions, the Operational Design Domain (OOD) defines the operational boundaries of a driving automation system to specify the scope of the safety case, represented by human and machinereadable languages.Many approaches and standards that involve the essential modelling and safety concepts have been introduced to represent scenario-based simulation.Some parts of these approaches were implemented as graphical tools, such as System Entity Structure (SES) and Pruned Entity Structure (PES) tools, which are based on ontology and its derived structure.However, an extensive implementation covering scenario modelling and management, ODD, and assessment is still missing.This paper proposes an adapted scenariobased approach based on related research, and implements it in a robust GUI tool called Operational Domain Modeling Environment (ODME).ODME is progressing to fill the gap and address the aforementioned limitations by covering the modelling functions and safety approaches in one comprehensive environment with a wide range of capabilities and features.

Containers are quickly gaining popularity in cloud computing environments due to their scalable and lightweight characteristics. However, the problem of Resource Allocation in Container-based clouds (RAC) is much more challenging than the Virtual Machines (VMs)based clouds because RAC includes two levels of allocation problems: allocating containers to VMs and allocating VMs to Physical Machine (PMs). In this paper, we proposed a novel Group Genetic Algorithm (GGA) with energy-aware crossover, Best-Fit-Decreasing Insert (BFDI), and Local Search based Unpack (LSU) operator to solve RAC problems. Meanwhile, we apply an energy model with heterogeneous PMs that accurately captures the energy consumption of cloud data centers. Compared to state-of-the-art methods, experiments show that our method can significantly reduce the energy consumption on a wide range of test datasets.

The domain of beyond-the-visual-line-of-sight (BVLOS) flights of unmanned aerial vehicles (UAVs) has unique navigational challenges such as the reliable estimation, evaluation and mitigation of the risk of the associated flight paths. To tackle these challenges domain data from heterogeneous sources is needed. Failing to integrate this data carefully could result in inaccurate navigation decisions, poor situational awareness, and in general unsafe flight operations. In this paper we present a conceptual model that can be used to design conceptual graph databases that integrate the information of several domains to BVLOS. Furthermore, we show that our proposed conceptual graph database schema scales well for increasing sizes of stored domain and application data and permits constant query execution times for important recurring queries.

Web service composition composes existing Web services to accommodate users' requests for required functionalities with the best possible quality of services (QoS). Due to the computational complexity of this problem, evolutionary computation (EC) techniques have been employed to efficiently find composite services with near-optimal functional quality (i.e., quality of semantic matchmaking, QoSM for short) or nonfunctional quality (i.e., QoS) for each composition request individually. With a rapid increase in composition requests from a growing number of users, solving one composition request at a time can hardly meet the efficiency target anymore. Driven by the idea that the solutions obtained from solving one request can be highly useful for tackling other related requests, multitasking service composition approaches have been proposed to efficiently deal with multiple composition requests concurrently. However, existing attempts have not been effective in learning and sharing knowledge among solutions for multiple requests. In this article, we model the problem of collectively handling multiple service composition requests as a new multitasking service composition problem and propose a new permutation-based multifactorial evolutionary algorithm based on an estimation of distribution algorithm (EDA), named PMFEA-EDA, to effectively and efficiently solve this problem. In particular, we introduce a novel method for effective knowledge sharing across different service composition requests. For that, we develop a new sampling mechanism to increase the chance of identifying high-quality service compositions in both the single-tasking and multitasking contexts. Our experiment shows that our proposed approach, PMFEA-EDA, takes much less time than existing approaches that process each service request separately, and also outperforms them in terms of both QoSM and QoS.

Microservice is a new architecture for application development that makes applications more flexible to deploy, extend and update compared to monolithic architectures. As container-based clouds rapidly gained popularity in recent years, more microservices are deployed in containers and composed of complex and elaborated applications for users. The challenges of microservices deployment in a container-based clouds arise from two-level resource allocations to not only minimize the overall energy consumption but also to reduce the communication data volume between microservices in physical networks to improve application performance. However, there is still a lack of research that considers these two important challenges jointly during microservice composition and resource allocation. Motivated by this, in this work, we propose a genetic algorithm-based algorithm, namely EC-GGA, to not only minimize the energy consumption in cloud data centers but also minimize the communication data volume of applications. We compare EC-GGA with several state-of-the-art algorithms to demonstrate the effectiveness of our proposed algorithm.