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Cloud manufacturing (CMfg) emerges as a promising manufacturing paradigm, where service composition (SC) is a critical process concentrating on matching tasks and services. Existing studies usually ignore the dynamic nature of the CMfg environment, where task information is not always known before. Moreover, CMfg services are re-entrant, i.e. after being occupied for a period of service time, these services re-enter the CMfg platform (i.e. be available again). Re-entrant services significantly complicate CMfg platform revenue management. In this regard, we study the dynamic SC problem of CMfg (CMfg-DSC) incorporating re-entrant services within an online setting for the first time. CMfg-DSC is reformulated as an online packing problem. If a task is accepted, each requested service will be occupied until service time terminates. We propose online policies with performance guarantees, namely, static online packing policy (Static), opportunity-cost-based policy (Oppo), and dual-based policy with/without known distribution (Dual-k & Dual-u). Experiment results show that (1) Static is applicable for most cases; (2) Oppo has the potential for decent performance but at the cost of time; (3) Dual-u is reliable when only past observations are available; (4) Dual-k performs well given abundant service provision, but its performance would deteriorate if we lower the reward-cost threshold. [ABSTRACT FROM AUTHOR]

One of the most well-known names that has recently attained new heights and set a standard is Internet of Things (IoT). IoT aims to connect all physical devices in such a way that they are subject to human control over the Internet.The emergence of IoT in almost all the industries has redesigned them including smart agriculture. In today's world, the growth in agriculture sector is rapid, smarter and precise than ever. In case of IoT, the objects are termed as services, sometimes with similar functionalities but distinct quality of service parameters. As the user's requirements are complex, a single service cannot fulfil them efficiently. So, service composition is the solution. These services known as atomic services, are represented as workflow, with each of them having distinct candidate composite services. Fulfilling these Quality of Service (QoS) constraints makes it a NP-hard problem which can't be solved using traditional approaches. Hence, comes the concept of evolutionary approaches. In this paper one of the evolutionary approach- NSGA-II is used to optimize the production of apple by composing the various services, taking into account the cost and time as multi-objective problem to be solved. This is for the very first time that QoS aware service composition problem has been optimized in smart agriculture as found in the literature. Results are further compared with multi-objective genetic algorithm (MOGA) and it has been found that NSGA-II outperforms MOGA by generating well-proportioned pareto optimal solutions. [ABSTRACT FROM AUTHOR]

In recent years, the transformative evolution of cloud computing has reshaped organizational practices by enabling the outsourcing of web service applications. This shift has led to the emergence of the cloud environment, characterized by the involvement of Cloud Service Providers (CSPs) and intelligent applications. Cloud Service Composition (CSC) has become pivotal in this context, playing a crucial role in enhancing efficiency, Quality of Service (QoS), and customer satisfaction through the aggregation of diverse Cloud Services (CSs) to create composite services. However, the vast array of available CSs presents a challenge in efficiently addressing specified QoS requirements, turning CSC into a recognized NP-hard problem. Existing solutions, often involving third-party brokers, struggle with scalability in large-scale systems and overlook crucial security concerns. To address these limitations, we propose the Hierarchical Service Composition (HSC) approach, leveraging blockchain and federated learning to minimize computational complexity. The integration of Blockchain-enabled Federated Learning (BFL) facilitates machine learning model training with decentralized data, ensuring practicality and fairness. HSC comprises an initialization phase and two selection layers. The first selection layer enables each CSP to efficiently select services using a pre-trained model, while the second selection layer employs a blockchain-based QoS-aware mechanism for the final composition result, addressing privacy concerns. HSC introduces a novel framework, collaborative service selection methods, and a smart selection algorithm, demonstrating remarkable composition efficiency in extensive simulations compared to the baseline approach. [ABSTRACT FROM AUTHOR]

Abstract In recent years, the transformative evolution of cloud computing has reshaped organizational practices by enabling the outsourcing of web service applications. This shift has led to the emergence of the cloud environment, characterized by the involvement of Cloud Service Providers (CSPs) and intelligent applications. Cloud Service Composition (CSC) has become pivotal in this context, playing a crucial role in enhancing efficiency, Quality of Service (QoS), and customer satisfaction through the aggregation of diverse Cloud Services (CSs) to create composite services. However, the vast array of available CSs presents a challenge in efficiently addressing specified QoS requirements, turning CSC into a recognized NP-hard problem. Existing solutions, often involving third-party brokers, struggle with scalability in large-scale systems and overlook crucial security concerns. To address these limitations, we propose the Hierarchical Service Composition (HSC) approach, leveraging blockchain and federated learning to minimize computational complexity. The integration of Blockchain-enabled Federated Learning (BFL) facilitates machine learning model training with decentralized data, ensuring practicality and fairness. HSC comprises an initialization phase and two selection layers. The first selection layer enables each CSP to efficiently select services using a pre-trained model, while the second selection layer employs a blockchain-based QoS-aware mechanism for the final composition result, addressing privacy concerns. HSC introduces a novel framework, collaborative service selection methods, and a smart selection algorithm, demonstrating remarkable composition efficiency in extensive simulations compared to the baseline approach.

Nowadays, digital technologies are being used to transform the agriculture sector into a more effective and environmentally friendly one by including technologies such as the Internet of Things (IoT), cloud computing, machine learning, and artificial intelligence (AI). These techniques are used to improve sustainability through more productive farming practices. Internet of Things is one of the most revolutionary techniques to provide improved agricultural services by efficiently managing and analyzing related processes. Traditional methods of a few agricultural services cannot satisfy the intricate needs of a growing population. Hence, a combination of multiple services or service composition is needed to meet user demand. This study addresses this problem by considering various agricultural services and integrating them into a composite service. The proposed work is divided into two phases. To ascertain the non-linear relationship between the cost and time of different services, Lagrange's interpolation model has been employed in the first phase. Subsequently, in the second phase, the Non-dominated Sorting Genetic Algorithm (NSGA-II), a multi-objective evolutionary algorithm, has been utilized to optimize the time and cost of services. The efficacy of the work has been evaluated by analyzing the results of the proposed method using the same multi-objective evolutionary technique, which has a linear relationship between the cost and time of various services. [ABSTRACT FROM AUTHOR]

Summary: Cloud computing is quickly becoming a common commercial model for software delivery and services, enabling companies to save maintenance, infrastructure, and labor expenses. Also, Internet of Things (IoT) apps are designed to ease developers' and users' access to networks of smart services, devices, and data. Although cloud services give nearly infinite resources, their reach is constrained. Designing coherent and organized apps is made possible by integrating the cloud and IoT. Expanding facilities by combining services is a critical component of this technology. Various services may be presented in this environment based on the user's demands. Considering their Quality of Service (QoS) attributes, discovering the appropriate available atomic services to construct the needed composite service with their collaboration in an orchestration model is an NP‐hard issue. This article suggests a service composition method using Grey Wolf Optimization (GWO) and MapReduce framework to compose services with optimized QoS. The simulation outcomes illustrate cost, availability, response time, and energy‐saving improvements through the suggested approach. Comparing the suggested technique to three baseline algorithms, the average gain is a 40% improvement in energy savings, a 14% decrease in response time, an 11% increase in availability, and a 24% drop in cost. [ABSTRACT FROM AUTHOR]

Service composition is widely employed in cloud manufacturing. Due to the abundance of similar cloud manufacturing services, the search space for optimizing service composition tends to be expansive. Existing optimization models primarily focus on QoS (quality of service) while often neglecting QoC (quality of collaboration). Furthermore, there remains scope for improving the quality and stability of service composition optimization. Therefore, this paper proposes a two-phase method for optimizing service composition in cloud manufacturing. In the first phase, we introduce a service cluster-oriented service response framework, efficiently generating the candidate response service set to reduce solution search space. In the second phase, we construct an optimization model that integrates QoS and QoC. Subsequently, we devise an artificial bee colony (ABC) algorithm incorporating a multi-search strategy island model to optimize cloud manufacturing service composition. Experimental results demonstrate that the introduction of service clusters enhances search efficiency, with the proposed method outperforming compared ABC algorithms and other swarm intelligence algorithms in optimization quality and stability. [ABSTRACT FROM AUTHOR]

In the cloud system, service providers supply a pool of resources in the form of a web service and the services are merged to provide the required composite services. Composing a quality-of-service aware web service is like the knapsack problem and this problem is NP-hard. Different artificial intelligence and heuristic methods have been used to achieve optimal or near-optimal composite services. In this paper, the Ant Lion optimization algorithm was modified and discretized to choose the appropriate web services from the existing services and to provide the optimal composite services. The QWS dataset contains a collection of 2507 real-world web services which are used to evaluate the proposed method. In this study, response time parameters, availability, throughput, success capability, reliability, and latency were used as the web service quality metrics. The results of the conducted experiments confirm that the provided composite service by the proposed method has considerably higher quality than the other related algorithms. Hence, the proposed method can be used in the cloud resource discovery layer. [ABSTRACT FROM AUTHOR]

In today’s smart environments, the serviceli-zation of various resources has produced a tremendous number of IoT- and cloud-based smart services. Thanks to the pivotal role of pillar paradigms, such as edge/cloud computing, Internet of Things, and business process management, it is now possible to combine and translate these service-like resources into configurable workflows, to cope with users’ complex needs. Examples include treatment workflows in smart healthcare, delivery plans in drone-based missions, transportation plans in smart urban networks, etc. Rather than composing atomic services to obtain these workflows, reusing existing process fragments has several advantages, mainly the fast, secure, and configurable compositions. However, reusing smart process fragments has not yet been addressed in the context of smart environments. In addition, existing solutions in smart environments suffer from the complexity (e.g., multi-modal transportation in smart mobility) and privacy issues caused by the heterogeneity (e.g., package delivery in smart economy) of aggregated services. Moreover, these services may be conflicting in specific domains (e.g. medication/treatment workflows in smart healthcare), and may affect user experience. To solve the above issues, the present paper aims to accelerate the process of generating configurable treatment workflows w.r.t. the users’ requirements and their smart environment specificity. We exploit the principles of software reuse to map each sub-request into smart process fragments, which we combine using Cocke-Kasami-Younger (CKY) method, to finally obtain the suitable workflow. This contribution is preceded by a knowledge graph modeling of smart environments in terms of available services, process fragments, as well as their dependencies. The built information network is, then, managed using a graph representation learning method, in order to facilitate its processing and composing high-quality smart services. Experimental results on a real-world dataset proved the effectiveness of our approach, compared to existing solutions. [ABSTRACT FROM AUTHOR]

In today's dynamic business landscape, organizations heavily rely on cloud computing to leverage the power of virtualization and resource sharing. Service composition plays a vital role in cloud computing, combining multiple cloud services to fulfill complex user requests. Service composition in cloud computing presents several challenges. These include service heterogeneity, dynamic service availability, QoS (Quality of Service) constraints, and scalability issues. Traditional approaches often struggle to handle these challenges efficiently, leading to suboptimal resource utilization and poor service performance. This work presents a fuzzy-based strategy for composing cloud services to overcome these obstacles. The fact that service composition is NP-hard has prompted the use of a range of metaheuristic algorithms in numerous papers. Therefore, Particle Swarm Optimization (PSO) has been applied in this paper to solve the problem. Implementing a fuzzy-based PSO for service composition requires defining the fuzzy membership functions and rules based on the specific service domain. Once the fuzzy logic components are established, they can be integrated into the PSO algorithm. The simulation results have shown the high efficiency of the proposed method in decreasing the latency, cost, and response time. [ABSTRACT FROM AUTHOR]

SUMMARY: The growing number of services processed and stored in the cloud has led to difficulties in managing and discovering the required services efficiently. Multilevel index model is an efficient method to manage and retrieve services in service repositories. When adding a new service to a multilevel index model, a key needs to be selected for the service, but existing key selection methods cannot adapt to the situation that hot services change over time. To address this problem, this article proposes an adaptive key selection method to improve the efficiency of service retrieval. However, the service addition operation of the adaptive key selection method is inefficient in the multilevel index model. For this reason, this article improves the multilevel index model by introducing local equivalence partition. This indexing model improves the service addition efficiency of the adaptive key selection method without affecting the service retrieval efficiency. It is experimentally demonstrated that the retrieval and addition efficiencies of the adaptive key selection method are close to the ideal state optimum under the multilevel index model with local equivalence partitioning. [ABSTRACT FROM AUTHOR]

Social IoT (SIoT) refers to socializing in the Internet of Things (IoT), where things generate social relationships. Due to the development of objects and issues such as delayed response, slow search, and composite service process, distributed object service discovery, selection, and composition based on the social structure have become essential challenges in the SIoT. Therefore, it is necessary to provide an efficient method for evaluating the effectiveness of service discovery in identifying suitable devices to offer requested services and the best composition strategy for combining requested services. This paper presents a new community detection algorithm that detects IoT devices with social connections in the SIoT network to facilitate service discovery and composition by reducing search space. Additionally, it introduced a new service provisioning algorithm to optimize service discovery and composition, called An Improved Genetic Algorithm based on Community Detection (IGA-CD). Its effectiveness in detected communities is better than other methods in computation modularity, execution time, and cluster assignment quality methods by determining the network’s ideal devices. Experimental results demonstrate the efficacy of the proposed algorithms, which outperform other approaches in terms of scalability, efficiency, and flexibility. The IGA-CD average execution time is 0.129 seconds, which proves its efficiency and faster composition.

In recent years, with the continuous development of serviceoriented computing technology, the industry has increasingly high quality requirements for service solving. A dynamic perceptual service composition framework is proposed for solving Web service problems. During the process, the user provides three aspects of information, including user context, user requests, and user preferences. The relevant elements in the problem are divided into multiple logical levels and processed layer by layer. The entire service process is represented through a directed graph, and specific business is represented using a vertex set. A forward step re-planning rule is established to skip business vertices. The experimental results showed that the research method had an error of only 0.071% in solving quality analysis at 20 business vertices, which was lower compared with other methods. In the multi concurrency tolerance test, the research method only experienced 5 crashes during a 500s runtime. In the analysis of packet loss rate during operation, when there was no network fluctuation, the packet loss rate of the research method fluctuated between 0.1% and 0.7%. The research method can provide service discovery results that are more in line with the actual needs and preferences, which can provide better solution results for service composition problems.

Abstract In recent years, with the rapid development of mobile communication networks, some new services such as cloud virtual reality, holographic communication, and etc. continue to emerge. Service composition has been researched in cloud computing. however, as the fast development of edge clouds, the service components can be deployed on the edge clouds to reduce the composition latency, so the more flexible and intelligent service composition algorithms are urgently need to study. Based on this, we propose a service composition strategy under the multi-cloud environment, and we propose an ant colony optimization algorithm (ACO) based on the multi-pheromone mechanism to optimize the quality of service (QoS). To avoid the occurrence of local optima, we further introduce the mutation operation of the genetic algorithm. Finally, the simulation results show that the proposed algorithm can achieve better QoS parameters such as latency and response time while ensuring the stability of services.

Today, ubiquitous computing is gaining traction as a new generation capable of addressing the vast and scalable changes in the quality and amount of data processed and used by the public. As a result, various publications have tackled the problem of service composition by adopting methodologies that take into account the ideas of intention and context, but without actually merging them as concordant and relevant aspects in the service composition process. In this context, we propose an approach for service composition, guided by the user's intention and context, which is inspired by different works that have addressed the topic of service composition by exploiting artificial intelligence (AI) planning and the concepts related to intention and context. The main idea behind this approach is the implementation of conceptual and architectural aspects that allow the composition of services, independently of any platform, programming language, or specific tool, while ensuring the integrity of the handled data and the quality of the offered services. In this sense, we present in this paper a method to conceive a service composition problem into an AI planning problem, which is parameterized by the user's contextual data and seeks to achieve a goal related to a fixed intention by implementing an AI planner that exploits and manipulates the functionalities offered by a genetic algorithm (GA), which has as a goal to propose solutions that solve the conceived planning problem (composite services). [ABSTRACT FROM AUTHOR]

In order to quickly find an appropriate composition of services that meet the individual user's requirements in the Internet big data, this paper proposes an improved particle swarm service composition method based on prior knowledge. This method firstly mines the service composition partial segments with certain frequencies of usage from a large number of historical service composition solutions, i.e. the service pattern. While receiving the user's service composition requirement, this method uses the service pattern matching algorithm proposed in this paper to match the corresponding service patterns as a partial solution of this composition requirement. Then the method proposes an improved particle swarm algorithm for the part that do not successfully match the corresponding service patterns. This improved particle swarm algorithm has a mechanism to escape from the local optima. Finally, the method integrates the partial solutions of the two aspects into a complete solution, i.e. a complete service composition solution. This paper compares the optimality, time complexity and convergence with other related service composition optimization algorithms through simulation experiments. According to the analysis of the experimental results, the method proposed in this paper shows good performance in three aspects: optimality, time complexity and convergence. [ABSTRACT FROM AUTHOR]

In the complex Internet of Things (IoT) environment, a plethora of IoT services with akin functions but varying qualities of service exist. To meet diverse customer needs and drive widespread application, service composition optimization becomes crucial. In the current era of rapid development in artificial intelligence, intelligent algorithms play a significant role in optimizing service composition. However, algorithms applied to IoT service composition optimization face common challenges of low search efficiency and insufficient optimization precision, including the Shuffled Frog Leaping Algorithm (SFLA) and Genetic Algorithm (GA). Therefore, this study seeks to enhance the perception of service quality in IoT service composition. It proposes an improved SFLA (ISFLA) based on the original SFLA. The algorithm integrates chaos theory and reverse learning theory for the acquisition of the initial population. It utilizes Euclidean distance to partition the population into groups and employs Gaussian mutation to optimize the optimal individual of each group. Finally, the entire population undergoes evolution through a local update method based on two strategies. Simulated experiments were conducted to search for optimal IoT service composition solutions of different scales. The results indicate that, compared to the SFLA, GA, ISFLA*, IGSFLA and SFLAGA, ISFLA achieves superior fitness values, better composition solutions, and exhibits faster convergence, higher stability, and greater overall operational efficiency.

Service composition is a core issue of cloud manufacturing (CMfg) to integrate distributed manufacturing services for customised manufacturing tasks. Existing studies focus on the quality of service (QoS) in composition by assuming that each service is independent with each other. However, the correlation between services determines whether a composition is feasible in practice and is a primary factor of its QoS. This paper considers two typical correlations, composability-oriented correlation and quality-oriented correlation. The composability-oriented correlation is modelled as a group of constraints to decide whether a solution is feasible. The influence of the quality-oriented correlation between two services on the overall QoS of a composition is quantified by a discount percentage based on their correlation degrees. A mathematical model of correlation-aware service composition is then established. To solve this problem, a many-objective memetic algorithm termed HypE-C (Hypervolume Estimation Algorithm for Multiobjective Optimisation involving Correlation) is designed. Three correlation-based local search strategies are established in the frame of HypE (Hypervolume Estimation Algorithm for Multiobjective Optimisation) to achieve better trade-off among multiple conflicting QoS criteria. Experiments demonstrate the effectiveness of the proposed algorithm HypE-C compared with five many-objective algorithms on eliminating infeasible search space and providing high QoS service composition solutions. [ABSTRACT FROM AUTHOR]

Cloud platforms rapidly develop and supply many cloud-based services with various functions but varying Quality of Service (QoS) attributes. In the context of cloud computing, a major concern is how to incorporate disparate cloud services to maximize QoS value, which is underpinned by meeting the local QoS requirements of users. To deal with multiple QoS parameters in service composition, we propose a new mechanism using the Capuchin Search Algorithm (CapSA) that provides solutions to satisfy connectivity constraints. The CapSA algorithm mimics the dynamic behavior of capuchin monkeys in the forest to solve global and local optimization problems. The CapSA was selected for its simplicity, reduced computational complexity, and exploration/exploitation balance. The approach takes the form of an optimization problem to minimize energy consumption and total cost. Compared with other approaches, the proposed algorithm improves the efficiency of cloud service composition by convergent rapidly and obtaining better compositions. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) applications have invaded several domains (supply chain, healthcare, etc.). To enhance the quality of the provided service in terms of latency, response time, etc., service providers such as Amazon, Google, and Microsoft turned to running IoT tasks near the end user by invoking the fog computing concept. Fog computing extends cloud services to the edge of the network. It provides a variety of computing resources in the form of fog nodes, which offer multiple services known as fog services. These latter are used to store and process the data generated by IoT devices. Fog services are characterized by their high reusability. It enables the construction of a composite service to provide complicated IoT tasks. In this paper, we introduce an adaptive requirements-aware approach for configuring IoT applications in fog computing. The configuration is based on a Composite Fog Service (CFS) model and is restricted by a set of constraints. The proposed approach is implemented in a Smart Car Parking (SCP) scenario. Simulation results reveal the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

As network services tend to be integrated to provide a better quality of service (QoS) to customers, correlations appear to be useful measurements to better design service compositions of an integrated network. However, as the integration intensifies, the determination of the service compositions of the network requires the ease of computational issues in this dynamic environment, which are resolved by the cloud computing platforms. The manuscript proposes an algorithm based on multi-constraint optimal path selection (MCOPS) that benefits from available notions such as QoS correlation criteria and correlation ratios, and skyline algorithm to construct a novel directed cyclic graph whose calculations are conducted on the cloud platform dynamically. Both cost and delay attributes in the construction of network service compositions for customers are included in the graph. Simulations suggest that both average calculation time and the quality of the path solution are substantially enhanced with the utilization of cloud computing in network service compositions. Consequently, a better service composition plan (SCP) is attained when a correlated structure is assumed to exist. [ABSTRACT FROM AUTHOR]

The problem of service composition is the process of assigning resources to services from a pool of available ones in the shortest possible time so that the overall quality of service is maximized. This article provides solutions for the composition problem that takes into account its scalability, services' locations, and users' restrictions, which are key for the management of applications using state‐of‐the‐art technologies. The provided solutions use different techniques, including genetic algorithms and heuristics. We provide an extensive experimental evaluation, which shows the pros and cons of each of them, and allows us to characterize the preferred option for each specific problem. Since no solution dominates the others, we propose a decision tree, based on our results, to select the best composition algorithm in each situation. [ABSTRACT FROM AUTHOR]

Recently, we noticed the emergence of several data management architectures to cope with the challenges imposed by big data. Among them, data lakehouses are receiving much interest from industrial and academic fields due to their ability to hold disparate multi-structured batch and streaming data sources in a single data repository. Thus, the heterogeneous and complex aspect of the data requires a dedicated process to improve their quality and retrieve value from them. Therefore, data curation encompasses several tasks that clean and enrich data to ensure it continues to fit the user requirements. Nevertheless, most existing data curation approaches need more dynamics, flexibility, and customization in constituting the data curation pipeline to align with end user requirements that may vary according to her/his decision context. Moreover, they are dedicated to curating only a single type of structure of batch data sources (e.g., semi-structured). Considering the changing requirements of the user and the need to build a customized data curation pipeline according to the users and the data source characteristics, we propose a service-based framework for adaptive data curation in data lakehouses that encompasses five modules: data collection, data quality evaluation, data characterization, curation service composition, and data curation. The proposed framework is built upon new data characterization and evaluation modular ontology and a curation service composition approach that we detail in the following paper. The experimental findings validate the contributions' performance in terms of effectiveness and execution time. [ABSTRACT FROM AUTHOR]

Compatibility is a decisive and necessary factor which impacts the successfulness and correctness of establishing connections among services. Reliable service parameter predictions on compatibility may not only provide assurance on fulfilling workflow, but also reduce the risk of unexpected wasting on time and expenses. Recent research on service's parameter prediction focus on two aspects: Collaborative Filtering and Matrix Factorization. Both of them faces challenges from data-sparse problem, lack of solid mathematical support, and weakness on tracking dynamic services. In this paper, we propose Link-ability, a parameter to reveal the compatibility of a service. Firstly, we propose an architecture called Topology-Retrievable Service Oriented Architecture (TSOA), which is capable of collecting successful invocation records from service requesters and generate the whole topology of the service pool in a specific period of time. Secondly, we bring up the Link-ability Generation Algorithm (LGA) and Markovian mathematical strategies to generate Link-ability value. The algorithm is supported by solid mathematical proof and capable of solving data-sparse problems. Both TSOA and LGA are upgraded to adapt the circumstances of services exiting the service pool (we define it as a Drop-out). Lastly, we design and conduct two series of experiment to strengthen the reliability and correctness of Link-ability. The result shows that Link-ability considers comprehensive information of the whole topology and bring 54.18% reduction in average error rate against Drop-out scenarios. [ABSTRACT FROM AUTHOR]

In the near future, manufacturing industries will be mostly recognised with characteristics like IoT, and massive data transactions. To fulfil these characteristics, paradigms like Cloud manufacturing, Industry 4.0 and smart factory have passed their preliminary steps to become the primary inspirations. Considering the nature of Cloud manufacturing which consists of a vast number of service providers and Service demanders being introduced to the manufacturing cloud, service composition problem is introduced. However, there is a big challenge for fulfilling the dynamic behaviour of parameters which change rapidly over time in the service composition problem. This paper challenges the centralised mechanism of service composition problem and introduces a novel platform entitled Blockchain-based service composition model (Block-SC) based on the Blockchain technology. Block-SC as a novel manufacturing architecture conquers the centralised mechanism by dividing the original service composition problem into multiple sub-problems each of which contains a small fraction of the service/task pool. The capabilities of the proposed platform are remarkable from two perspectives; first, it provides an effective mechanism for collaboration of service composition service providers with a service-oriented approach and from the second perspective, the optimality of service composition problem is profoundly affected considering the dynamic behaviour of Cloud manufacturing. [ABSTRACT FROM AUTHOR]

In recent years, economic globalisation and manufacturing resource globalisation as two key factors have driven enterprises to transform their business processes to survive in competitive environments. This transformation is challenging as manufacturing enterprises should maintain their support for their customers with high-quality products, lower cost, product customisation capabilities, and quick delivery. Moreover, globalisation has resulted in geographically distributed suppliers across the globe. This challenge will turn into a major research topic when paradigms like Cloud manufacturing are introduced. Cloud manufacturing is a new paradigm which provides ubiquitous, convenient, on-demand network access to a shared pool of configurable manufacturing resources. In this paper, to achieve the ideal goal of Cloud manufacturing, the concept of supplier network logistics planning and manufacturing service composition is focused. Every production task and logistics operation would be defined as a service considering operation process chart flows. The paper has proposed a mathematical model which selects an optimal set of manufacturing and logistics service composition in order to lower operation and logistics costs in operational perspective while fulfilling a novel idea for configured cloud entropy of logistics and operation suppliers. Finally, the paper has presented a numerical example and concluded the remarks, and outlined future research. [ABSTRACT FROM AUTHOR]

In the next few years, the number of devices connected to the web will increase dramatically. This has encouraged the development of complexes and intelligent service-based applications using new paradigms and technologies such as edge computing and cloud computing. These applications are mainly built by the interaction between heterogonous web services. Answering end-users' queries demands that these services collect, integrate and communicate sensitive data, which makes the vulnerability of data a serious issue. Thereby, this issue is a critical problem in the automatic services composition while users are frequently unquieted about the security and control of their sensitive data. To address this problem, our main idea is that avoiding disclosure of sensitive information can be ensured by selecting services where their mutual trust is high while preserving a good quality of service (QoS) as possible. A new Heuristic Trust-aware Web Services Composition Approach (CWS_SMA) is proposed. CWS_SMA aims to: (i) compute an optimal trust and QoS-aware web services composition based on the mathematical coordinate system, (ii) improve the composition response time based on a set of cooperative intelligent agents. Eventually, the simulations results show the effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) represents a cutting-edge technical domain, encompassing billions of intelligent objects capable of bridging the physical and virtual worlds across various locations. IoT services are responsible for delivering essential functionalities. In this dynamic and interconnected IoT landscape, providing high-quality services is paramount to enhancing user experiences and optimizing system efficiency. Service composition techniques come into play to address user requests in IoT applications, allowing various IoT services to collaborate seamlessly. Considering the resource limitations of IoT devices, they often leverage cloud infrastructures to overcome technological constraints, benefiting from unlimited resources and capabilities. Moreover, the emergence of fog computing has gained prominence, facilitating IoT application processing in edge networks closer to IoT sensors and effectively reducing delays inherent in cloud data centers. In this context, our study proposes a cloud-/fog-based service composition for IoT, introducing a novel fuzzy-based hybrid algorithm. This algorithm ingeniously combines Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) optimization algorithms, taking into account energy consumption and Quality of Service (QoS) factors during the service selection process. By leveraging this fuzzy-based hybrid algorithm, our approach aims to revolutionize service composition in IoT environments by empowering intelligent decision-making capabilities and ensuring optimal user satisfaction. Our experimental results demonstrate the effectiveness of the proposed strategy in successfully fulfilling service composition requests by identifying suitable services. When compared to recently introduced methods, our hybrid approach yields significant benefits. On average, it reduces energy consumption by 17.11%, enhances availability and reliability by 8.27% and 4.52%, respectively, and improves the average cost by 21.56%. [ABSTRACT FROM AUTHOR]

Abstract In the internet and cloud environment, service composition is always used to enhance the function and processing ability of clouds. Those clouds work together for a user and provide different functions. A service request may involve multiple clouds. The past work focuses on the method of service composition and ignores the energy composition when files are transferred between clouds, including the energy consumption for transferring files (sending files from the user to the cloud and receiving files from the cloud to the user) of the user. The paper models the service composition in a multicloud environment. Based on those models, we use the GA (genetic algorithm) algorithm (GA-C) to solve the service composition problem with multiple targets in a multicloud environment. Simulation results show that the GA-C can: (1) reduce the average number of involved clouds and the energy consumption between clouds, and (2) reduce the energy consumption of the user and the failure rate of service composition.

To improve the optimization quality, efficiency and stability of cloud manufacturing service composition, a optimization method for cloud manufacturing service composition based on improved artificial bee colony algorithm was proposed.Firstly, three methods of service collaboration quality calculation under cloud manufacturing service composition scenario were put forward.Then, the optimization model with service collaboration quality was constructed.Finally, an artificial bee colony algorithm with multi-search strategy island model was designed to solve the optimal cloud manufacturing service composition.The experimental results show that the proposed algorithm is superior to the current popular improved artificial bee colony algorithms and other swarm intelligence algorithms in terms of optimization quality, efficiency and stability.

Automatically generating service composition solutions that meet user application requirements is one of the hot research topics in the field of service composition in the context of Web service big data. To address the challenges of accurately obtaining reward function values and significant increase in time complexity when dealing with large-scale data in the context of reinforcement learning-based service composition, this paper proposes a novel approach that combines zero-sum game and inverse reinforcement learning. The proposed method models the service composition problem as a Markov Decision Process (MDP) and dynamically adjusts the service composition solution by solving for the optimal policy. By leveraging runtime records of service composition operations, we generate an expert experience dataset and develop a novel inverse reinforcement learning algorithm based on the integration of zero-sum game principles. Experimental results demonstrate that the proposed method effectively reduces the dependence of the inverse reinforcement learning algorithm on the quality of expert experience data and reduces the time cost of service composition.