Service Composition Research Articles

Designing a resilient cloud network fulfilled by reinforcement learning

Cloud manufacturing networks operate in stochastic environments, making them susceptible to various disruptions. Designing a resilient cloud network that can withstand and recover from disruptions while maintaining an efficient service composition is crucial. This paper emphasizes the importance of developing resilient cloud manufacturing networks, particularly in the context of unprecedented challenges. It explores the complexities of service composition using reinforcement learning algorithms, specifically Deep Deterministic Policy Gradient (DDPG), Twin Delayed DDPG (TD3), Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). SAC has emerged as a standout performer, demonstrating superior capabilities in enhancing network resilience. The model’s verification is conducted by investigating and comparing the convergence of the results. This research not only examines these reinforcement learning methodologies but also broadens its scope to provide ongoing development and evaluation of a robust network. The model is applied to a COVID-19 case study to validate the results. Beyond theoretical frameworks, this research highlights the need for adaptive and resilient manufacturing systems, offering insights into real-world challenges. The results emphasize the significance of using RL algorithms, particularly when customer satisfaction is the top priority. Moreover, when logistic costs and costs of changing servers are lower than customer dissatisfaction, the use of RL algorithms becomes essential for addressing disruption scenarios. The findings highlight the critical role of collaboration in intertwined supply networks in helping a disrupted network withstand and recover from a disruption.

Cloud-edge collaboration composition and scheduling for flexible manufacturing service with a multi-population co-evolutionary algorithm

The Cloud Manufacturing Service Composition and Scheduling (CMfg-SCS) are essential processes in cloud manufacturing. Flexible Manufacturing Services (FMS), such as those provided by industrial robots, are widely used in cloud manufacturing to improve service quality and efficiency. Traditional CMfg-SCS methodologies, however, fall short in effectively managing the inherent temporal-dynamic QoS and flexible capability of FMS. To overcome these challenges, we propose a novel Cloud Manufacturing Service Cloud-edge Collaboration Composition and Scheduling (CMfg-SCCCS) method for FMS. Firstly, the service-task matching hypernetwork is constructed, and the temporal-dynamic QoS and flexible capacity of FMS are modeled. Subsequently, we develop a CMfg-SCCCS optimization model aimed at three objectives, along with a cloud-edge collaboration scheduling mechanism to harmonize cloud and edge-local tasks. Finally, a multi-population co-evolution algorithm with adaptive meta-knowledge transfer mechanism is proposed to solve the complex optimization model. The computational experiments serve to validate the effectiveness of the CMfg-SCCCS method and further reveal the superiority of the co-evolution algorithm in enhancing both the convergence and diversity of the population.

Cloud Manufacturing Service Composition Optimization Based on Improved Chaos Sparrow Search Algorithm with Time-Varying Reliability and Credibility Evaluation

The economic friction and political conflicts between some countries and regions have made multinational corporations increasingly focus on the reliability and credibility of manufacturing supply chains. In view of the impact of poor manufacturing entity reliability and service reputation on the new-era manufacturing industry, the time-varying reliability and time-varying credibility of cloud manufacturing (CMfg) services were studied from the perspective of combining nature and society. Taking time-varying reliability, time-varying credibility, composition complexity, composition synergy, execution time, and execution cost as objective functions, a new six-dimension comprehensive evaluation model of service quality was constructed. To solve the optimization problem, this study proposes an improved chaos sparrow search algorithm (ICSSA), where the Bernoulli chaotic mapping formula was introduced to improve the basic sparrow search algorithm (BSSA), and the position calculation formulas of the explorer sparrow and the scouter sparrow were enhanced. The Bernoulli chaotic operator changed the symmetry of the BSSA, increased the uncertainty and randomness of the explorer sparrow position in the new algorithm, and affected the position update and movement strategies of the follower and scouter sparrows. The asymmetric chaotic characteristic brought better global search ability and optimization performance to the ICSSA. The comprehensive performance of the service composition (SvcComp) scheme was evaluated by calculating weighted relative deviation based on six evaluation elements. The WFG and DTLZ series test functions were selected, and the inverse generation distance (IGD) index and hyper volume (HV) index were used to compare and evaluate the convergence and diversity of the ICSSA, BSSA, PSO, SGA, and NSGA-III algorithms through simulation analysis experiments. The test results indicated that the ICSSA outperforms the BSSA, PSO, SGA, and NSGA-III in the vast majority of testing issues. Finally, taking disinfection robot manufacturing tasks as an example, the effectiveness of the proposed CMfg SvcComp optimization model and the ICSSA were verified. The case study results showed that the proposed ICSSA had faster convergence speed and better comprehensive performance for the CMfg SvcComp optimization problem compared with the BSSA, PSO, SGA, and NSGA-III.

Гимнография Спасу Нерукотворному в древнерусских богослужебных рукописях студийского периода

The article considers the ordinance for August 16 in honor of the waftage of the Holy Mandylion of Edessa to Constantinople. An archeographical overview of the worship texts was compiled, drawing upon manuscripts dating from the dominance of the Studite Typikon: Old Russian Book of Sticheron from the XII to XIV centuries and Middle Bulgarian unnotated Menaia from the second half of the XIII to the beginning of the XIV century. The study engaged eleven manuscript sources from the Studite period, nine of which were notated with znamenny chant notation and two were unnotated. The fixed composition of the service and the repertoire of hymnographic texts are analyzed. With the help of source analysis, the changes in the composition of the ordinance were revealed. The paper examines and formulates the main trends of the fixed compositions of worship. Conclusions are drawn regarding the degree of veneration of the Mandylion relic: during the Studite period, the service to the Holy Sudaruim is not characterized by a high liturgical status and is typically combined with the afterfeast period of the Assumption of the Virgin or order of divine service in honor of the Martyr Diomedes or the venerable Joachim of Sarandapor. The stability of the service composition in manuscripts from the Studite era is identified. Emphasis is placed on the stability of the musical interpretation of the poetic texts of the ordinance.

Bi-level optimization of shared manufacturing service composition based on improved NSGA-II.

To address the issue of insufficiently comprehensive representation of service composition indexes in the shared manufacturing environment, service reliability, confidence, and other indexes are decomposed in detail to establish a composition evaluation system, and a shared manufacturing service composition optimization model based on bi-level programming is proposed. The model takes Quality of Service (QoS) as the upper objective function and service reliability, service confidence and task fit as the lower objective function. The upper objective function needs to be minimized, while the lower objective function needs to be maximized. To achieve the optimal composition scheme of shared manufacturing services, the Criteria Importance Though Intercrieria Correlation (CRITIC) is used to determine the weights of the indicators, and the improved Fast Elitist Non-Dominated Sorting Genetic Algorithm (Improved NSGA-II) is employed to solve the multi-objective optimization problem. Finally, the improved NSGA-II has a 23.33% increase in convergence speed and a 69.99% gain in operational efficiency when compared to the traditional NSGA-II. The viability and effectiveness of the improved NSGA-II have been demonstrated.

Сутність поняття “туризм”, “турист” та “туристичний кластер”

The article is devoted to the study of the concept’s “tourist”, “tourism”, “tourist service”, “tourist product” and “tourist cluster”. The analysis of the legislation of Ukraine, which regulates tourist relations, was carried out, in particular, regarding the definition of the essence of the concepts “tourism”, “tourist”, “tourist product” and their comparison with the interpretation and understanding in economic thought and statistics. The main characteristics of a tourist have been clarified, namely: physical person; travel time – from 24 hours to 1 year; the purpose of the trip is not related to earning income. A distinction is made between tourist service and tourist product, the collision that arises with the interpretation of the concepts of “tourist product” and “tourist service” in various sciences is investigated. Approaches to understanding the concept of “tourism” as an economic phenomenon, a place of commercial enterprises and their classification into those that directly satisfy the needs of tourists and those that indirectly take part in satisfying these needs are defined. The expediency of determining the legal nature and subject composition of the tourist service, which affects the possible ways of protecting the violated rights of the tourist and the correct identification of the person who will be responsible for the improper provision of services, has been clarified: if it is a tourist service, the subject of responsibility will be the tour operator, if it is another service, the relevant entity providing this service (hotel, transport company, tour guide, etc.). The article examines the concept of “tourism” through the prism of clustering as the most comprehensive approach to understanding this term. It has been studied that the concept of “tourism” as a branch of the economy should be considered in the context of cluster associations of enterprises and other entities that provide services to tourists. The author’s interpretation of the concept of “tourist cluster” is proposed, namely: it is a territorial association of enterprises, research, production and financial companies and other business entities that have vertical and horizontal relationships with the aim of achieving economic and social effects and satisfaction needs of tourists.

Proactive Resource Allocation for Cloud Manufacturing: A Dynamic Approach with DNN-Based Hybrid Many Objective Leopard Seal Algorithm

In the domain of cloud manufacturing, diverse services and the escalating demand for efficient and cost-effective service composition selection in cloud manufacturing have spurred day by day. Traditional methods face several challenges such as the risk of convergence issues and suboptimal solutions, high dimensionality of decision space, increased computational complexity, and scalability issues. To overcome these complexities, this paper proposed a novel method named the Deep Neural Network (DNN) based Hybrid Many Objective Leopard Seal (HMOLS) algorithm. In this study, DNN is employed to predict future Quality of Service (QoS) for each service based on historical data and integrated features. These predictions are fed back into the optimization model, influencing the dynamic weight calculation for each objective function. In this work, the Leopard Seal Optimization is implemented for hyperparameter optimization to enhance the performance of the HMOLS algorithm. Various evaluation metrics namely accuracy, throughput, reliability, resource utilization, and scalability are utilized to evaluate the performance of the HMOLS method and compare its performance with existing methods. The experimental outcomes depict the effectiveness of the HMOLS approach for the effective selection of services and fusion for cloud manufacturing. DNN-based predictions utilize deep neural networks to predict resource demand or utilization patterns and enable more accurate allocation decisions. These predictions optimize resource allocation by reallocating resources accordingly and address the challenges of resource utilization. Meanwhile, this method is required to overcome the computational overhead for applicability in cloud manufacturing.

A new service composition method in the cloud‐based Internet of things environment using a grey wolf optimization algorithm and MapReduce framework

SummaryCloud 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.

A Hybrid Strategy Improved SPEA2 Algorithm for Multi-Objective Web Service Composition

A Hybrid Strategy Improved SPEA2 Algorithm for Multi-Objective Web Service Composition

Multi-objective Service Composition Optimization in Smart Agriculture Using Fuzzy-Evolutionary Algorithm

Multi-objective Service Composition Optimization in Smart Agriculture Using Fuzzy-Evolutionary Algorithm

AIDCT: An AI service development and composition tool for constructing trustworthy intelligent systems

The growing prevalence of AI services on cloud platforms is driving the demand for technologies and tools which enable the integration of multiple AI services to handle intricate tasks. Traditional methods of evaluating intelligent systems focus mainly on the performance of AI components, without providing comprehensive metrics for the system as a whole. Additionally, as these AI components are often sourced from third-party providers, users may face challenges due to inconsistent quality assurance and limitations in further developing AI models, and dealing with third-party service providers’ limitations. These limitations often involve quality assurance and a lack of capability for secondary development and training of services. To address these issues, we have developed a tool based on our previous work. It can autonomously build Intelligent systems from AI services while tackling the issues mentioned above. This tool not only creates service composition solutions that align with user-defined functional requirements and performance metrics but also executes these solutions to verify if the metrics meet user requirements. We have demonstrated the effectiveness of this tool in constructing trustworthy intelligent systems through a series of case studies.

Estimating the composition ratios of network services carried in mixed traffic

Network performance management (NPM) and quality of service (QoS) guarantee are the key concerns of network operators and service providers/consumers, respectively. The basis for traffic scheduling is one of the vital elements guiding the implementation of both NPM and QoS assurance. Although traffic identification technologies can theoretically be applied to providing basis for traffic scheduling, the computational complexity caused by the flow-by-flow processing makes them unsuitable for actual large-scale high-throughput network scenarios. In this work, we introduce a new scheme to provide a basis for service-oriented NPM and QoS guarantee. The proposed scheme treats the mixed traffic as a whole and estimates the composition ratios of network services carried in mixed traffic, which makes traffic scheduling for NPM and QoS more reasonable and flexible. To estimate the composition ratios, the observed mixed traffic is expressed as a bipartite graph according to the communication relationship. Then, the bipartite graph is further projected to a multi-dimensional hyper-image based on node coding, random walking and embedding mapping. By this way, we convert the service composition ratio estimation into a problem similar to image classification and enable it to be solved by general machine learning methods like convolutional neural networks. The proposed scheme facilitates batch traffic scheduling and can reorganize the distribution of compositions of network services for each link based on given management strategies. Its computational complexity is significantly lower than that of flow-by-flow processing methods. We validate the proposed solution via real network data and evaluate its performance with some benchmark methods based on traffic identification. The experimental results show that the proposed solution can accurately infer the service composition ratios of mixed traffic with low computational complexity.

A Knowledge Graph Service Composition Model for E-Government

In an era of Artificial Intelligence (AI), governments around the world are advancing their digital footprints, and using AI to achieving their objectives of effective and efficient public service delivery and citizens engagement. Knowledge Graph (KG), a constituent AI technology, has proven to be the means through which a government can use to manage the complexity of an e-Government instance, and realize its objective. One area in e-Government that KG is used to address, is the challenge of automated composition of web services, where web services are created and maintained by one government department, but is used by another, in an integrated and interoperable manner. Service composition is the combination of two or more web services to create a composite service in order to accomplish a task. In this paper, we present a KG-based approach for automatically composing web services in an e-Government context. Our aim is to advance the e-Government objective of effective and efficient public service delivery and citizens engagement, given a complex e-Government. We focus on creating a Service Dependency Graph (SDG), which captures the semantic relationships between the services, and we further describe the services with QoS characteristics, and then we create a weighted graph based on these two descriptions. We then adapted the Uniform Cost Search algorithm to find the shortest path between a start node and a goal node. We implement our algorithm, and carried-out experiments to obtain execution times, and we compared our results with benchmark results of comparable experimental setup. The difference in execution times of the service composition task is dependent on factors such as the design of the composition model, the complexity of the composition graph, and the hardware and software environments. Our research approach affords us the means to analyze and design our model; and validate and evaluate our work. Our model and the development approach help to achieve the e-Government goals of efficient and effective service delivery and citizens engagement.

A two-phase method to optimize service composition in cloud manufacturing

A two-phase method to optimize service composition in cloud manufacturing

IoT service composition based on improved Shuffled Frog Leaping Algorithm

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.

Energy-aware cloud manufacturing service selection and scheduling optimization

ABSTRACT Cloud Manufacturing Service Selection and Scheduling (CMSSS) is vital for optimizing resource allocation and meeting task requirements. However, inattention to the preheating process of manufacturing equipment has resulted in wasted energy. To reduce manufacturing energy consumption and ensure the Quality of Service (QoS), this paper establishes a bi-level programming model for CMSSS, quantifies the preheating energy consumption of manufacturing equipment by task cohesion, and proposes an energy-aware cloud manufacturing service selection and scheduling approach. The approach selects the service composition for a task from candidate service sets, schedules subtasks to avoid service occupancy conflicts and maximises task cohesion to reduce the preheating energy consumption of manufacturing equipment by Energy-aware Scheduling Generation Scheme (ESGS). Finally, it integrates ESGS into Non-dominated Sorting Genetic Algorithm II (NSGA-II) to determine the optimal task execution solution. Experimental results show that ESGS has a better Pareto front than the previous Feasible Scheduling Generation Scheme (FSGS) under seven types of QoS weights. With almost the same QoS satisfaction level, ESGS consumes, on average, 2% to 8% less energy than FSGS for preheating manufacturing equipment. In cloud manufacturing scenarios with preheating processes, ESGS can meet the QoS requirements of demanders as FSGS but with a better energy economy.

Mobility and energy efficient services composition algorithm with QoS guarantee for large scale Cyber–Physical–Social Systems

Due to the mobile and random nature of services in cyber–physical–social systems (CPSSs), developing service composition approaches that ensure high availability, minimal energy consumption, and high quality of service (QoS) remains a complex challenge. Over the last two decades, several service composition approaches have been proposed in the literature to deal with this challenge. Nevertheless, the existing approaches have certain limitations, particularly in situations where services may move from one location to another, become unavailable due to intensive battery usage, encounter failures, or undergo a decline in quality. These limitations often arise because these approaches do not simultaneously integrate mobility, energy, and QoS constraints while defining the user’s movement in a random manner. In this paper, the learning-based swarm optimization-aware service composition algorithm (LS-SCA) is proposed to overcome the aforementioned shortcoming. This approach surpasses existing ones by accounting simultaneously for the user’s mobility, energy, and QoS criteria during the service composition process. First, the Small World in Motion (SWIM) mobility model is employed in this study to determine the user’s mobility traces, avoiding the random generation of users’ traces. Second, an energy consumption model is proposed to increase the energy efficiency by avoiding the overuse of the devices’ batteries that can reduce the availability of services and lead to the composition failure. Third, the two-phase learning-based swarm optimizer (TPLSO) method is used in the composition process to find the sub-optimal composition that satisfies the global QoS constraints with the highest utility in terms of mobility, energy, and QoS. Unlike the most existing metaheuristic-based service composition approaches where the overall composition population is improved over a given number of iterations, the TPLSO method is exploited in this paper to improve only a subset of compositions, which reduces the composition time and increases the QoS utility of the composition. The simulation scenarios using two real datasets demonstrate that the LS-SCA approach outperforms six baselines in terms of energy consumption, QoS utility, and availability of composition. This notable performance makes the proposed approach more suitable for real-world applications where energy efficiency, QoS, and availability are crucial factors to consider.

The Data Product-service Composition Frontier: A Hybrid Learning Approach

The service dominant logic is a base concept behind modern economies and software products, with service composition being a well-known practice for companies to gain a competitive edge over others by joining differentiated services together, typically assembled according to a number of features. At the other end of the spectrum, product compositions are a marketing device to sell products together in bundles that often augment the value for the customer, e.g., with suggested product interactions, sharing, and so on. Unfortunately, currently each of these two streams—namely, product and service composition—are carried out and delivered individually in splendid isolation: anything is being offered as a product and as a service, disjointly. We argue that the next wave of services computing features more and more service fusion with physical counterparts as well as data around them. Therefore a need emerges to investigate the interactive engagement of both (data) products and services. This manuscript offers a real-life implementation in support of this argument, using (1) genetic algorithms (GA) to shape product-service clusters, (2) end-user feedback to make the GAs interactive with a data-driven fashion, and (3) a hybridized approach which factors into our solution an ensemble machine-learning method considering additional features. All this research was conducted in an industrial environment. With such a cross-fertilized, data-driven, and multi-disciplinary approach, practitioners from both fields may benefit from their mutual state of the art as well as learn new strategies for product, service, and data product-service placement for increased value to the customer as well as the service provider. Results show promise but also highlight plenty of avenues for further research.

Web service reliability and scalability determination using optimized depth wise separable convolutional neural network

AbstractWeb service composition (WSC), a distributed architecture, creates new services atop existing ones. Ensuring trust and assessing performance and dependability in online services coordination is essential. In this paper, “Web Service Reliability and Scalability Determination Using Depth Wise Separable Convolutional Neural Network” (WSRS‐DWSCNN) is proposed to assess the trustworthiness of online service compositions, particularly focusing on performance and dependability. This work addresses the need to predict the reliability and scalability of Business Process Execution Language (BPEL) composite web services. The proposed approach transforms the BPEL specification into a Depth Wise Separable Convolutional Neural Network (DWSCNN) and annotates it with probabilistic properties for prediction. The DWSCNN model classifies the outcomes as correct or incorrect, and to enhances the prediction of web service composition scalability and reliability, we optimize the DWSCNN's weight parameters using the Adolescent Identity Search Algorithm (AISA). The proposed technique is activated in Python and its efficacy is analyzed under some metrics, such as reliability, scalability, accuracy, sensitivity, specificity, precision, F‐measure. The proposed method provides 12.36%, 45.39%, and 25.97% better reliability, 41.39%, 11.39%, 34.16% better accuracy compared with existing methods like, Web service reliability prediction depending on machine learning (WSRS‐K‐means), reliability prediction method for multiple state cloud/edge‐basis network utilizing deep neural network (WSRS‐DNN‐BO), and improving reliability of mobile social cloud computing utilizing machine learning in content addressable network (WSRS‐CAN), respectively.

A Discrete Adaptive Lion Optimization Algorithm for QoS-Driven IoT Service Composition with Global Constraints

A Discrete Adaptive Lion Optimization Algorithm for QoS-Driven IoT Service Composition with Global Constraints