Service Composition Solutions Research Articles

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.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.

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.

UHI based Healthcare Management System through Digital Transformation

The Unified Health Interface (UHI) represents a pivotal initiative within the Indian healthcare landscape, offering an open and interoperable IT ecosystem tailored for modern and innovative health service delivery. In response to the challenges posed by the COVID-19 pandemic, UHI emerges as a strategic solution aimed at ensuring enhanced accessibility and quality of healthcare services. Through its seamless interface, UHI facilitates various functionalities including online search, lab test bookings, virtual medical consultations via video, voice, or chat, digital transfer of health records, and secure digital payments. This paper explores the transformative potential of UHI in addressing the evolving needs of healthcare delivery in India. By fostering ease of application and improving access to quality healthcare services, UHI stands as a cornerstone in the nation's healthcare infrastructure. The emergence of enterprise- and national-level healthcare systems underscores the growing demand for comprehensive and cost-effective public healthcare solutions. Leveraging classic telecommunications and internet services such as presence, voice, video, and messaging, along with service composition solutions and enterprise networks, UHI lays the groundwork for unified health applications. This research delves into the implications of UHI in driving healthcare accessibility, affordability, and efficiency, while also examining its role in shaping the future trajectory of healthcare delivery in India. By elucidating the integration of UHI within existing healthcare frameworks, this paper provides valuable insights into the transformative potential of digital technologies in revolutionizing public health services.

Domain Constraints-Driven Automatic Service Composition for Online Land Cover Geoprocessing

With the rapid development of web service technology, automatic land cover web service composition has become one of the key challenges in solving complex geoprocessing tasks of land cover. Service composition requires the creation of service chains based on semantic information about the services and all the constraints that should be respected. Artificial intelligence (AI) planning algorithms have recently significantly progressed in solving web service composition problems. However, the current approaches lack effective constraints to guarantee the accuracy of automatic land cover service composition. To address this challenge, the paper proposes a domain constraints-driven automatic service composition approach for online land cover geoprocessing. First, a land cover service ontology was built to semantically describe land cover tasks, data, and services, which assist in constructing domain constraints. Then, a constraint-aware GraphPlan algorithm was proposed, which constructs a service planning graph and searches services based on the domain constraints for generating optimal web service composition solutions. In this paper, the above method was integrated into a web prototype system and a case study for the online change detection automatic geoprocessing was implemented to test the accuracy of the method. The experimental results show that with this method, a land cover service chain can generate automatically by user desire objective and domain constraints, and the service chain execution result is more accurate.

Logistics-involved service composition in a dynamic cloud manufacturing environment: A DDPG-based approach

Service composition as an important technique for combining multiple services to construct a value-added service is a major research issue in cloud manufacturing. Highly dynamic environments present great challenges to cloud manufacturing service composition (CMfg-SC). Most of previous studies employ heuristic algorithms to solve service composition issues in cloud manufacturing, which, however, are designed for specific problems and lack adaptability necessary to dynamic environment. Hence, CMfg-SC calls for new adaptive approaches. Recent advances in deep reinforcement learning (DRL) provide a new means for solving this issue. Based on DRL, we propose a Deep Deterministic Policy Gradient (DDPG)-based service composition approach to cloud manufacturing, with which optimal service composition solutions can be learned through repeated training. Performance of DDPG in solving CMfg-SC in both static and dynamic environments is examined. Results obtained with another DRL algorithm - Deep Q-Networks (DQN) and the traditional Ant Colony Optimization (ACO) are also presented. Comparison indicates that DDPG has better adaptability, robustness, and extensibility to dynamic environments than ACO, although ACO converges faster and its steady QoS value of the service composition solution is higher than that of DDPG by 0.997%. DDPG outperforms DQN in convergence speed and stability, and the QoS value of the service composition solution of DDPG is higher than that of DQN by 3.249%.

Tackling temporal-dynamic service composition in cloud manufacturing systems: A tensor factorization-based two-stage approach

Service composition (SC) is a critical matching process in cloud manufacturing (CMfg) mainly to organize geographically-scattered manufacturing services for manufacturing tasks. Currently, most of the existing CMfg SC approaches assume that cloud service data are always static without considering the dynamics in cloud environments. Meanwhile, existing approaches rely heavily on easy-to-get dense historical service data for SC. However, adapting such ideal approaches to fast-changing temporal-dynamic cloud environments possibly leads to unsatisfactory matching results. This paper proposes a hybrid two-stage approach to deal with temporal-dynamic CMfg SC problem by incorporating tensor factorization (TF). The approach has two stages: temporal-dynamic prediction (Stage I) and many-objective SC optimization (Stage II). For Stage I, a non-negative TF based CMfg SC predictor is proposed to obtain average attribute values of candidate services in desired time slots easily and conveniently. The predictor extracts temporal-dynamic QoS values of candidate services into low-dimensional latent factor space and build a low-rank approximation. Based on the predicted service data, Stage II optimizes temporal-dynamic CMfg SC solutions by an improved many-objective evolutionary optimizer, GrEA-X. GrEA-X is reinforced with multiple variation operators and a modified mating pool selection procedure to enhance its exploration ability in a given time limit. Numerical experiments are then carried out to validate the efficacy of the proposed approach. Based on these results, we confirm the superior performance of our approach over other benchmarks.

A Graph Neural Network and Pointer Network-Based Approach for QoS-Aware Service Composition

Quality-of-service (QoS)-aware service composition aims to aggregate multiple existing services to meet users' complex functional and nonfunctional requirements that cannot be met by simple services. The accumulation of user tasks and service composition solutions makes it possible to mine empirical rules from those historical compositions to reduce the search space and thus improve the composition efficiency. Traditional empirical rule-based methods focus on mining with well-designed rules, ignoring the underlying correlations between tasks and between services. Meanwhile, infrequently used services are not valued by these methods, but these services may still be used in constructing optimal service solutions. In addition, many methods use reinforcement learning to compose services to efficiently construct service solutions, but they do not achieve the same effect as traditional metaheuristic methods. In view of the above shortcomings, considering the ability of graphs to express relationships, we first construct tasks and services as graphs and then use a graph neural network (GNN) to mine underlying correlations and predict the probability that each service will be used to construct the solution corresponding to the task. Next, based on these high-probability services, we utilize pointer network (PN)-based reinforcement learning to efficiently construct the initial service solution. The PN is often used to solve combinatorial optimization problems and is noninferior to metaheuristics for small-scale data. To increase the generalization ability of the network, we superimpose another layer on the PN. Finally, to take advantage of infrequently used services, we use the local whale optimization algorithm (WOA) to fine-tune the initial solution and obtain a high-quality solution. The experimental results show that our approach outperforms several existing methods in terms of both composition efficiency and solution quality.

Hybrid Metaheuristics Web Service Composition Model for QoS Aware Services

Recent advancements in cloud computing (CC) technologies signified that several distinct web services are presently developed and exist at the cloud data centre. Currently, web service composition gains maximum attention among researchers due to its significance in real-time applications. Quality of Service (QoS) aware service composition concerned regarding the election of candidate services with the maximization of the whole QoS. But these models have failed to handle the uncertainties of QoS. The resulting QoS of composite service identified by the clients become unstable and subject to risks of failing composition by end-users. On the other hand, trip planning is an essential technique in supporting digital map services. It aims to determine a set of location based services (LBS) which cover all client intended activities quantified in the query. But the available web service composition solutions do not consider the complicated spatio-temporal features. For resolving this issue, this study develops a new hybridization of the firefly optimization algorithm with fuzzy logic based web service composition model (F3L-WSCM) in a cloud environment for location awareness. The presented F3L-WSCM model involves a discovery module which enables the client to provide a query related to trip planning such as flight booking, hotels, car rentals, etc. At the next stage, the firefly algorithm is applied to generate composition plans to minimize the number of composition plans. Followed by, the fuzzy subtractive clustering (FSC) will select the best composition plan from the available composite plans. Besides, the presented F3L-WSCM model involves four input QoS parameters namely service cost, service availability, service response time, and user rating. An extensive experimental analysis takes place on CloudSim tool and exhibit the superior performance of the presented F3L-WSCM model in terms of accuracy, execution time, and efficiency.

An Incentive-based Mechanism for Volunteer Computing Using Blockchain

The rise of fast communication media both at the core and at the edge has resulted in unprecedented numbers of sophisticated and intelligent wireless IoT devices. Tactile Internet has enabled the interaction between humans and machines within their environment to achieve revolutionized solutions both on the move and in real-time. Many applications such as intelligent autonomous self-driving, smart agriculture and industrial solutions, and self-learning multimedia content filtering and sharing have become attainable through cooperative, distributed, and decentralized systems, namely, volunteer computing. This article introduces a blockchain-enabled resource sharing and service composition solution through volunteer computing. Device resource, computing, and intelligence capabilities are advertised in the environment to be made discoverable and available for sharing with the aid of blockchain technology. Incentives in the form of on-demand service availability are given to resource and service providers to ensure fair and balanced cooperative resource usage. Blockchains are formed whenever a service request is initiated with the aid of fog and mobile edge computing (MEC) devices to ensure secure communication and service delivery for the participants. Using both volunteer computing techniques and tactile internet architectures, we devise a fast and reliable service provisioning framework that relies on a reinforcement learning technique. Simulation results show that the proposed solution can achieve high reward distribution, increased number of blockchain formations, reduced delays, and balanced resource usage among participants, under the premise of high IoT device availability.

Multitask-oriented manufacturing service composition in an uncertain environment using a hyper-heuristic algorithm

One of the most important issues in cloud manufacturing involves obtaining an optimal manufacturing service composition solution. However, traditional manufacturing service composition methods either focused on single-task-oriented service composition or optimized solutions under a deterministic environment. In the study, a multitask-oriented manufacturing service composition (MMSC) model with two stages in uncertain environment is proposed. It handles the problem of multitask scheduling and also deals with the inherent uncertainty and ambiguity in cloud manufacturing including the occurrence of urgent task requests and the delayed delivery time of raw materials. In order to solve the MMSC model, a new genetic based hyper-heuristic algorithm (GA-HH) with adjustable length of chromosome is proposed. The GA-HH contains a set of low-level heuristics that directly operate on the solution domain that are organized by the high-level heuristic (i.e., genetic algorithm). Finally, the proposed GA-HH is proved as an efficient, effective, and robust algorithm to solve the MMSC model with considerations of multitask and uncertainty, by comparing it with other well-known meta-heuristic algorithms such as the genetic algorithm and particle swarm optimization.

Enriching Web Services Tags to Improve Data-Driven Web Services Composition

Due to the large number of existing services and complexity of manual composition, automatic service composition is provided to enable automatic search of the service compositions for the given queries. Many solutions for automatic service composition have been developed, including integer programming, graph planning, artificial intelligence, and so on in this paper, a heuristic method is proposed to improve the data-driven composition of web services by enriching tags based on tags semantic. To do so, firstly, useful information on web services is collected from various sources and is turned into collections of tags. In the next step, using the hierarchical clustering algorithm, the tags are clustered based on semantic similarity. Thereafter, for services which do not have enough tags, enrichment of the tag is carried out and finally, using an algorithm, composition solutions based on QoS parameters are extracted, which can formulate user targets or even provide potential compositions. Moreover, a series of tests were conducted on the web services, which validate the efficiency of the proposed approach. The experimental results confirm the effectiveness of the proposed service composition method and high quality of tag enriching strategies.

A Multi-Population Genetic Algorithm for Adaptive Qos-Aware Service Composition in Fog-Iot Healthcare Environment

The growth of Internet of thing (IoT) implies the availability of a very large number of services which may be similar or the same, managing the Quality of Service (QoS) helps to differentiate one service from another.The service composition provides the ability to perform complex activities by combining the functionality of several services within a single process. Very few works have presented an adaptive service composition solution managing QoS attributes, moreover in the field of healthcare, which is one of the most difficult and delicate as it concerns the precious human life.In this paper, we will present an adaptive QoS-Aware Service Composition Approach (P-MPGA) based on multi-population genetic algorithm in Fog-IoT healthcare environment. To enhance Cloud-IoT architecture, we introduce a Fog-IoT 5-layared architecture. Secondly, we implement a QoS-Aware Multi-Population Genetic Algorithm (P-MPGA), we considered 12 QoS dimensions, i.e., Availability (A), Cost (C), Documentation (D), Location (L), Memory Resources (M), Precision (P), Reliability (R), Response time (Rt), Reputation (Rp), Security (S), Service Classification (Sc), Success rate (Sr), Throughput (T). Our P-MPGA algorithm implements a smart selection method which allows us to select the right service. Also, P-MPGA implements a monitoring system that monitors services to manage dynamic change of IoT environments. Experimental results show the excellent results of P-MPGA in terms of execution time, average fitness values and execution time / best fitness value ratio despite the increase in population. P-MPGA can quickly achieve a composite service satisfying user’s QoS needs, which makes it suitable for a large scale IoT environment.

A nonlinear service composition method based on the Skyline operator

The concept of service composition can provide the complex functionality for users. As the widespread application of cloud computing, the number of services grows exponentially. It becomes more difficult to find out the optimal service composition solution quickly. This paper proposes a nonlinear service composition method based on the Skyline operator. The Skyline operator is to find a collection of data that cannot be dominated by others, which is used to prune the redundant services to reduce the search space. Then the service composition problem is formulated as a nonlinear integer programming model by a mathematical programming language (AMPL), and solved by the existing nonlinear solvers Bonmin. The experiments show that the proposed method can effectively improve the efficiency of service composition, while ensuring the quality of solution.

QoS-Aware Service Composition in Cloud Manufacturing: A Gale–Shapley Algorithm-Based Approach

Cloud manufacturing (CMfg) is an emerging paradigm that aims to provide on-demand manufacturing services over the Internet. Service composition as an important means for generating value-added services plays an important role in achieving the aim of CMfg. Most of previous works focused on exploring techniques of service composition for a single composite task using meta-heuristic algorithms. However, the issue of service composition for multiple composite tasks has rarely been considered. Meta-heuristic algorithms suffer from cumbersome parameter tuning as well as the tendency of getting into local optima. In addition, the effectiveness of different algorithms has not yet been fully explored when different degrees of constraints are imposed. Different from approaches in most of the previous works, this paper proposes an extended Gale–Shapley (GS) algorithm-based approach for service composition that allows generation of multiple service composition solutions effectively. Requirements with different constraints are considered. Experimental results indicate that: 1) meta-heuristic algorithms can be used in various scenarios with different degrees of constraints. However, they are incapable of finding the optimal solutions in situations with relatively loose constraints, and moreover, the failure rate of finding solutions for a batch of multiple tasks is high; 2) the dynamic programming (DP) is a method that is the most sensitive to constraints. It performs better only under loose constraints and in the case of a single requirement; and 3) the application range of the GS method proposed is wider than that of the DP method. It can achieve better performance when constraints are relaxed irrespective of task status (i.e., a single task or multiple tasks), and moreover, it can make more tasks find solutions in the multitask scenario without service reuse.

Logistics-involved QoS-aware service composition in cloud manufacturing with deep reinforcement learning

• Deep reinforcement learning is used to solve the QoS-aware service composition problem in cloud manufacturing with the consideration of logistics. • A DRL algorithm that combines the basic DQN algorithm, the dueling architecture, and the prioritized replay mechanism is proposed. • Experimental results demonstrate the effectiveness, high efficiency, adaptability, and scalability of the proposed DRL algorithm. Cloud manufacturing is a new manufacturing model that aims to provide on-demand manufacturing services to consumers over the Internet. Service composition is an essential issue as well as an important technique in cloud manufacturing (CMfg) that supports construction of larger-granularity, value-added services by combining a number of smaller-granularity services to satisfy consumers’ complex requirements. Meta-heuristics algorithms such as genetic algorithm, particle swarm optimization, and ant colony algorithm are frequently employed for addressing service composition issues in cloud manufacturing. These algorithms, however, require complex design flows and painstaking parameter tuning, and lack adaptability to dynamic environment. Deep reinforcement learning (DRL) provides an alternative approach for solving cloud manufacturing service composition (CMfg-SC) issues. DRL as model-free artificial intelligent methods enables a system to learn optimal service composition solutions through training, which can therefore circumvent the aforementioned problems with meta-heuristics algorithms. This paper is dedicated to exploring possible applications of DRL in CMfg-SC. A logistics-involved QoS-aware DRL-based CMfg-SC is proposed. A dueling Deep Q-Network (DQN) with prioritized replay named PD-DQN is designed as the DRL algorithm. Effectiveness, robustness, adaptability, and scalability of PD-DQN are investigated, and compared with that of the basic DQN and Q-learning. Experimental results indicate that PD-DQN is able to effectively address the CMfg-SC problem.

A many-objective memetic algorithm for correlation-aware service composition in cloud manufacturing

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.

Adaptive and large-scale service composition based on deep reinforcement learning

In a service-oriented system, simple services are combined to form value-added services to meet users’ complex requirements. As a result, service composition has become a common practice in service computing. With the rapid development of web service technology, a massive number of web services with the same functionality but different non-functional attributes (e.g., QoS) are emerging. The increasingly complex user requirements and the large number of services lead to a significant challenge to select the optimal services from numerous candidates to achieve an optimal composition. Meanwhile, web services accessible via computer networks are inherently dynamic and the environment of service composition is also complex and unstable. Thus, service composition solutions need to be adaptable to the dynamic environment. To address these key challenges, we propose a new service composition scheme based on Deep Reinforcement Learning (DRL) for adaptive and large-scale service composition. The proposed approach is more suitable for the partially observable service environment, making it work better for real-world settings. A recurrent neural network is adopted to improve reinforcement learning, which can predict the objective function and enhance the ability to express and generalize. In addition, we employ the heuristic behavior selection strategy, in which the state set is divided into the hidden and fully observable state sets, to perform the targeted behavior selection strategy when facing with different types of states. The experimental results justify the effectiveness and efficiency, scalability, and adaptability of our methods by showing obvious advantages in composition results and efficiency for service composition.

On workflow aware location-based service composition for personal trip planning

Trip planning is one of the most important techniques in supporting digital map services. It aims to find a set of location based services (LBSs) that can cover all user intended activities specified in query, such that the services have reasonable spatial distribution. In the temporal domain, the planned trip is supposed to satisfy all the workflow temporal dependencies among intended activities required by users. However, existing service composition solutions do not consider the complex spatio-temporal characteristics. To address this problem, this paper studies the problem of workflow aware location based service composition, which targets to return a rational trip formed by a set of location-based services that not only are rationally distributed in spatial, but also ensure all intended activities and their temporal workflow constraints to be met. Particularly, we propose a collective spatial keyword search based method to speed up the query processing. Experimental study on two real datasets demonstrates the effectiveness and efficiency of our proposed algorithms.

Networked correlation-aware manufacturing service supply chain optimization using an extended artificial bee colony algorithm

Manufacturing service supply chain (MSSC) optimization has been intensively studied to find an optimal service composition solution with the best quality of service (QoS) value. However, traditional MSSC optimization methods usually assume that candidate services are independent of one another. Therefore, potentially better MSSC solutions may have been neglected by not considering the positive influence of correlations between services on the QoS value. This study proposes a novel networked correlation-aware manufacturing service composition (NCMSC) mathematical model to characterize the influence of vertical and horizontal correlations between services on the QoS value of MSSC solution. To solve the NCMSC model, an extended artificial bee colony (ABC) algorithm is proposed to find a near-optimal solution with the best QoS value. The specific improvements to the original ABC algorithm include the following: (1) a new matrix-based encoding scheme is proposed to describe the MSSC solution in which each column contains a vertical composite structure and collaborative services for each subtask; (2) the migration operator of a biogeography-based optimization algorithm is combined with the original ABC algorithm to address the discrete MSSC optimization problem and improve the performance of the original ABC algorithm. The results of the experiments illustrate the importance of networked correlations between services, better practicality, effectiveness, and efficiency of the extended ABC algorithm in solving the optimization problem of MSSC.

Urgent task-aware cloud manufacturing service composition using two-stage biogeography-based optimisation

ABSTRACTOptimal service composition is a critical issue in cloud manufacturing systems. However, unpredictable dynamic factors, such as urgent task request occurring during the production process will have an influence on the original execution plan. In this paper, a new multi-objective optimisation problem based on quality of service of urgent task-aware cloud manufacturing service composition is presented, and two service recomposition methods based on vertical collaboration and speed selection are proposed to expedite task completion. A two-stage (i.e. composition and recomposition) biogeography-based optimisation (TBBO) algorithm is proposed to solve the corresponding model. Variable neighbourhood search is used to improve the exploitation ability of the TBBO algorithm. Experiment results demonstrate that the proposed TBBO algorithm can obtain better cloud manufacturing service composition and recomposition solutions compared with the basic biogeography-based optimisation algorithm, genetic algorithm, and differential evolution, and the two proposed service recomposition methods can reduce task execution time more effectively than the original composition method.