Web Service Composition Research Articles

Service-oriented model-based fault prediction and localization for service compositions testing using deep learning techniques

As service-oriented computing systems become more buoyant and complex, the occurrence of faults dramatically increases. Fault prediction plays a crucial role in the service-oriented computing paradigm, aiming to reduce testing cost while maximizing testing quality to utilize testing resources effectively and increase the reliability of service compositions. Although various fault prediction techniques were considered in software testing, service-oriented systems were less fortunate, in which most of the studies have focused on single web services testing rather than service compositions. Moreover, mainly the detection of faulty/non-faulty services was addressed, ignoring the estimate of faults count, their severity, as well as predicting when and where such faults would occur. In this paper, a multilateral model-based fault prediction and localization approach is proposed using deep learning techniques for web service compositions testing rather than single web service testing, which uniquely predicts not only faulty services, but also their count and severity level, location of faults, and time at which faults would occur. Three deep learning models are investigated: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and a proposed hybrid model based on both CNN and RNN. The proposed approach is language-independent, as it adopts process metrics rather than code metrics to overcome the code unavailability concern of services. The experimental analysis adopted main performance metrics on multiple public datasets to evaluate its efficiency and effectiveness. The results indicated that the hybrid CNN_RNN model achieves an average accuracy range of 84%–95.7%, where the RNN and CNN models individually achieve 75%–90% and 70%-79.3% respectively. Thus, the hybrid model increases the accuracy level by 5%–10% and 15%–20%, while achieving the least mean square error of 30% and 60% compared to the RNN and CNN models respectively. In terms of time, the RNN model consumes less average time as of 30–50 ms for the different datasets of variant sizes compared to the CNN and hybrid CNN_RNN models that consume 79–102 and 177–224 ms respectively. Thus, RNN model consumes around 50%–80% less time than those of the CNN and hybrid models respectively.

Mutual Information-Based Modeling for Services Dependency

Web services composition has drawn a great attention in computing industries to build complex and large systems. However, web services composition modeling has major challenges, including dependency determination, complexity, user requests dependency, handling cycles within a composition, service redundancy and scalability concerns. The service dependency graph (SDG) between services in a repository should be accurate to ensure the quality of composition and the associated user requests' responses. Despite of the crucial importance of accurate dependencies for adequate web services compositions, current modeling approaches do not provide any metric to evaluate the dependency between services for quality estimation. In this paper, the Mutual Information-based Services Dependency (MISD) model is proposed as a graph-based modeling approach, independent to any given user request. It constructs services dependency graphs based on Web Services Mutual Information (WSMI), a proposed modified version of mutual information as the dependency metric, along with other criteria for an accurate, efficient dependency evaluation. It finds the optimum composition representing the structure of the web services composition in a repository rather than the path of given user requests. The experimental dependency analysis emphasizes the efficiency of the generated OC and accuracy of the constructed SDG to be 76% and 86% higher than the state-of-the-art models respectively. The time cost to build the SDG and to find the OC is reduced dramatically up to 99% for different public repositories compared to the state-of-the-art studies as the number of user requests increases.

An Enhanced Energy-Efficient Web Service Composition Algorithm Based on the Firefly Algorithm

Numerous web services with the same function but different service qualities are constantly emerging on the network. Optimizing web service composition based on multiple candidate services sets an urgent problem in the service composition neighborhood. This paper modifies the traditional Firefly algorithm and adds exchange and mutation mechanisms to optimize the Web service composition efficiently in multiple candidate service sets. Meanwhile, it discretizes the continuous space of its solution set and better adapts to the service composition optimization problem. Experimental results show that compared with the GA, IA, SA, ACO, FACO, and EFACO algorithms, this algorithm has better optimization performance, faster speed, and higher energy efficiency for solving service composition optimization problems in the case of large-scale data. The higher the combined complexity of the solution, the stronger the performance compared to other algorithms. It can better deal with the increasingly complex situation of Web service composition problems.

Digital twin composition in smart manufacturing via Markov decision processes

Digital Twins (DTs) are considered key components in smart manufacturing. They bridge the virtual and real world with the goal to model, understand, predict, and optimize their corresponding real assets. Such powerful features can be exploited in order to optimize the manufacturing process. In this paper, we propose an approach, based on Markov Decision Processes (MDPs) and inspired by Web service composition, to automatically propose an assignment of devices to manufacturing tasks. This assignment, or policy, takes into account the uncertainty typical of the manufacturing scenario, thus overcoming limitations of approaches based on classical planning. In addition, obtained policies are proven to be optimal with respect to cost and quality, and are continuously updated in order to adapt to an always evolving scenario. The proposed approach is showcased in an industrial application scenario, and is implemented as a freely available tool.

A novel membrane‐inspired multiverse optimizer algorithm for quality of service‐aware cloud web service composition with service level agreements

SummaryCloud computing is an emerging technology in computing that provides different services over the Internet. It needs composite services to perform a complex task. Optimal selection of services that provides both functionality and nonfunctionality requirements is an NP‐hard problem. This study uses nondeterministic parallel and distributed structures of membrane systems for the recently improved multiverse optimization algorithm to improve the quality of solutions. In the previous membrane‐inspired algorithm, the population was divided into subpopulations that evolve different dynamic membranes. This study not only uses a conventional membrane‐inspired approach to introduce a conventional membrane‐inspired multiverse optimizer (CMIMVO) for the first time but also proposes an algorithm that divides the variables (dimension) into subgroups for different membranes called proposed membrane‐inspired multiverse optimizer (PMIMVO). Thus, in PMIMVO, each membrane works on a subgroup to gain global information, which considers the best values obtained by other membranes for other variables. The PMIMVO shows promising results on benchmark function problems. Furthermore, simulation results show that the PMIMVO approach could achieve up to 38% improvement in integrated quality of service (QoS) with attributes including response time, price, availability, and reliability in comparison with the previous approaches, including genetics algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), moth–flame optimization (MFO) improved multiverse optimizer (MVO), and CMIMVO.

EVALUATION OF WEB SERVICES COMPOSITION PROBLEM IN MULTI-CLOUD ENVIRONMENT

The development of Web services as a distribution, service consumption, and discovery architecture was successful. The components of SOA are widely used in e-business, process control, multimedia services, and several other disciplines. A QoS is still helpful for connecting non-functional concepts to Web services, and it is next used as a key point of differentiation for the different Service provider. In a multi-cloud environment (MCE), each atomic web service published by any cloud provider with the same functionality has a different price and quality of service (QoS). Service discovery and composition are the key challenges for web services development. The challenges in the composition of services distributed in multi-cloud environments include increased cost and a reduction in its speed due to the increasing number of services, providers, and clouds. Consequently, to overcome these challenges, the QoS-aware multi-cloud web service composition is presented in this work a Hybrid Firefly and Bee Colony Optimization Algorithm is introduced for the Multi-cloud service composition problem of NP-Hard in a multi- cloud environment. Keywords: Services Composition, Firefly, Bee Colony Algorithm, QoS Metrics.

Cloud Service Selection System Approach based on QoS Model: A Systematic Review

The Internet of Things (IoT) has received a lot of interest from researchers recently. IoT is seen as a component of the Internet of Things, which will include billions of intelligent, talkative "things" in the coming decades. IoT is a diverse, multi-layer, wide-area network composed of a number of network links. The detection of services and on-demand supply are difficult in such networks, which are comprised of a variety of resource-limited devices. The growth of service computing-related fields will be aided by the development of new IoT services. Therefore, Cloud service composition provides significant services by integrating the single services. Because of the fast spread of cloud services and their different Quality of Service (QoS), identifying necessary tasks and putting together a service model that includes specific performance assurances has become a major technological problem that has caused widespread concern. Various strategies are used in the composition of services i.e., Clustering, Fuzzy, Deep Learning, Particle Swarm Optimization, Cuckoo Search Algorithm and so on. Researchers have made significant efforts in this field, and computational intelligence approaches are thought to be useful in tackling such challenges. Even though, no systematic research on this topic has been done with specific attention to computational intelligence. Therefore, this publication provides a thorough overview of QoS-aware web service composition, with QoS models and approaches to finding future aspects.

Monitoring Land Cover Change by Leveraging a Dynamic Service-Oriented Computing Model

Land cover change (LCC) is increasingly affecting global climate change, energy cycle, carbon cycle, and water cycle, with far-reaching consequences to human well-being. Web service-based online change detection applications have bloomed over the past decade for monitoring land cover change. Currently, massive processing services and data services have been published and used over the internet. However, few studies consider both service integration and resource sharing in land cover domain, making end-users rarely able to acquire the LCC information timely. The behavior interaction between services is also growing more complex due to the increasing use of web service composition technology, making it challenging for static web services to provide collaboration and matching between diverse web services. To address the above challenges, a Dynamic Service Computing Model (DSCM) was proposed for monitoring LCC. Three dynamic computation strategies were proposed according to different users’ requirements of change detection. WMS-LCC was first developed by extending the existing WMS for ready-use LCC data access. Spatial relation-based LCC data integration was then proposed for extracting LCC information based on multi-temporal land cover data. Processing service encapsulation and service composition methods were also developed for chaining various land cover services to a complex service chain. Finally, a prototype system was implemented to evaluate the validity and feasibility of the proposed DSCM. Two walk-through examples were performed with GlobeLand30 datasets and muti-temporal Landsat imagery, respectively. The experimental results indicate that the proposed DSCM approach was more effective and applicable to a wider range of issues in land cover change detection.

PIRAP: Security Assessment Framework for Heterogeneous Web Service Composition

In recent times, enterprise environments such as E-banking, E-commerce, etc., are greatly affected by the evolution of web services and their associated attacks. The process of web service composition gives rise to security issues due to the incompatibility standards of the Simple Object Access Protocol (SOAP) and representational state transfer (REST) features and the compromising service-level agreement (SLA) between the end parties which has not been considered yet. Also, the security assessment process needs to cooperate with good security standards and web services. In case of heterogeneous web services, assessment done using machine learning technologies makes use of pattern analysis where this does not work with the evolving trend behind the web service environment. Hence, there is a need for a security assessment model which will incorporate finite state machine (FSM) in its assessment process to keep track of the various WS-attacks that arise in the web application via web services in heterogeneous environments. The proposed model does the web service composition by compromising all the security conflicts that arise due to the usage of dissimilar web services. We experimented the concept by testing several web services and observed that the performance of the proposed framework in attack detection is accurate and automatic thereby achieving traceability and computability metrics.

Multi-objective distributed Web service composition—A link-dominance driven evolutionary approach

Service-oriented architecture (SOA) encourages the creation of modular applications involving Web services as the reusable components. Data-intensive Web services manipulate and deal with the massive data that emerged by technological advances and their various applications. Distributed Data-intensive Web Service Composition (DWSC) includes the selection of data-intensive Web services from diverse locations on the network and composing them into a service to accomplish a complex task. Optimising Quality of Service (QoS) while satisfying the functional requirements is a fundamental challenge for service developers. The multi-objective and distributed nature of the DWSC problem demands satisfying algorithms to automatically produce the Pareto optimal set of composite services. In this paper, we propose a new Evolutionary Computation (EC) approach based on the Non-dominated Sorting Genetic Algorithm (NSGA-II) and a novel local search technique to effectively solve the multi-objective distributed DWSC problems. Our local search technique employs an innovative concept, communication link dominance, to incorporate domain-specific knowledge. The performance of our proposed link-dominance driven EC approach is evaluated on benchmark datasets. The results show that our proposed method has the highest quality with acceptable execution time for most of the composition tasks among competing algorithms.

Interoperability approach for Hospital Information Systems based on the composition of web services

Nowadays, many health centers use Hospital Information Systems (HIS) for the daily management of center activities such as patient data management. In Burkina Faso, many HIS are used for patient data management but these systems cannot cooperate because the data sources are often heterogeneous. In order to guarantee a better diagnosis in patient management, physicians need to access these data sources in a unique way through queries. For better analysis of a patient's situation, the health worker may want to access multiple data sources that do not belong to the same health center. This is only possible if the HIS involved interoperate. In a previous work, we proposed an interoperability architecture based on semantic web services. This solution has the advantage of not modifying the current organization of health centers. Indeed, for a complex query, a composition of web services is a solution to satisfy different needs. In this paper, we detail our approach for composing semantic web services. In our approach, the functionalities of each HIS application will be implemented by a web service semantically annotated by an ontology. An ontology-based mediation service is used to enrich the physician query and the MiniCon algorithm to create a composite web service. The composite web service is executed to return the requested data.

Hybridizing Artificial Bee Colony with Bat Algorithm for Web Service Composition

In the Internet of Things (IoT), the users have complex needs, and the Web Service Composition (WSC) was introduced to address these needs. The WSC’s main objective is to search for the optimal combination of web services in response to the user needs and the level of Quality of Services (QoS) constraints. The challenge of this problem is the huge number of web services that achieve similar functionality with different levels of QoS constraints. In this paper, we introduce an extension of our previous works on the Artificial Bee Colony (ABC) and Bat Algorithm (BA). A new hybrid algorithm was proposed between the ABC and BA to achieve a better tradeoff between local exploitation and global search. The bat agent is used to improve the solution of exhausted bees after a threshold (limits), and also an Elitist Strategy (ES) is added to BA to increase the convergence rate. The performance and convergence behavior of the proposed hybrid algorithm was tested using extensive comparative experiments with current state-of-the-art nature-inspired algorithms on 12 benchmark datasets using three evaluation criteria (average fitness values, best fitness values, and execution time) that were measured for 30 different runs. These datasets are created from real-world datasets and artificially to form different scale sizes of WSC datasets. The results show that the proposed algorithm enhances the search performance and convergence rate on finding the near-optimal web services combination compared to competitors. The Wilcoxon signed-rank significant test is used where the proposed algorithm results significantly differ from other algorithms on 100% of datasets.

Neighborhood Search Based Improved Bat Algorithm for Web Service Composition

Web services are provided as reusable software components in the services-oriented architecture. More complicated composite services can be combined from these components to satisfy the user requirements represented as a workflow with specified Quality of Service (QoS) limitations. The workflow consists of tasks where many services can be considered for each task. Searching for optimal services combination and optimizing the overall QoS limitations is a Non-deterministic Polynomial (NP)-hard problem. This work focuses on the Web Service Composition (WSC) problem and proposes a new service composition algorithm based on the micro-bats behavior while hunting the prey. The proposed algorithm determines the optimal combination of the web services to satisfy the complex user needs. It also addresses the Bat Algorithm (BA) shortcomings, such as the tradeoff among exploration and exploitation searching mechanisms, local optima, and convergence rate. The proposed enhancement includes a developed cooperative and adaptive population initialization mechanism. An elitist mechanism is utilized to address the BA convergence rate. The tradeoff between exploration and exploitation is handled through a neighborhood search mechanism. Several benchmark datasets are selected to evaluate the proposed bat algorithm’s performance. The simulation results are estimated using the average fitness value, the standard deviation of the fitness value, and an average of the execution time and compared with four bat-inspired algorithms. It is observed from the simulation results that introduced enhancement obtains significant results.

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.

An Improved Whale Optimization Algorithm for Web Service Composition

In the current circumstance, the Web Service Composition (WSC) was introduced to address complex user needs concerning the Quality of Services (QoS). In the WSC problem, the user needs are divided into a set of tasks. The corresponding web services are retrieved from the web services discovery according to the functionality of each task, and have different non-functional constraints, such as QoS. The WSC problem is a multi-objective optimization problem and is classified as an NP-hard problem. The whale optimization algorithm (WOA) is proven to solve complex multi-objective optimization problems, and it has the advantage of easy implementation with few control parameters. In this work, we contribute to improving the WOA algorithm, where different strategies are introduced to enhance its performance and address its shortcomings, namely its slow convergence speed, which produces low solution accuracy for the WSC problem. The proposed algorithm is named Improved Whale Optimization Algorithm (IWOA) and has three different strategies to enhance the performance of the WOA. Firstly, the Sine chaos theory is proposed to initiate the WOA’s population and enhance the initialization diversity. Secondly, a Lévy flight mechanism is proposed to enhance the exploitation and exploration of WOA by maintaining the whales’ diversity. Further, a neighborhood search mechanism is introduced to address the trade-off between exploration and exploitation searching mechanisms. Different experiments are conducted with datasets on 12 different scales (small, medium, and large), and the proposed algorithm is compared with standard WOA and five state-of-the-art swarm-based algorithms on 30 different independent runs. Furthermore, four evaluation criteria are used to validate the comparison: the average fitness value, best fitness values, standard deviation, and average execution time. The results show that the IWOA enhanced the WOA algorithm’s performance, where it got the better average and best fitness values with a low variation on all datasets. However, it ranked second regarding average execution time after the WOA, and sometimes third after the WOA and OABC, which is reasonable because of the proposed strategies.

Method for developing and modelling composite web-services

The article describes a method for developing and modelling composite web-services. Web-service composition is used to derive new functionality from the interaction of existing web-services. Composite web-services are built in several stages: specifications (determining the type of service); development of the structure of the service based on the algebra of services; service composition modelling; selecting a service variant and generating a service. The main elements of the proposed approach are the algebra of services and web-services interaction models (basic and composite). The above approach formally presents the consideration of the main aspects in solving problems related to the construction of effective composite web-services and the selection of mathematical models, namely: description of web-services, determining the structure of web-services, taking into account the dynamics of information changes, taking into account the main uncertainties in building the structure web-services. Based on the approach, a method for constructing composite web-services has been developed. The method is based on an algebraic description of a web-service based on a specialized algebra of services. The method consists of the following steps: description of the task of developing a web-service; development of the structure of a web service based on the algebra of services; formal description of the structure and clarification of service operations; building models of functioning and interaction of service components; building simulation models of the composite service; defining the final structure of the composite service; web-service implementation. The main tool for describing composite services is the algebra of services, and the model building tool is colouredPetri nets implemented using СPN Tools. As an example of the application of the developed method, the construction of a pharmaceutical service is described, in which the modellingof the interaction of web-services based on Petri nets is used. The process of building a composite model of a web-service is considered. The process begins with building a pharmaceutical web-service model structure based on the service algebra. Based on the service algebra, operators were developed to implement composite services. The structure of the model is developed, which is presented in the form of a formal description. The process of building pharmaceutical service models in the СPN Tools environment is described in detail.

Platform for Inter-Institutions E-Governance Based on a Dynamic Web Services Composition

Platform for Inter-Institutions E-Governance Based on a Dynamic Web Services Composition

WSFeIn: A Novel, Dynamic Web Service Composition Adapter for Cloud-Based Mobile Application

Service-Oriented Computing (SOC) has been a cornerstone development in today’s fast-paced world, covering the lifecycle of services and contributing to service delivery through distributed applications. SOC is striving to integrate cloud computing with complicated mobile apps. Dynamic and adaptable web service composition is the tip of the iceberg for SOA adoption. Dynamic service binding is vital for mobile computing due to the need for distributed mobile internet consumption at runtime. This study addresses SOC difficulties associated with web service composition, whose growth creates a paradigm change in identifying data type matching solutions. This allows for data type-level matching research to ensure high-quality web service creation. In this work, the composition process is divided into three phases: web service discovery, web service selection, and web service composition, where web service personalization and workflow reliability are emphasized. The final result is a complicated mobile app that runs without depleting device resources and an adaptable, reusable web service composition workflow. This improves matching at the data type level, an SOC pain point.

Priority-Based Selection of Individuals in Memetic Algorithms for Distributed Data-Intensive Web Service Compositions

In distributed computing, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Web Service Composition (WSC)</i> leads to the effective reuse of existing services and produces added value. WSC must fulfil functional requirements and optimise <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Quality of Service (QoS)</i> attributes, simultaneously. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Memetic Algorithms (MAs)</i> are promising for automatically composing numerous Web services to satisfy the above requirements. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Data-intensive Web services</i> focus on providing and updating data with a significant volume of data operation and exchange. However, current composition approaches have ignored the impact of data communication and the distribution of services, which significantly affect the performance when applied to the challenging <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Distributed Data-intensive Web Service Composition (DDWSC)</i> problem. Although recent approaches have revealed the usefulness of local search, they have completely overlooked the question of preferring appropriate composition solutions for local search. To address this research issue, we propose a priority-based selection method for the local search that can be consistently integrated with any MA for DDWSC. This enables us to develop state-of-the-art algorithms for DDWSC by explicitly considering the problem-specific, population and solution-related information for choosing a solution. Extensive experimental evaluation using benchmark datasets shows that our proposed method significantly outperforms several recently proposed methods.

Proposition of Web Services Discovery and Composition Approach: Application in E-learning Platform

The rapid penetration of Web technologies and strong economic pressure have led to an irresistible proliferation of Web services. In this paper, we are interested in the discovery and composition of Web services. In this sense, we propose a conceptual architecture and technical tools to carry out our approach. The originality of the proposed solution lies in the use of mixed tools ranging from multi-agents systems to Business Process Management (BPM) techniques, including Matchmaking algorithms and semantic models.