Service Composition Research Articles

Efficient Composition Algorithm Strategies on Semantic Web Services Using Nature Inspired Approach

Web services are used all around the world for commercial purposes. It works majorly by Universal Resource Locators, which are stored in the repository UDDI. The different web service has to be composed for creating new business services. It is time dependent for composing more web services, which is massive. A semantically described WSDL description solves automatic composition of web services. Also with ontological models and languages, such as RDF, OWL, and SWRL, the better automatic discovery, selecting, integration, and composition of services have been done. Service requirements, such as connectivity, quality-of-service properties, correctness, and scalability, need to be satisfied. For achieving these, many evolutionary algorithms will be used for improving quality of service parameters. This paper describes many optimizations algorithms which will be used efficiently in optimizing composition techniques in semantic web.

Identity Management in Future Railway Mobile Communication System

The Future Railway Mobile Communication System (FRMCS) has emerged as a worldwide standard for railway communication. This technology enables the operational efficiency and safety of railways to be improved by providing mission critical communications, machine-type communication for the railway system on board, in addition to trackside telemetry and broadband connectivity for passengers. Different equipment types, users, and functional identities can be involved in communication, and each of them is uniquely identified. Identity management is an important part of the security functions provided by the FRMCS system. This paper presents a service-oriented approach to identity management functionality, enabling service composition for railway applications and service virtualization. This paper studies functionality for the initial registration and subsequent deregistration of railway devices, users, and their functional identities, in addition to the transfer of the registered identities between different FRMCS serving areas while the train moves. Two FRMCS services that follow the principles of representational state transfer architecture are proposed. Services’ functionality is illustrated by use cases, data types, and application programming interfaces that enable services to be interacted with. Identity registration status models are developed, formally described, and mathematically verified. Discussion of the applicability of the proposed services for the implementation of FRMCS security and safety functions is provided. The presented service-oriented approach features a satisfactory level of flexibility and versatility.

PPDRL: A Pretraining-and-Policy-Based Deep Reinforcement Learning Approach for QoS-Aware Service Composition

Service composition is a mainstream paradigm for rapidly constructing large-scale distributed applications. QoS-aware service composition, i.e., selection of the optimal execution plan that maximizes the composition’s end-to-end QoS properties, is an active area of research and development endeavors in service composition. In this paper, we propose PPDRL, a pretraining-and-policy-based deep reinforcement learning approach, to solve the QoS-aware service composition problem. Its significant feature is to incorporate a maximum likelihood estimate and a policy scoring mechanism into a deep reinforcement learning framework. As a result, our approach can balance the exploitation and exploration efforts adaptively and can search for the solution space in a robust and efficient manner. We have executed our approach to solve 6 randomly generated QoS-aware service composition problems with different sizes and structures based on QWS data set including 2,507 real Web services classified into 233 categories. The results indicate that our approach can find near-optimal solutions within moderate numbers of iteration and has performance superiority in comparison with five state-of-the-art algorithms.

Fuzzy Based Optimal QoS Constraint Services Composition in Mobile Ad Hoc Networks

In recent years, computational capability of mobile devices such as Laptops, mobile phones, PDAs, etc., are greatly improved. Implementation of SOA ("Service Oriented Architectures") in mobile ad hoc networks increases the flexibility of using mobile devices. On composing different available services to satisfy end user requirement is a critical challenge in MANETs ("Mobile Ad Hoc Network") due to dynamic topology, Resource heterogeneity, Band width constraint and highly distributed service providers. Existing composition services approaches are not suitable for MANETs due to lack of constraints consideration while choosing services. In this paper, we proposed Fuzzy based optimal QoS constrained Service Composition in MANETs. We consider Energy constraint, hop count, Response time & service throughput as QoS Constraints composing optimal services. We proposed fuzzy logic based system to provide rating to the services for optimal selection of services. We also considered that each node can provide one or more services. The service composition failure rate will be reduced by selecting optimal services in available services. The simulation result demonstrates that the proposed method outperformed than the traditional AODV in terms of average packet delay, energy constraint, throughput and turnaround time.

Local Consistency Reinforcement for Enhancing Web Service Composition

Composing Web services to fulfill a user query remains one of the most challenging research problems due to its importance to the world economy. Several composition techniques have been proposed, but these techniques are becoming more and more expensive due to the tremendous growth of data over the internet, the intensive use of services, and the continuous changes of available services. The workflow-based Web service composition problem is an NP-complete problem, due mainly to the property of the workflow structuring the composition and the diversity of underlying constraints. Reinforcing local consistency is one of the well-known pre-processing techniques to reduce the complexity of most NP-complete problems. These techniques are mainly dedicated to binary constraint satisfaction problems. Therefore, only few researchers devoted their efforts toward reinforcing some level of local consistency on Web service composition problems that can be semantically defined in the Ontology Web Language for Web Services (OWL-S) control structure based constraints. The main goal of this paper is to propose a novel approach for reinforcing a reasonable level of local consistency, node and arc-consistency on any Web service composition problems. It is expected to provide a more up-to-date and realistic representation of any Web service composition problems without any user interactions, but with the ability to cope with any types of user’s query, such as incomplete, ambiguous, and others. This is due to the existence of several novel rules for reinforcing node and arc-consistency on all types of control structure constraints. Experiments were performed on random problems of different levels of complexity to evaluate the performance of the proposed approach.

Optimal composition of solo and pool services for on-demand ride-hailing

On-demand ride-hailing services are becoming a ubiquitous means of mobility in major cities. While convenient and accessible, they are costly for many, and thus ride-hailing providers are often offering pooling options that allow passengers to share a ride and split the fare. The downside of pooling is the extra waiting time passengers may experience as drivers make detours to find and board additional passengers. This paper assesses the competitive advantage of solo (single occupancy) and pool services and investigates the potential benefits of offering both. We characterize the search friction using bilateral meeting functions and present a closed queueing system to capture the pooling effects. We then derive the optimal fare, vehicle occupancy and fleet size for solo and pool services. In symmetric-elasticity meeting functions, we analytically show that (i) the pool service is profitable only when there is increasing returns-to-scale in matching, (ii) the solo service is profitable when the marginal fleet acquisition cost is low, and (iii) the provider should offer both solo and pool services only when there is decreasing returns-to-scale in matching. These observations are further validated numerically for more general cases.

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.

Knowledge-Driven Automated Service Composition as a Method for Developing Decision Support Systems

A decision support system often necessitates a large amount of effort from both a domain modeling and technological standpoint. Using automated service composition, the study provides a method for minimizing the complexity of decision support system development. The justification for the approach is that many software systems (including decision support systems) today are based on service-oriented architecture, and the development of such systems may be described to some extent as a building composition of services that meet the required requirements. Automated planning algorithms can successfully address the difficulty of creating such compositions. The functional framework of decision support systems, requirements analysis for configurable service-oriented decision support systems and its primary components, and a conceptual model of a configurable service-oriented decision support system are all presented in the article.

Dynamic QoS/QoE-aware reliable service composition framework for edge intelligence.

Edge intelligence has become popular recently since it brings smartness and copes with some shortcomings of conventional technologies such as cloud computing, Internet of Things (IoT), and centralized AI adoptions. However, although utilizing edge intelligence contributes to providing smart systems such as automated driving systems, smart cities, and connected healthcare systems, it is not free from limitations. There exist various challenges in integrating AI and edge computing, one of which is addressed in this paper. Our main focus is to handle the adoption of AI methods on resource-constrained edge devices. In this regard, we introduce the concept of Edge devices as a Service (EdaaS) and propose a quality of service (QoS) and quality of experience (QoE)-aware dynamic and reliable framework for AI subtasks composition. The proposed framework is evaluated utilizing three well-known meta-heuristics in terms of various metrics for a connected healthcare application scenario. The experimental results confirm the applicability of the proposed framework. Moreover, the results reveal that black widow optimization (BWO) can handle the issue more efficiently compared to particle swarm optimization (PSO) and simulated annealing (SA). The overall efficiency of BWO over PSO is 95%, and BWO outperforms SA with 100% efficiency. It means that BWO prevails SA and PSO in all and 95% of the experiments, respectively.

Fair and energy-aware IoT service composition under QoS constraints

Fair and energy-aware IoT service composition under QoS constraints

Dynamic service composition framework for service‐oriented architectures based e‐government in Cameroon

AbstractE‐government has become a strategic tool for managing public administration. It makes it possible to improve interoperability between government's computer systems agencies. Taking profit to technological advances, notably the internet and web services, it has been associated with service‐oriented architectures (SOA). Service‐oriented e‐government has thus grown rapidly in developed countries. He has also gradually established itself in many projects in developing countries to take advantage to the economic benefits derived from SOA. However, when the activity of stakeholders becomes important in an e‐government based on SOA, the scalability becomes a major challenge. This is even more striking in developing countries, which suffer from less robust infrastructures and poor energy supply. The scalability has its origins mainly in dynamic service composition operations. This is why this work proposes to improve the dynamic service composition layer to deal with the scalability in case of service‐oriented e‐government in developing countries. This makes it possible to construct and dynamically enrich each system with a service composition server, a registry and a library of requests associated with their composite services. Thus, each system checks its ability to process a request locally before sending it to a higher‐level system. The paper demonstrates that using this participatory approach, the latency of the majority of end‐user requests is significantly shortened. Beyond this important aspect, our approach can provide with a good solution for Cameroon suffer from infrastructural insufficiency and energy instability. The evaluation of this architecture gave us satisfactory results.

SCM-IoT: An Aproach for Internet of Things Services Integration and Coordination

Today, new applications demand an internet of things (IoT) infrastructure with greater intelligence in our daily use devices. Among the salient features that characterize intelligent IoT systems are interoperability and dynamism. While service-oriented architectures (SOA) offer a well-developed and standardized architecture and protocols for interoperability, answering whether SOA offers enough dynamism to merge IoT with artificial intelligence (AI) is still in its beginnings. This paper proposes an SOA model, called SCM-IoT (service composition model for IoT), for incorporating AI into IoT systems, addressing their coordination by a mediator offering services for storage, production, discovery, and notification of relevant data for client applications. The model allows IoT systems to be incrementally developed from three perspectives: a conceptual model, platform-independent computational model, and platform-dependent computational model. Finally, as a case of study, a domotic IoT system application is developed in SCM-IoT to analyze the characteristics and benefits of the proposed approach.

Cloud supply chain: Integrating Industry 4.0 and digital platforms in the “Supply Chain-as-a-Service”

In this paper, we introduce cloud supply chain which is a business model based on cloud-enabled networking of some third-party physical and digital assets to design and manage a supply chain network. Cloud supply chain integrates concepts and technology of Industry 4.0 and digital platforms emerging in the “supply chain-as-a-service” paradigm. This paper conceptualizes the cloud supply chain as a new and distinct research area. Through analysis of practical cases, we deduce some generalized characteristics of the cloud supply chain. In the generalised model, we formalize supply chain multi-structural dynamics and dynamic service composition. Our results show that the main generalized characteristics of the cloud supply chain are related to (i) multi-structural dynamics; (ii) platforms, digital supply chains, ecosystems, and visibility, (iii) dynamic service composition with dynamically changing buyer/supplier roles, (iv) resilience and viability, and (v) intertwined supply networks and circular economy. We close by discussing future research directions including novel context of Industry 5.0.

Web Service Composition Optimization with the Improved Fireworks Algorithm

Even though the number of services is increasing, a single service can just complete simple tasks. In the face of complex tasks, we require composite multiple services to complete them. For the purpose of improving the efficiency of web service composition, we propose a service composition approach based on an improved fireworks algorithm (FWA++). First, we use the strategy of random selection to keep N − 1 individuals for the next generation, and the purpose is to speed up the convergence speed of the FWA++ and enhance the search ability. Second, we randomize the total number of sparks and maximum amplitude of sparks for each generation. In this way, the search ability and the ability to jump out of the local optimal solution are dynamically balanced throughout the execution of the algorithm. Our experimental results show that compared with other existing approaches, the approach proposed in this paper is more efficient and stable.

An extended artificial bee colony with local search for solving the Skyline-based web services composition under interval QoS properties

In this paper, we introduce an extended version of artificial bee colony with a local search method (EABC) for solving the QoS uncertainty-aware web service composition (IQSC) problem, where the ambiguity of the QoS properties are represented using the interval-number model. At first, we formulate the addressed problem as an interval constrained single-objective optimization model. Then, we use the skyline operator to prune the redundant and dominated web services from their sets of functionally equivalent ones. Whereas, EABC is employed to solve the IQSC problem in a reduced search space more effectively and more efficiently. For the purpose of validation of the performance and the efficiency of the proposed approach, we present the experimental comparisons to an existing skyline-based PSO, an efficient discrete gbest-guided artificial bee colony and a recently provided Harris Hawks optimization with an elite evolutionary strategy algorithms on an interval extended version of the public QWS dataset.

SC-TRUST: A Dynamic Model for Trustworthy Service Composition in the Internet of Things

A future Internet of Things (IoT) will feature a service-oriented architecture consisting of lightweight computing platforms offering individual, loosely coupled microservices. Often, an end-user will request a bespoke service that will require a composition of two or more microservices offered by different service providers. However, the underlying complexities of soft compositions and the increased security risks are inherent in such a massively decentralized and distributed architecture. The use of trust management to secure the IoT is well studied in the literature. However, there are limitations to its use in service compositions in the IoT. Specifically, transparent (agnostic) trust composition and decomposition remain key problems for this area. A novel model for trustworthy service compositions in the IoT, SC-TRUST, is therefore proposed to deal with these challenges. In this study, the trust properties of service compositions and the effect of service workflows on transparent trust composition and decomposition are investigated. Based on the findings, relevant trust evaluation functions are derived to guide the compositions. SC-TRUST was implemented in a suitable application and its performance, in terms of the utility derived and the trust accuracy, convergence, and resiliency, was evaluated. The results show that SC-TRUST improves the quality of service compositions and adequately mitigates trust-related attacks, thus increasing both efficiency and security.

Multiservice Reliability Evaluation Algorithm Considering Network Congestion and Regional Failure Based on Petri Net

With the development of complex networks and with increasing service demands, service use is becoming more complex and the composition of services is becoming more complicated. In the XaaS (X as a Service) environment, users only care about the QoE of a service and do not care about the composition process of the service. Therefore, it is important to evaluate the reliability of the entire service. In this article, we use Petri Net as a basis for modeling the composition of services. In addition, we consider the problems of shared resources and common cause faults. Both of these problems can cause network congestion and regional failures. We use distance to assess the effects of regional faults and queuing theory to simulate the network congestion process. Moreover, in the simulation, we verify the impacts of regional failures and network congestion on service reliability. We choose the Tree-Based Search algorithm and the Semi-Markov Model as comparison algorithms. The results of our algorithm are related to service time. Our algorithm can timely reflect the impact of regional failure or network congestion, and it can feedback different evaluation results according to environmental changes. Therefore, our algorithm is more comprehensive and has better performance.

Social-Sensor Composition for Tapestry Scenes

The extensive use of social media platforms and overwhelming amounts of imagery data creates unique opportunities for sensing, gathering and sharing information about events. One of its potential applications is to leverage crowdsourced social media images to create a tapestry scene for scene analysis of designated locations and time intervals. The existing attempts however ignore the temporal-semantic relevance and spatio-temporal evolution of the images and direction-oriented scene reconstruction. We propose a novel social-sensor cloud (SocSen) service composition approach to form tapestry scenes for scene analysis. The novelty lies in utilising images and image meta-information to bypass expensive traditional image processing techniques to reconstruct scenes. Metadata, such as geolocation, time and angle of view of an image are modelled as non-functional attributes of a SocSen service. Our major contribution lies on proposing a context and direction-aware spatio-temporal clustering and recommendation approach for selecting a set of temporally and semantically similar services to compose the best available SocSen services. Analytical results based on real datasets are presented to demonstrate the performance of the proposed approach.

An approach based on genetic algorithms and neural networks for QoS-aware IoT services composition

In the Internet of Things (IoT) environment, the services provided by the connected objects are published as services through the web. This allows to machines to interact between them and, makes the IoT services composition possible. However, the vast proliferation of smart object generates services with the same functionalities but different in terms of quality of services (QoS) proprieties. This makes the satisfaction of the user requirements often complex and a NP-hard problem. Indeed, respecting the QoS constraints (user preferences in terms of QoS) is a challenge, due to the high number of candidate services for the composition. This challenge consists of selecting the most appropriate services so that the composite service must meet both the functional and the non-functional requirements of the user. To deal with this challenge, we propose an approach based on Genetic Algorithms (GA) and Neural Networks (NN) for QoS-aware IoT Services Composition in the context of large-scale environments. The combination between GA and NN allows finding the quasi-optimal IoT service composition. This latter is based on global QoS optimization. To reach this objective, the QoS intervals are decomposed into M QoS-levels to engage them into the theoretical composition. Then, the proposed first GA is used to obtain the ideal theoretical composition with an overall QoS optimization. Afterward, the proposed NN is used to eliminate the inappropriate concrete IoT services, and retain only the services having the same categories of the atomic theoretical services composing the ideal theoretical composition. This allows us to optimize the search space as well as the execution time. Finally, we apply the second GA on the concrete services of the retained categories, in order to obtain the IoT service concrete composition with an overall QoS optimization. The simulation results show that the proposed approach has the best composition time, the best Hypervolume indicator and the best compositional optimality compared to SC-FLA, Improved GA and MGA approaches. On another side, it has almost the same performances compared to TS-QCA and, it finds the near-optimal composition in a very short time compared to PSA, which is an optimal approach. Thus, the obtained results show the effectiveness of our approach.

A web service composition framework in a heterogeneous environment

A web service composition framework in a heterogeneous environment