Service Composition Approach Research Articles

An innovative approach for QoS-aware web service composition using whale optimization algorithm

With the proliferation of services and the vast amount of data produced by the Internet, numerous services with comparable functionalities but varying Quality of Service (QoS) attributes are potential candidates for meeting user needs. Consequently, the selection of the most suitable services has become increasingly challenging. To address this issue, a synthesis of multiple services is conducted through a composition process to create more sophisticated services. In recent years, there has been a growing interest in QoS uncertainty, given its potential impact on determining an optimal composite service, where each service is characterized by multiple QoS properties (e.g., response time and cost) that are frequently subject to change primarily due to environmental factors. Here, we introduce a novel approach that depends on the Multi-Agent Whale Optimization Algorithm (MA-WOA) for web service composition problem. Our proposed algorithm utilizes a multi-agent system for the representation and control of potential services, utilizing MA-WOA to identify the optimal composition that meets the user’s requirements. It accounts for multiple quality factors and employs a weighted aggregation function to combine them into a cohesive fitness function. The efficiency of the suggested method is evaluated using a real and artificial web service composition dataset (comprising a total of 52,000 web services), with results indicating its superiority over other state-of-the-art methods in terms of composition quality and computational effectiveness. Therefore, the proposed strategy presents a feasible and effective solution to the web service composition challenge, representing a significant advancement in the field of service-oriented computing.

An efficient cloud manufacturing service composition approach using deep reinforcement learning

Cloud Manufacturing (CMfg) revolutionizes manufacturing by providing resources as cloud-based services. The main challenge in CMfg is identifying the best combination of these services to meet customer needs efficiently. This paper explores the potential of Deep Reinforcement Learning (DRL), a technology with successful applications in fields such as healthcare and transportation, to tackle scheduling challenges in CMfg. A tailored DRL environment for CMfg is introduced, along with a novel DRL-based algorithm designed to optimize service composition in CMfg. Comparative evaluations against existing algorithms, both in the new DRL-based CMfg environment and a benchmark DRL environment, consistently showed the superior performance of the proposed algorithm. Additionally, the study analyzes different combinations of weight coefficients and probabilities of service failure within the new CMfg environment, revealing their significant impact on scheduling outcomes. These findings underscore both the robustness and efficiency of the proposed solution, showcasing DRL’s potential to significantly enhance CMfg operations.

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.

A New Integrated Approach for Cloud Service Composition and Sharing Using a Hybrid Algorithm

The concept of a “Smart City” emphasizes the need to employ information and communication technologies to strengthen the quality, connectivity, and efficiency of various municipal services. Cloud computing and the Internet of Things are shaping future tech. Both ideas greatly impact smart city application and solution development. Cloud computing is amazing at managing and storing remote service access. Several companies have switched to cloud leasing to reduce local resource burden. Due to the intricacy and flexibility of cloud-maintained services, selecting jobs that best suit client needs should be optimized. Quality of service criteria for each cloud service are the best tools for choosing and optimizing cloud carriers. Genetic algorithms (GAs) and ant colony optimization (ACO) are combined to make cloud computing. It is discovered that the recommended ACO + GA obtains an accuracy of 82% when compared to existing methods of energy- and reliability-aware multiobjective optimization method and the hybrid cuckoo particles swarm, artificial bee colony optimization (CPS + ABCO) where accuracy is 68% and 75%, respectively.

PrSChain: A Blockchain Based Privacy Preserving Approach for Data Service Composition

PrSChain: A Blockchain Based Privacy Preserving Approach for Data Service Composition

Detection and filling of functional holes in microservice systems: Method and infrastructure support

With the widespread use of microservices technology, a growing number of Microservice Systems (MSS) have emerged. Monolithic applications are divided into several small and independent microservices that provide functions through APIs. These microservices can be orchestrated by service composition to satisfy various user requirements: microservice functionalities are aggregated into coarse-grained solutions to provide composite functions to users. However, although various service composition approaches have been presented in many works of literature, they failed to solve the situation that no feasible solutions can be found because of missing functions in MSS, named Functional Hole (FH). As a result, the system cannot satisfy user requirements and faces Quality of Service (QoS) declines. In this paper, to reduce the impact of missing functions, we defined the FH to describe the absence of functions for MSS when it cannot satisfy user requirements. Moreover, for the first time, we proposed the Detection and Filling Problem of FH (DFPFH) in a running MSS. A three-phase algorithm with supporting infrastructure was developed to solve DFPFH. It detects FHs based on hypergraphs, fills FHs with services from an external service system at runtime, and generates suggestions for thoroughly filling FH to developers. Plenty of experiments were conducted, and the results validate our approaches’ usability, effectiveness, and performance.

Using an Estimation of Distribution Algorithm to Achieve Multitasking Semantic Web Service Composition

Web service composition composes existing web services to accommodate users’ requests for required functionalities with the best possible quality of services (QoS). Due to the computational complexity of this problem, evolutionary computation (EC) techniques have been employed to efficiently find composite services with near-optimal functional quality (i.e., quality of semantic matchmaking, QoSM for short) or non-functional quality (i.e., QoS) for each composition request individually. With a rapid increase in composition requests from a growing number of users, solving one composition request at a time can hardly meet the efficiency target anymore. Driven by the idea that the solutions obtained from solving one request can be highly useful for tackling other related requests, multitasking service composition approaches have been proposed to efficiently deal with multiple composition requests concurrently. However, existing attempts have not been effective in learning and sharing knowledge among solutions for multiple requests. In this paper, we model the problem of collectively handling multiple service composition requests as a new multi-tasking service composition problem and propose a new Permutation-based Multi-factorial Evolutionary Algorithm based on an Estimation of Distribution Algorithm (EDA), named PMFEA-EDA, to effectively and efficiently solve this problem. In particular, we introduce a novel method for effective knowledge sharing across different service composition requests. For that, we develop a new sampling mechanism to increase the chance of identifying high-quality service compositions in both the single-tasking and multitasking contexts. Our experiment shows that our proposed approach, PMFEA-EDA, takes much less time than existing approaches that process each service request separately, and also outperforms them in terms of both QoSM and QoS.

Formal Analysis of Trust and Reputation for Service Composition in IoT

The exponential growth in the number of smart devices connected to the Internet of Things (IoT) that are associated with various IoT-based smart applications and services, raises interoperability challenges. Service-oriented architecture for IoT (SOA-IoT) solutions has been introduced to deal with these interoperability challenges by integrating web services into sensor networks via IoT-optimized gateways to fill the gap between devices, networks, and access terminals. The main aim of service composition is to transform user requirements into a composite service execution. Different methods have been used to perform service composition, which has been classified as trust-based and non-trust-based. The existing studies in this field have reported that trust-based approaches outperform non-trust-based ones. Trust-based service composition approaches use the trust and reputation system as a brain to select appropriate service providers (SPs) for the service composition plan. The trust and reputation system computes each candidate SP's trust value and selects the SP with the highest trust value for the service composition plan. The trust system computes the trust value from the self-observation of the service requestor (SR) and other service consumers' (SCs) recommendations. Several experimental solutions have been proposed to deal with trust-based service composition in the IoT; however, a formal method for trust-based service composition in the IoT is lacking. In this study, we used the formal method for representing the components of trust-based service management in the IoT, by using higher-order logic (HOL) and verifying the different behaviors in the trust system and the trust value computation processes. Our findings showed that the presence of malicious nodes performing trust attacks leads to biased trust value computation, which results in inappropriate SP selection during the service composition. The formal analysis has given us a clear insight and complete understanding, which will assist in the development of a robust trust system.

Scalable and Optimal QoS-Aware Manufacturing Service Composition via Business Process Decomposition

With the adoption of service-oriented manufacturing modes, more and more manufacturing services are released over manufacturing service platforms. As it is known, the problem of the QoS (Quality of Service)-aware manufacturing service composition is NP-hard. Thus, the optimization remains a challenging research issue, especially in the situation of large-scale manufacturing service data which arouse a scalability problem as well. To improve both the optimization performance and scalability of the QoS-aware manufacturing service composition, this paper proposes a scalable and optimal QoS-aware manufacturing service composition approach via business process decomposition. Specifically, the service composition process is decomposed by using a refined process structure tree (RPST). Moreover, an optimized service composition is achieved layer by layer based on the refined process structure tree in a bottom-up manner. For the atomic tasks or the compound tasks in the same layer of RPST, the corresponding QoS-aware service selection is optimized by calculating Skyline services, which can be carried out in parallel if necessary. When the optimization arrives at the root node, the complete service composition plans are derived. In our approach, the optimal manufacturing service candidates are picked out stage by stage. In this way, both the optimality and scalability of the whole approach can be guaranteed. Extensive experiments are conducted to verify the optimality and scalability of our approach.

From Service Composition to Mashup Editor: A Multiperspective Taxonomy

Service-oriented computing has become a popular area of research, with a particular focus on service composition. There have been many developments in this field, such as new techniques for data engineering in service description languages, protocols for publication and discovery, the optimization of service selection and scheduling, and the deployment and monitoring of composed services. However, this diversity of approaches and methodologies can make it challenging to navigate between different proposed solutions and identify research gaps. In order to provide a clearer understanding of this body of work, this paper presents a comprehensive framework for the taxonomy of service composition approaches, methodologies, and tools. This framework proposes a structured view of different perspectives, such as formal, semantic, and automatic approaches, with a particular focus on the end-user’s perspective and tools such as Mashups.

An efficient clustering mechanism towards large scale service composition in IoT

Internet of things (IoT) applications hinder heterogeneity at different levels, such as device level, usage level, and communication level. In this context, service-oriented architecture (SOA) facilitates limited supports for handling heterogeneity in IoT-based applications. This article proposes a clustered hypergraph colouring (CHC) approach for designing large-scale SOA (LSS). Two noble approaches (service clustering and composition) are discussed in LSS for satisfying the service consumers (SCs) requirements. A service clustering approach is presented for the clustering of homogeneous services. This mechanism enables to reduce the search time for the service composition mechanism. A service composition approach is described with some parameters. Further, the proposed approach is validated through an experimental setup by considering the factors such as the number of services, execution time, and memory size. The execution time of LSS is minimised as compared to normal or medium-scale SOA (NSS) due to the service clustering mechanism. The novelty of this work is to minimise the execution time in the large service domain during service composition.

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 Adaptive Multiobjective Multitask Service Composition Approach Considering Practical Constraints in Fog Manufacturing

Fog manufacturing (FMfg) is an emerging real-life cloud manufacturing (CMfg) paradigm based on digital twin models, which perceive finely dynamic processes and provide on-demand optimization services. Service composition as a core plays a vital role in achieving the optimization goal. Service composition issues in actual CMfg are mostly multitask problems that need considering dynamic factors. Otherwise, the planned production process will deviate significantly from actual situations. However, such multitask service composition (MTSC) models have scarcely been studied. Thus, this article formulates a multiobjective MTSC model considering the crucial resource competition process between multitasks under multiple practical constraints (MPC). Then, to solve the MTSC–MPC, this article develops an adaptive multiobjective whale optimization algorithm (AMOWOA) containing well-designed strategies. Numerical experiments and application cases verify that AMOWOA outperforms the comparison algorithms. AMOWOA continually can optimize and adaptively adjust the service composition process based on the crucial manufacturing resource available time under actual constraints in FMfg environment.

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%.

Coordinate System-Based Trust-Aware Web Services Composition in Edge and Cloud Environment

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

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.

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.

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.

Cognitive Architecture Configuration Model for Performing Dynamic Pervasive Service Composition

With the breakthroughs in capabilities of mobile and embedded devices which allows them to become part of our everyday lives, and the subsequent advent of fields such as Internet of Things, Smart Cities and Ambient Intelligence, the process of exploiting individual device functionalities to create complex services, known as service composition, gains more traction. However, to perform service composition in such dynamic environments, where availability of resources is far from uncertain, there are many challenges that must be overcome. The way the human brain works allows it to configure itself to perform specific tasks, just like a service composition arranges a set of services to achieve a goal, and is capable to adapt to changes in its environment, providing us with a great degree of resilience, something very valuable for dealing with dynamic environments. In this work, we propose a model whereby the various modules of the Cuāyōllōtl Biologically Inspired Cognitive Architecture (BICA), which will be in charge of managing the different devices of a pervasive system, are to be configured to create a workflow that employs only the required modules to achieve an objective, in a similar fashion to how conventional service composition approaches produce composite services.

Suitability of self-organizing service composition approach for smart healthcare ecosystem: A study

Future IoT systems will be deployed in open environments where the functionality of millions of IoT devices that are heterogeneous will be abstracted. In such a large scale system manual service composition is not feasible and often erroneous. A self-organizing service composition is a well known approach to deal with the problems in IoT systems. In a self-organizing service composition, the service composition is a runtime and autonomous process and human intervention is minimal. The atomic components will interact among themselves in a decentralized manner to form complex composites according to a set of self-organizing rules. The features of a self-organizing software composition are aptly suitable for the IoT domain. Smart healthcare has provided affordable healthcare for patients and enables them to self manage emergencies. This paper aims to establish the suitability of a self-organizing service composition for the smart healthcare ecosystem with special focus on real time monitoring applications.