Service Composition Research Articles

Adaptive multi-objective service composition reconfiguration approach considering dynamic practical constraints in cloud manufacturing

Dynamic uncertainty factors such as equipment faults are common in practically implemented cloud manufacturing (CMfg) environments, often causing the manufacturing service to be invalidated. In that case, efficient reconfiguration of the original service composition under practical constraints is critical; however, existing research scarcely focuses on it. This paper proposes a dynamic service composition reconfiguration model to bridge the gap by considering practical constraints (DSCRPC) in a real-life cloud manufacturing environment. Based on the constraints considered in this study, the DSCRPC model redefines three objectives: time (T*), cost (C*), and product service quality (Q*S*). To optimize the DSCRPC model, this study developed an adaptive multi-population multi-objective whale optimization algorithm (AMPOWOA) based on the Pareto strategy. The algorithm adopts four balancing strategies and adaptively optimizes and adjusts the key parameters under various balancing strategies through well-designed reinforcement learning models. Finally, we conduct numerical experiments and actual application case tests to compare the performances of AMPOWOA and other algorithms (MOWOA, MOHHO, NSGA-II). The results show that DSCRPC can continuously tackle the cloud manufacturing service composition (CMSC) reconfiguration issue with constraints until an order is completed. Moreover, AMPOWOA is superior to the other algorithms optimizing the DSCRPC model. This significantly enhances the robustness of service composition reconfiguration in real-life CMfg.

Dynamic assessment of sustainable manufacturing capability based on correlation relationship for industrial cloud robotics

The industrial cloud robotics (ICRs) integrates distributed industrial robot resources in various places to support complex task processing for multi-resource service requirements, and manufacturing capability assessment plays an important role in determining the optimal service composition to realize the value-added of ICRs resources. However, ignoring correlation relationship within the robot composition, the traditional evaluation method cannot reflect the impact of the robot individuals on the overall manufacturing capability of the ICRs composition. In addition, the problems of excessive resource consumption and environmental pollution in the manufacturing industry are becoming increasingly serious. The paper proposes a dynamic assessment method of sustainable manufacturing capability for ICRs based on the correlation relationship. Firstly, an extensible multi-dimensional indicator system of sustainable manufacturing capability is constructed. Then, multiple composition correlation relationships among ICRs are analyzed to establish the correlation assessment model. Furthermore, a set of dynamic evaluation methods is proposed, in which raw data of the evaluation indicators is processed based on the service correlation model, and the traditional network analytic network process method is improved based on the data correlation model. Finally, a case study is implemented to show the reasonability and effectiveness of the proposed method in the assessment of sustainable manufacturing capability for ICRs.

Service-oriented architecture for Internet of Things: A semantic approach

The Internet of Things (IoT) aids an interconnection between systems, humans, and services to develop computation-intensive autonomous applications. Service-oriented architecture (SOA) concepts have been used as defacto software architecture to develop IoT-based systems. Though SOA facilitates various benefits, several challenges exist for integration with IoT-based systems, including configurability, interoperability, and manageability. The ontology-based approach provides a semantically enriched and rigorous platform for interoperating different heterogeneous IoT-based systems and applications. The semantic modelling research has focused on IoT resource management without concerning the method of accessing and utilizing IoT information. In this context, an IoT-based large-scale SOA (IoT-LSS) ontology is proposed that supports interoperability, heterogeneity, flexibility, manageability, extendibility, scalability. This ontology integrates the concept of large-scale SOA (LSS) with semantic sensor network (SSN) ontology. Clinical decision support system (CDSS) ontology has considered as a case study to illustrate the proposed ontology. A detailed comparative analysis between the proposed system and several existing IoT-based service ontologies has been performed based on different characteristics. This analysis shows that IoT-LSS supports dynamic features and exposes to service with its different mechanisms. This article presents an ontological framework for the IoT service-based environment to efficiently use for service composition mechanisms in the large service domain.

An Extensive Review of Web-Based Multi-Granularity Service Composition

The paper reviews the efforts to compose SOAP, non-SOAP and non-web services. Traditionally efforts were made for composite SOAP services, however, these efforts did not include the RESTful and non-web services. A SOAP service uses structured exchange methodology for dealing with web services while a non-SOAP follows different approach. The research paper reviews the invoking and composing a combination of SOAP, non-SOAP, and non-web services into a composite process to execute complex tasks on various devices. It also shows the systematic integration of the SOAP, non-SOAP and non-web services describing the composition of heterogeneous services than the ones conventionally used from the perspective of resource consumption. The paper further compares and reviews different layout model for the discovery of services, selection of services and composition of services in Cloud computing. Recent research trends in service composition are identified and then research about microservices are evaluated and shown in the form of table and graphs.

Research of the Service Composition Optimization Model Based on Petri Net

Cloud service systems bring together a wide variety of flexible and scalable mass services. The service combination scheme which can meet the user’s needs is provided to the user through the flexible and changing service mode of service combination technology. However, service combination failures often occur. In view of the above problems, this paper establishes the cloud service combination optimization model. Firstly, it is proposed that the Service Combination Optimization Petri Net models and analyzes the service selection and combination. In order to avoid local convergence, combining Service Combination Optimization Petri Net with improved Genetic Algorithm, a service combination model based on Local Search Operator Genetic Algorithm is proposed, and the legitimacy of service combination sequence is verified by Petri net. The results of experiment show that the Petri net service composition model can effectively verify the constraints of service composition and the logical rationality of the system, the Local Search Operator Genetic Algorithm effectively reduces the search space and improves the convergence rate. Compared with other algorithms, the execution efficiency and accuracy of Local Search Operator Genetic Algorithm are improved.

A novel and efficient index based web service discovery approach

Nowadays, with the expansion of the number of web services, the discovery and the selection of the latter are becoming complex tasks. To address this issue, different methods (indexing, clustering, database etc.) have been proposed in the literature so far. In this paper, we deal with index based methods to manage web service repositories. Indeed, using indexes perspective in web services discovery can help to promote the web service composition. Within this context, we propose a novel index method named Multi Level Sorted Index (MLSI) to improve the run time of the discovery process. The implementation and the simulation of our method have shown the efficiency of our approach in comparison with the inverted index and Multilevel Index Model (MLIM) in term of retrieval and addition times.

Global Optimization Algorithm for Cloud Service Composition

Global Optimization Algorithm for Cloud Service Composition

Multi-criteria Web Services Selection: Balancing the Quality of Design and Quality of Service

Web service composition allows developers to create applications via reusing available services that are interoperable to each other. The process of selecting relevant Web services for a composite service satisfying the developer requirements is commonly acknowledged to be hard and challenging, especially with the exponentially increasing number of available Web services on the Internet. The majority of existing approaches on Web Services Selection are merely based on the Quality of Service (QoS) as a basic criterion to guide the selection process. However, existing approaches tend to ignore the service design quality, which plays a crucial role in discovering, understanding, and reusing service functionalities. Indeed, poorly designed Web service interfaces result in service anti-patterns, which are symptoms of bad design and implementation practices. The existence of anti-pattern instances in Web service interfaces typically complicates their reuse in real-world service-based systems and may lead to several maintenance and evolution problems. To address this issue, we introduce a new approach based on the Multi-Objective and Optimization on the basis of Ratio Analysis method (MOORA) as a multi-criteria decision making (MCDM) method to select Web services based on a combination of their (1) QoS attributes and (2) QoS design. The proposed approach aims to help developers to maintain the soundness and quality of their service composite development processes. We conduct a quantitative and qualitative empirical study to evaluate our approach on a Quality of Web Service dataset. We compare our MOORA-based approach against four commonly used MCDM methods as well as a recent state-of-the-art Web service selection approach. The obtained results show that our approach outperforms state-of-the-art approaches by significantly improving the service selection quality of top- k selected services while providing the best trade-off between both service design quality and desired QoS values. Furthermore, we conducted a qualitative evaluation with developers. The obtained results provide evidence that our approach generates a good trade-off for what developers need regarding both QoS and quality of design. Our selection approach was evaluated as “relevant” from developers point of view, in improving the service selection task with an average score of 3.93, compared to an average of 2.62 for the traditional QoS-based approach.

TU59. A FRAMEWORK TO IMPROVE POLYGENIC QUESTIONS AND STRENGTHEN INFERENCES IN PSYCHIATRIC GENETIC EPIDEMIOLOGY

Web Service Composition has been studied intensively in the past two decades, considering the many aspects of the problem, like: semantic modeling, general QoS metrics, services state, and much more. In this paper, our focus is on integrating throughput metric with the automation formalism, without any limitation on automation, i.e. allowing full automation with meaningful throughput propagation. The user request and each service in the respiratory are described by their throughput values that are: an extra requirement for the request; and a maximum allowed load for services. In the proposed version, it is possible that to satisfy a request’s throughput, multiple traditional compositions are used concurrently, adding up for the required throughput. This highly increases the problem complexity both in how to model the combining throughput values, and also computationally, as the algorithms are required to search for multiple compositions. However, we also propose an algorithm that reduces the original problem to multiple constraint satisfaction problem instances and can find compositions for requests and considerable-sized repositories, which we prove through a synthetic evaluation.

Ecosystem services from urban forests: The case of Oslomarka, Norway

Urban and peri-urban forests provide multiple ecosystem services for city dwellers, including water, opportunities for recreation and tourism, climate regulation and habitat provision. Despite growing scholarly attention to urban forests, few studies to date have conducted comprehensive assessments of their ecosystem services. This study conducts an interdisciplinary and wide-ranging assessment of ecosystem services of Oslomarka, the emblematic forest surrounding Oslo, Norway’s capital city. First, we set the historical background and examine drivers of change shaping forest uses and ecosystem service composition at the study area. Second, we identify, characterize and measure ecosystem services delivered at the study area. Third, we assess trends in these ecosystem services over the past 50 years. Three main findings are highlighted. First, institutional, technological, and cultural factors are the main drivers of change shaping current ecosystem service composition. Second, ten ecosystem services and three sub-services were assessed and characterized, with water and opportunities for recreation and tourism found to be of particular prominence to locals. Third, our results show that provisioning and habitat services have declined over the past fifty years, while carbon sequestration and storage has increased. Cultural services increased overall, while also experiencing major changes in their nature and composition. Our research reveals the fundamental importance of Oslo’s urban forests and its ecosystem services for the well-being of local inhabitants.

How to improve the university library intelligent knowledge service: A system dynamics model

Based on the theory of system dynamics, this article analyses the system composition of a university library intelligent knowledge service (IKS) and its relevant functions. First, we designed a system dynamics model involved the steps and flow of system modelling, the analysis and frame diagram, the system boundaries and their settings, and the system causality model. Furthermore, the system simulation of the IKS is carried out with result analysis, validity test and application analysis. The system simulation results show that (1) the model can simulate the system operation processes of a university library IKS, (2) the model reveals the relationships and the operation trends among the elements of the IKS and (3) the effective implementation of a library IKS can be improved by following the essential law of service development, paying attention to the coordination and interaction among constituent elements, and influencing measures around key variables.

Towards Automated Choreographing of Web Services Using Planning

For Web service composition, choreography has recently received great attention and demonstrated a few key advantages over orchestration such as distributed control, fairness, data efficiency, and scalability. Automated design of choreography plans, especially distributed plans for multiple roles, is more complex and has not been studied before. Existing work requires manual generation assisted by model checking. In this paper, we propose a novel planning-based approach that can automatically convert a given composition task to a distributed choreography specification. Although planning has been used for orchestration, it is difficult to use planning for choreography, as it involves decentralized control, concurrent workflows, and contingency. We propose a few novel techniques, including compilation of contingencies, dependency graph analysis, and communication control, to handle these characteristics using planning. We theoretically show the correctness of this approach and empirically evaluate its practicability.

Layer-based Composite Reputation Bootstrapping

We propose a novel generic reputation bootstrapping framework for composite services. Multiple reputation-related indicators are considered in a layer-based framework to implicitly reflect the reputation of the component services. The importance of an indicator on the future performance of a component service is learned using a modified Random Forest algorithm. We propose a topology-aware Forest Deep Neural Network (fDNN) to find the correlations between the reputation of a composite service and reputation indicators of component services. The trained fDNN model predicts the reputation of a new composite service with the confidence value. Experimental results with real-world dataset prove the efficiency of the proposed approach.

Service function chain composition and placement using grammar‐based genetic algorithm

SummaryService function chaining (SFC) creates a requested orchestration for composition and placement of several service functions. One of the most critical issues for the service function providers is achieving the highest quality of service. We consider the problem of optimal Service Function composition and placement in an SFC‐enabled network with the aim of achieving maximum Quality of Service (QoS). To this end, a method of merging the composition, routing, and placement phases is introduced. A genetic algorithm is designed to find the service chain instance with the maximum QoS using grammar‐based Genetic Algorithm (GA) operators. This grammar is provided to filter invalid service compositions based on the data center, security, and mobile scenarios defined by Internet engineering task force. A chromosome in the proposed genetic algorithm corresponds to a service chain instance that matches the proposed grammar. For routing, strongly connected components (SCCs) are extracted to reduce search time in the network. Consequently, the search space is reduced for requests whose start and end nodes are placed in a SCC. Besides, the Floyd Warshall algorithm is used for routing between consecutive services. According to the results, the proposed method outperformed other methods in terms of the number of responded requests, runtime, and cost.

Availability Evaluation of Multi-Tenant Service Function Chaining Infrastructures by Multidimensional Universal Generating Function

The Network Function Virtualization (NFV) paradigm has been devised as an enabler of next generation network infrastructures by speeding up the provisioning and the composition of novel network services. The latter are implemented via a chain of virtualized network functions, a process known as Service Function Chaining. In this paper, we evaluate the availability of multi-tenant SFC infrastructures, where every network function is modeled as a multi-state system and is shared among different and independent tenants. To this aim, we propose a Universal Generating Function (UGF) approach, suitably extended to handle performance vectors, that we call Multidimensional UGF. This novel methodology is validated in a realistic multi-tenant telecommunication network scenario, where the service chain is composed by the network elements of an IP Multimedia Subsystem implemented via NFV. A steady-state availability evaluation of such an exemplary system is presented and a redundancy optimization problem is solved, so providing the SFC infrastructure which minimizes deployment cost while respecting a given availability requirement.

Optimized Artificial Bee Colony Algorithm for Web Service Composition Problem

Optimized Artificial Bee Colony Algorithm for Web Service Composition Problem

Reliability concept drift online measurement for composite cloud systems

Benefiting from the pay-as-you-go business paradigm, the scale of the global cloud computing industry grows rapidly. It has become a possible way to dynamically integrate and cooperate RESTful and SOAP-based component cloud services to develop large-scale complex software systems based on service composition techniques. The constructed composite cloud systems run under dynamic and uncertain operating environments. The issue of how to deal with the concept drift of the reliability data streams to guarantee a stable execution of composite cloud systems has become a grand challenge. To provide some early guidance and act as a trigger mechanism for composite cloud systems reliability adaptation during the system operation and maintenance, we propose a reliability concept drift measurement approach via Sinkhorn distance for composite cloud systems (or RCDMeas) in this paper. This method employs entropy regularization and fixed-point iteration to identify the reliability concept drift between the historical accumulated reliability flow data and the up-to-date reliability flow data. Large-scale data experiments verified the effectiveness of the proposed approach.

QoS-Based Pattern Recognition Approach for Web Service Discovery: Ar_WSDS

Web service composition requires high levels of integration and reliability of the services involved in its operation, which must meet specific quality criteria to ensure their proper execution and deployment. The discovery and selection of web services currently face optimization problems. Many services might satisfy a requirement with similar quality criteria. Because of this, software developers have to choose the most appropriate services for a given composition, complicated by the rapid increase in providers and services available in the cloud. Service composition also implies coupling according to a composition flow and non-functional requirement criteria. Such requirements make selection and composition a complex task not previously solved in the literature. This paper presents Ar_WSDS, a computational approach for web services discovery and selection in cloud environments, which bases its implementation on the brain’s pattern recognition systematic functioning. This process allows classifying web services through recognition modules created dynamically based on their quality parameters, resulting in a set of web services suitable for a web service composition. This approach allows a solution to the selection problem using less complex tasks. This paper introduces an architectural and procedural definition that provides the web service description with a pattern to recognize and select services using different recognition levels. We simulated our approach and evaluated it using a dataset from the QWS project that offers a set of quality criteria collected from different providers. The web services are recognized and classified using different quality criteria for the composition and each of their services. The results demonstrate the effectiveness of the discovery and selection process compared to other approaches. Furthermore, Ar_WSDS allows us to recognize and filter out web services with ambiguity and similarity in their provider information, a process that minimizes the discovery space for services.

The role of an ant colony optimisation algorithm in solving the major issues of the cloud computing

ABSTRACT There are many issues and problems in cloud computing that researchers try to solve by using different techniques. Most of the cloud challenges are NP-hard problems; therefore, many meta-heuristic techniques have been used for solving these challenges. As a famous and powerful meta-heuristic algorithm, the Ant Colony Optimisation (ACO) algorithm has been recently used for solving many challenges in the cloud. However, in spite of the ACO potency for solving optimisation problems, its application in solving cloud issues in the form of a review article has not been studied so far. Therefore, this paper provides a complete and detailed study of the different types of ACO algorithms for solving the important problems and issues in cloud computing. Also, the number of published papers for various publishers and different years is shown. In this paper, available challenges are classified into different groups, including scheduling, resource allocation, load balancing, consolidation, virtual machine placement, service composition, energy consumption, and replication. Then, some of the selected important techniques from each category by applying the selection process are presented. Besides, this study shows the comparison of the reviewed approaches and also it highlights their principal elements. Finally, it highlights the relevant open issues and some clues to explain the difficulties. The results revealed that there are still some challenges in the cloud environments that the ACO is not applied to solve.

An Intelligent Web Service Composition and Resource-Optimization Method Using K-Means Clustering and Knapsack Algorithms

Service-oriented architecture (SOA) has emerged as a flexible software design style. SOA focuses on the development, use, and reuse of small, self-contained, independent blocks of code called web services that communicate over the network to perform a certain set of simple tasks. Web services are integrated as composite services to offer complex tasks and to provide the expected services and behavior in addition to fulfilling the clients’ requests according to the service-level agreement (SLA). Web service selection and composition problems have been a significant area of research to provide the expected quality of service (QoS) and to meet the clients’ expectations. This research paper presents a hybrid web service composition model to solve web service selection and composition problems and to optimize web services’ resource utilization using k-means clustering and knapsack algorithms. The proposed model aims to maximize the service compositions’ QoS and minimize the number of web services integrated within the service composition using the knapsack algorithm. Additionally, this paper aims to track the service compositions’ QoS attributes by evaluating and tracking the web services’ QoS using the reward function and, accordingly, use the k-means algorithm to decide to which cluster the web service belongs. The experimental results on a real dataset show the superiority and effectiveness of the proposed algorithm in comparison with the results of the state–action–reward–state–action (SARSA) and multistage forward search (MFS) algorithms. The experimental results show that the proposed model reduces the average time of the web service selection and composition processes to 37.02 s in comparison to 47.03 s for the SARSA algorithm and 42.72 s for the MFS algorithm. Furthermore, the average of web services’ resource utilization results increased by 4.68% using the proposed model in comparison to the resource utilization by the SARSA and MFS algorithms. In addition, the experimental results showed that the average number of service compositions using the proposed model improved by 26.04% compared with the SARSA and MFS algorithms.