Service Composition Model Research Articles

A deep learning based framework for optimizing cloud consumer QoS-based service composition

The service composition problem in Cloud computing is formulated as a multiple criteria decision-making problem. Due to the extensive search space, Cloud service composition is addressed as an NP-hard problem. In addition, it is a long term based and economically driven. Building an accurate economic model for service composition has great attention to interest and importance for the Cloud consumer. A deep learning based service composition (DLSC) framework has been proposed in this paper. The proposed DLSC framework is considered an amalgamation between the deep learning long short term memory (LSTM) network and particle swarm optimization (PSO) algorithm. The LSTM network is applied to accurately predict the Cloud QoS provisioned values, and the output of LSTM network is fed to PSO algorithm to compose the best Cloud providers to contract with them for composing the needed services to minimize the consumer cost function. The proposed DLSC framework has been implemented using a real QoS dataset. According to the comparative results, it is found that the performance of the proposed framework outperforms the existing models with respect to the predictive accuracy and composition accuracy.

Validating trustworthy service composition through VIPLE and pi-calculus

The current formal verification practice focuses on functionality and does not consider verification of the non-functional attributes. In this study, we propose a method in which non-functional attributes are incorporated into the logic rules of inference in terms of composition of the linear logic and pi-calculus. Giving the credibility to non-functional attributes is important, especially in the cloud computing platform and IoT environments, where trust and security are ultra-important. Such studies have not been paid much attention by researchers and practitioners. In our approach, the evolvement of the non-functional attributes is included in the process of formal verification of the service composition scheme. In addition to theoretical analysis, we applied a tool named Visual IoT/robotics Programming Language Environment (VIPLE) to execute and verify the validity of the service composition model's function. We translate the proving process of the linear logic into the corresponding pi-calculus expressions. VIPLE can translate visual work flow into pi-calculus and can verify the correctness of pi-calculus expressions.

Modeling and analysis of robust service composition for network functions virtualization

Fault tolerance is critical for constructing a reliable service in Network Functions Virtualization (NFV). In this paper, we propose novel models and algorithms that provide the resilience of NFV services from multiple node and link failures. We first design an optimization model and the PAR protection algorithm that can efficiently protect an NFV service demand from network failures without any action from a controller due to the diversity of flow assignment. We then develop an optimization model for total demand protection with a guarantee of recovering the whole demand volume. Further, a new restoration algorithm, namely UNIT, is proposed for the design of large survivable NFV-based networks with the recovery of the affected bandwidth under the uncertainty of multiple network failures. We analytically prove the performance guarantee of UNIT in comparison with the optimal static solution. The results of our experimental study in a Mininet-based environment with the Ryu controller show that a combination of PAR and UNIT efficiently protects NFV-based networks from failures in terms of both resource restoration and recovery time.

An Improved Grey Wolf Optimizer Algorithm for Energy-Aware Service Composition in Cloud Manufacturing

Green and sustainable manufacturing is an inevitable development trend of manufacturing in the future, wherein low-energy consumption is regarded as a significant component. As an emerging manufacturing service system, cloud manufacturing (CMfg) is characterized by wide distribution, large quantity and complicated calling method for manufacturing services. Therefore, the energy consumption in the execution of composition service should be considered. However, few works related to energy consumption are focused on service composition at present. In response, this paper proposes an energy-aware service composition and optimal selection (EA-SCOS) model to ensure high-quality and low-energy consumption during the tasks. The mathematical model of energy consumption based on quality of service (QoS) is established, wherein the evaluation methods for manufacturing energy consumption and logistics energy consumption are described in detail. In order to solve the EA-SCOS problem effectively, a state-of-the-art algorithm which has been successfully applied in other fields, named grey wolf optimizer (GWO), is introduced. In addition, two key improvements for GWO are proposed to ensure the accuracy of solution. Finally, the effectiveness and feasibility of improved GWO (IGWO) are verified by a comparative study with GWO, genetic algorithm (GA) and max–min ant system (MMAS). Simulation results show that the proposed model is valid for reducing service energy consumption. Moreover, the improved strategies have obvious, positive effect on the accuracy of solutions and the performance of IGWO for addressing EA-SCOS problem is obviously better than the other three algorithms.

A QoS-Oriented Novel Optimization Schemes for Web Service Composition for Improved Healthcare

Due to massive increase in Web service in order to provide users an improved service it is necessary to develop a dynamic approach based on multi constraint Quality-of-Service (QoS) driven web service composition model to recommend suitable services to intended users is a big challenge. QoS plays a prominent role in selecting a Web Service. QoS is mainly determined by several non-functional parameters like availability, reliability, robustness, integrity, accessibility, interoperability, accuracy and security. It is evidenced based on experimentation that the proposed Improved Rider Optimization scheme achieves near optimal solution where group of riders racing towards a target location and attains adaptability and scalability by making a more confident web service selection with QoS prediction.

Service composition model and method in cloud manufacturing

Abstract This study optimizes service composition on the basis of task requirements to solve the problem of multitask corresponding multi-service selection. First, the basic path structure and the implementation steps of cloud manufacturing (CMfg) service composition are analyzed, and service composition is divided into four patterns. Second, the quality of service (QoS) index system of service composition is proposed by combining the six goals of time, composability, quality, usability, reliability, and cost; the calculation expressions of QoS under different composition structures are listed; and the mathematical model of CMfg service composition is established. Then, the weight of each index value in QoS evaluation is determined using an improved fuzzy comprehensive evaluation method. Finally, the optimal selection scheme of service composition is proposed by using gray relational analysis method(GM), and the validity of the optimal selection scheme is verified by an example of mold manufacturing.

Cloud Manufacturing Service Composition Optimization with Improved Genetic Algorithm

Aiming at the problems in which there exists collocation between services and manufacturing tasks, multiobjective cloud manufacturing service composition optimization seldom considers the synergy degree of composite cloud services and the complexity of service composition, so a novel service composition optimization approach, called improved genetic algorithm based on entropy (IGABE), is put forward. First, the mathematical expressions of service collocation degree, composition synergy degree, composition entropy, and their related influence factors of the service composition are analyzed, and their definitions and calculation methods are given. Then, a multiobjective cloud manufacturing service composition optimization mathematical model is established. Moreover, crossover and mutation operators are improved by introducing normal cloud model theory and piecewise function, and improved roulette selection method is used to perform the selection operation. And the fitness function of the proposed IGABE is designed by combining Euclidean deviation with angular deviation. Finally, the manufacturing task of a wheeled cleaning robot is exemplified to verify the correctness of the proposed multiobjective optimization model for cloud manufacturing service composition and the effectiveness of the proposed algorithm, compared with Standard Genetic Algorithm (SGA), Hybrid Genetic Algorithm (HGA), and Cloud-entropy Enhanced Genetic Algorithm (CEGA). The studied results show that IGABE converges faster than SGA and HGA and can analyze and reflect the content difference expressed by the objective functions of service composition scheme and its approximation degree to the corresponding dimensions of the ideal point vector more comprehensively than CEGA. As such, the optimal service composition obtained by IGABE algorithm can better meet the complex needs of users.

A medical monitoring scheme and health‐medical service composition model in cloud‐based IoT platform

AbstractAdvanced technologies such as internet of things (IoT) and clouds have significantly influenced on modern medical monitoring systems. Analytical statistics derived from massive patients' medical data via different data analysis methods, contribute in remote medical monitoring, early diagnosis of diseases, predicting clinical events, and recommending vital health/medical instructions. According to existence of the same health/medical services in functional aspect, finding appropriate composite health/medical services by the patients has been remained as a major concern in modern medical systems. Regarding this challenge, in this paper, a medical monitoring scheme for cloud‐based IoT platform is proposed, in which the patients' medical conditions are derived through predicting diseases by mining her physiological data collected from IoT devices and other medical records. A disease diagnosis model is used to analyze the patients' medical data for the aim of offering a composite health/medical prescription. After confirming the outcomes by medical team, it is sent to the patient. Then, the patient indicates her nonfunctional requirements such as location, cost and time to find the most appropriate composite health/medical service based on her preferences. Experimental results reveal that the proposed scheme is successful in achieving effective diseases diagnosis for offering composite health/medical prescriptions.

Formal modeling and verification of cloud‐based web service composition

SummaryWeb service composition is a key technology to resolve cross‐organizational business process integration, and it faces many constraints and requirements as unexpected bugs can cause shutdown of the software. A high degree of reliability is expected from such systems, and formal modeling of web service composition is an active area of research. In this work, we propose a new approach for the formal modeling of diverse web service composition. Our approach verifies the terminate property on all possible runs of the system to ensure deadlock‐free workings. For a verification of the properties of interest, we use the NuSMV model checker and specify the properties in CTL*. The approach can be used to verify the soundness property of diverse web service composition to ensure live and deadlock‐free web services.

A blockchain-based service composition architecture in cloud manufacturing

ABSTRACT Cloud manufacturing is a promising rising paradigm in which geographically distributed manufacturing resources are exposed as services in the cloud platform. The task of cloud manufacturing is to provide manufacturing resources as services to fulfil users’ demands. However, in existing cloud manufacturing architecture all the information is possessed by the platform operator, and the management of manufacturing services is highly centralised. It is firmly believed that decentralisation and elevating information transparency can help increase the operational performance. Blockchain is such a technology that distributes transactions information to a peer-to-peer network such that information is fully shared and unchangeable by partial peers. This paper proposes a blockchain-based cloud manufacturing architecture to enhance information transparency and decentralisation. The proposed platform is responsible for exposing manufacturing resources and wrapping them as services. Manufacturing services can be purchased by any user accessing to the platform. The blockchain is used to intermediate the service composition and record transaction results. Moreover, a quality of service (QoS) aware service composition model is proposed in the blockchain-based cloud manufacturing. A particle swarm optimisation (PSO) is proposed to solve the model. Finally, simulations are done to provide an application of the service composition in blockchain-based cloud manufacturing.

A collaborative service group-based fuzzy QoS-aware manufacturing service composition using an extended flower pollination algorithm

Quality of service (QoS)-aware manufacturing service composition has attracted growing attention from experts and scholars. However, most of them ignore the fuzziness and complexity of QoS values and describe QoS values using precise numbers. In addition, a one-to-one mapping-based service composition method has been widely used making it difficult to obtain an optimal solution with higher QoS values. In this study, we construct a new collaborative service group-based fuzzy QoS-aware (CSGFQ) manufacturing service composition model, which not only expands the traditional one-to-one mapping-based relationship between services and subtasks, but also objectively describes QoS values using fuzzy numbers. An extended flower pollination algorithm (FPA) that embeds four improvements is presented to solve the corresponding model. Four groups of experiments are performed to compare our proposed method with other baseline algorithms to prove the practicality, effectiveness, efficiency, and other performance of the extended FPA in solving the CSGFQ service composition problem.

A Trust-Based Agent Learning Model for Service Composition in Mobile Cloud Computing Environments

Mobile cloud computing has the features of resource constraints, openness, and uncertainty which leads to the high uncertainty on its quality of service (QoS) provision and serious security risks. Therefore, when faced with complex service requirements, an efficient and reliable service composition approach is extremely important. In addition, preference learning is also a key factor to improve user experiences. In order to address them, this paper introduces a three-layered trust-enabled service composition model for the mobile cloud computing systems. Based on the fuzzy comprehensive evaluation method, we design a novel and integrated trust management model. Service brokers are equipped with a learning module enabling them to better analyze customers’ service preferences, especially in cases when the details of a service request are not totally disclosed. Because traditional methods cannot totally reflect the autonomous collaboration between the mobile cloud entities, a prototype system based on the multi-agent platform JADE is implemented to evaluate the efficiency of the proposed strategies. The experimental results show that our approach improves the transaction success rate and user satisfaction.

Research on the Cloud Service Description Model for Cloud Service Composition in Cloud Manufacturing System

cloud service description is the foundation of cloud service composition in cloud manufacturing paradigm. Through unified encapsulation and description, cloud services in cloud manufacturing service platform can provide effective cloud service information for cloud service composition. This paper puts forward the requirements of cloud service description and modeling in cloud manufacturing paradigm. Based on the general ontology construction method, the core ontology and extensible ontology of cloud manufacturing service are constructed by extending OWL-S. On this basis, a formal description model of cloud manufacturing service is established, which supports the implementation of cloud service description method based on OWL-S for cloud service composition.

Type theory based semantic verification for service composition in cloud computing environments

To ensure successful running of service composition in software-as-a-service (SaaS) applications under dynamic cloud computing environments, service semantic verification becomes essentially important and meaningful. Taking advantages from both semantic expression in dependence record types (DRT) and completeness of service description in session types (ST), this paper presents a novel method for service composition verification at semantic level. Based on the characteristics of DRT, an ontology approach is proposed to enrich DRTs semantic description and establish a set of subtyping rules to characterize relationships of transmitted messages among services. As a general service composition description and semantic verification method, message DRT extended multiparty session types (MDRT-MST) are then constructed. A theoretical justification on the main patterns of service composition models is given through the extended reduction rules. As a case study, an online payment system is implemented for evaluating the system performance in practice. Experimental results demonstrate the feasibility and effectiveness of the proposed method in service reduction and verification for the real-world SaaS applications.

A collaborative mobile edge computing and user solution for service composition in 5G systems

AbstractMobile edge computing (MEC) is an emergent technology that has revolutionized traditional cloud service solutions. Mobile edge computing extends cloud computing by providing processing, storage, and networking capabilities at the edge of the mobile network. Delay‐sensitive and context‐aware applications are able to execute within close proximity of mobile users. Additionally, today's cloud services are not tailored to user specifications, but rather diversified toward a group of users. To guarantee delivery of user‐specific services in 5G networks, service composition techniques should be incorporated. This article envisions a real‐time, context‐aware, service‐composition collaborative framework that lies at the edge of the network, comprising MEC and user devices for fast composite service delivery. The proposed solution decomposes cloud data into a set of files and services, which are then replicated to MEC nodes. Frequently requested files and services are further cached onto user mobile devices for faster access. Both MEC nodes and mobile users advertise their services onto the collaborative edge/user space, where services are delivered either composite or unrendered according to users' requests. Service composition is achieved through a learning‐based workflow‐net approach that relies on previous composition results to build service composition models to be used for new compositions. The presented solution provides guaranteed and fast delivery of the requested cloud composite services to end users while sustaining QoS requirements and load balancing among edge and mobile nodes.

Manufacturing service composition model based on synergy effect: A social network analysis approach

Service social network is an umbrella term used to describe several interaction and collaboration phenomena that are shaping the future of how services are provided on the cloud manufacturing platform. Social relationship plays an important role when services are orchestrated with each other to build manufacturing business process, a role which has not been adequately investigated in previous research. The existing manufacturing service composition methods consider functional qualifications and Quality of Service (QoS) as major competitiveness factors. It is difficult to adopt them to situations where synergy effect is required and social relationships have significant impact on ensuring effective resources, information and knowledge hand-off in the complex task process. Focusing on the social collaboration feature of manufacturing services, a service composition method based on synergy effect is proposed. According to the data of service interaction and cooperation on the cloud platform, we extract and describe service social network and five kinds of relationships, namely interactive transaction, co-community, physical distance, resource-related, social similarity relationship. Based on the calculation of these relationships strength, the service synergy network is derived through the weighted aggregation. A service selection model that maximizes the overall synergy effect based on collaboration requirement is presented. The validity and advantages of our model and algorithm is validated through simulation experiment of intelligent automobile cloud manufacturing. The results show that our approach is not only efficient, but also finds better service scheme in line with the actual manufacturing scenario.

Optimal Allocation of QoS and Web Services in Cloud Computing

Cloud computing is a great model of demand and supply in information communication and services. It represents a complex infrastructure and provides a dynamic, distributed, heterogeneous and autonomous platform for solving problems in business, science and technology. This paper proposes a cloud computing environment that supports dynamic application service composition model. We develop a Quality of Service (QoS) based framework for effective web services allocation. In computing, the service consumer is projected to provide the QoS requirements as part of service discovery query. The cloud as marketplace for trading instances of web services can be bought or leased by web applications. We found that using dynamic decision-making management approach relaying on dynamic portfolio allocation model mainly used in finance, one can achieve the purpose of saving resources by reducing costs of quality of services and eliminating risks related to different services simultaneously.

A Novel Self-Organization Approach for Stigmergy Based Cloud Service Composition

This article describes how cloud computing and the convergence toward “Everything as a service” have encouraged the proliferation of services ready for use. These facilities have increased the requests of the companies for the development of the applications. In this context, where the reusability is needed, cloud service composition techniques are widely used. However, traditional centralized service composition techniques are not sufficient to address the needs of applications in highly dynamic and open environments. Early attempts for service composition models in decentralized environments have been proposed, but they are limited by their ability to adapt when deploying in highly dynamic and open environments. In this paper, the authors use Stigmergic-based self-organization mechanisms inspired from nature to model the decentralized service interactions and handle service composition in highly dynamic and open environments.

Modeling and Verification of Response Time of QoS-aware Web Service Composition by Timed CSP

Web service composition enables the provision/reusing of existing services in different business processes to satisfy different business requirements without investing in new infrastructure. QoS-aware web service composition seeks to help users find the optimal solution with maximization of users’ satisfaction. A number of approaches based on Communicating Sequential Processes (CSP) have been proposed to model and verify properties of web service composition. However, little work has been done in verifying inputs, outputs and QoS criteria of web service composition.In this paper, we present a framework to model and verify QoS-aware web service composition by Timed CSP. It helps verify whether the service composition can accept inputs, generate outputs, and meet QoS requirements as specified. To do the verification, transformation rules that map QoS-aware web service composition to Timed CSP are defined. We design a case study, where the model of QoS-aware web service composition is transformed into the model of process composition in Timed CSP and the program in machine-readable CSP (CSPM). Furthermore, experiments are performed by using the Failure Divergence Refinement (FDR) tool to verify inputs, outputs, and QoS of the service composition.

Cloud manufacturing service composition with service cooperation level evaluation

Cloud manufacturing is a new type of modern manufacturing mode. The Cloud Manufacturing Service Composition (CMSC) is one of the key issues of cloud manufacturing. The quality and efficiency of cloud manufacturing services are influenced by the rationality of the optimisation model. Previous studies have rarely considered the impact of cooperation between services on the quality of the manufacturing service composition. In this paper, a Service Cooperation Level (SCL) evaluation mechanism and a corresponding update model are proposed to dynamically measure the level of cooperation between services. Furthermore, a novel Cloud Manufacturing Service Composition model with Service Cooperation Level Evaluation (CMSC-SCLE) is established by introducing the SCL evaluation value as a new objective. Finally, an Improved Strength Pareto Evolutionary Algorithm 2 (ISPEA2) is proposed to solve the CMSC-SCLE problem. The experimental results show that the CMSC-SCLE model is more practical than the CMSC model. In addition, compared with four other classical multi-objective optimisation algorithms, ISPEA2 achieves better performance when solving CMSC-SCLE problem.