Service Composition Selection Research Articles

Proactive Resource Allocation for Cloud Manufacturing: A Dynamic Approach with DNN-Based Hybrid Many Objective Leopard Seal Algorithm

In the domain of cloud manufacturing, diverse services and the escalating demand for efficient and cost-effective service composition selection in cloud manufacturing have spurred day by day. Traditional methods face several challenges such as the risk of convergence issues and suboptimal solutions, high dimensionality of decision space, increased computational complexity, and scalability issues. To overcome these complexities, this paper proposed a novel method named the Deep Neural Network (DNN) based Hybrid Many Objective Leopard Seal (HMOLS) algorithm. In this study, DNN is employed to predict future Quality of Service (QoS) for each service based on historical data and integrated features. These predictions are fed back into the optimization model, influencing the dynamic weight calculation for each objective function. In this work, the Leopard Seal Optimization is implemented for hyperparameter optimization to enhance the performance of the HMOLS algorithm. Various evaluation metrics namely accuracy, throughput, reliability, resource utilization, and scalability are utilized to evaluate the performance of the HMOLS method and compare its performance with existing methods. The experimental outcomes depict the effectiveness of the HMOLS approach for the effective selection of services and fusion for cloud manufacturing. DNN-based predictions utilize deep neural networks to predict resource demand or utilization patterns and enable more accurate allocation decisions. These predictions optimize resource allocation by reallocating resources accordingly and address the challenges of resource utilization. Meanwhile, this method is required to overcome the computational overhead for applicability in cloud manufacturing.

Developing a weighted synergy network–based model for selecting prefabricated construction services

PurposeThe purpose of this paper is to develop a model considering synergy effect for prefabricated construction service combination selection.Design/methodology/approachThis research defines prefabricated construction service as a service-led construction method that meets the specific requirements of clients. Based on network theory, the multi-dimensional collaborative relationships of the prefabricated construction inter-services are formulated. The synergy effect is quantitatively calculated through the linear weighting of the strengths of collaborative relationships. Further, a weighted synergy network (WSN) is developed, from which a service composition selection model considering the synergy effect is established. Then, a genetic algorithm is employed to implement the model.FindingsThe results showed that (1) when the number of prefabricated construction services is increased, the synergy effect of combination options is enhanced; (2) The finer-grained prefabricated construction services, the stronger the synergy effect of service combination; (3) Clients have heterogeneous preferences for collaborative relationships, and there are differences in the synergy effect of service combination.Originality/valueThe contribution of this research includes proposed a method to quantify the synergy effect from the perspective of collaborative relationships, explored the specific procedure for the prefabricated construction service combination selection under the service-led construction, and provided a reference for promoting the development in construction. Besides, the model proposed could be applied to prefabricated construction service composition selection with diverse research boundaries or client preferences by executing the same procedure.

A novel and efficient salp swarm algorithm for large-scale QoS-aware service composition selection

A novel and efficient salp swarm algorithm for large-scale QoS-aware service composition selection

Privacy regulation aware service selection for multi-provision cloud service composition

The proliferation of cloud computing is driving the rapid growth of multi-provision cloud service composition (MPCSC), in which each cloud service provider (CSP) can offer multiple services simultaneously. Due to the openness and virtualization of cloud environments, cloud service providers (CSPs) may collect and distribute users’ privacy information without permission or authorization. More seriously, some CSPs have the ability to infer additional privacy information by combining multiple pieces of such data. Therefore, privacy is one of the main concerns of users seeking to take advantage of cloud services (CSs). This paper aims at addressing this problem by constructing a privacy regulation aware cloud service composition under considering the multi-service provision characteristics of CSPs. Firstly, it models user privacy requirements to formulate the problem of privacy regulation aware service selection for MPCSC (PSSM). Then, it proposes a pre-processed Kuhn–Munkres (KM) algorithm-based approach to solving the PSSM problem. Finally, it demonstrates that the proposed approach is effective and efficient for solving the PSSM problem by conducting a series of simulation experiments.

Qos Correlation with Constraint Based Optimal Service Selection in Composition

In a service-oriented architecture, service composition plays an important role as a key technology for integrating complex web applications. The service selection as part of the service composition process attracted towards Quality of Service as mandatory criteria for optimal selection of services for the composition process. While fulfilling the non-functional requirements from the user among a functionally similar list of services is a very challenging task. Web services are treating Software as a Service for building web applications. The Quality of Service correlation of services with constraints for selection of service is proposed in this paper. Experimental results of correlation without constraint method and proposed method show that our method outperforms over existing approach.

Performance evaluation of a solution for composite service selection problem with multiple consumers

In recent years, building business applications from independently developed services has become one of the current trends in service computing. To satisfy clients’ requirements, service composition is performed to compose the various capabilities of available services. With the proliferation of services having similar functionalities, assessing the quality of a given composition is a paramount factor to decide which services must be selected, or to choose whether a given composition can provide the requested QoS (Quality of Service). However, due to the fluctuating conditions in the dynamic cloud computing environment, the QoS and the performances of the services become unreliable, and therefore call into the question the accuracy of the composition QoS. To tackle this issue, different methods that analyse the QoS have been developed, making it possible to help the designers to first, understand the system behaviour when providers and consumers, are interacting, thus allowing to optimize the system by identifying performance bottlenecks within a specified deployment environment.In this study, the particular problem of many consumers that are competing to acquire services with same functionalities but with different QoS, is considered. For this purpose, assuming a dynamic environment, different models based on Discrete Time Markov Chain are developed to implement several policies of the system. A theoretical modelling of the problem is proposed including analytical results obtained using the Xborne tool. For each model, the average time to reach a given QoS for a community of consumers is reported. Such a performance metric allows the designer to predict the system behaviour in a dynamic environment.

Evaluation of EHO Algorithm’s in solving various real-life problems

Abstract: This paper, presents a new swarm-based metaheuristic search algorithm, known as Elephant Herding Optimization (EHO), which is proposed for solving various daily real-life problems such as benchmark problems, Service Selection in QoS-Aware Web Service Composition, Energy-Based Localization, PID controller tuning, Appliance Scheduling in Smart Grid identification and other problems. The EHO method is inspired by the herding behavior of elephant group. In nature, elephants live in clans under the leadership of a matriarch (female elephant), while the male elephants separate from their family when they grow up. These two behaviors are used by EHO as two operators: clan updating operator and separating operator, which will be used in the optimizing process. Elephant Herding Optimization (EHO) is used to solve NP-Hard problems.

A preference-based multi-objective algorithm for optimal service composition selection in cloud manufacturing

ABSTRACT Aiming at addressing several conflicting criteria of quality of service (QoS) that should be trade-off optimized during service composition and optimal selection (SCOS) in cloud manufacturing (CMfg), the improved non-dominated sorting genetic algorithm III (NSGA-III) is proposed and employed to address the SCOS issue. This is the first time that a preference-based multi-objective algorithm has been used to address the SCOS problem. In this paper, a novel K-layer preference reference point set approach is proposed for generating a reference point set with this algorithm to guide the search towards the interesting parts of the Pareto optimal region based on customer preferences, which improves the efficiency of the algorithm and the convergence of the obtained solution. A new fitness assignment strategy and environment selection scheme is developed accordingly to balance the relationship of diversity and convergence of preserved individuals in each generation. Additionally, the memetic algorithm is integrated into the evolutionary mechanism of the algorithm to address the insufficiency of local search. To validate the performance of the proposed algorithm, several test cases are conducted. The results demonstrate that the proposed algorithm is more competitive than other considered algorithms.

An approach for composite service selection based on the records of request/matching

As the emergence of more and more web services, it becomes more difficult to find the candidate atomic services and composing them to form a matched service process, where not only time is strictly limited, but also user demand must be satisfied. In order to solve this problem, this paper presents a service process selection method, which is driven by historical successful service request/matching solutions. Firstly, the system framework model is analysed and several concepts about service selection and composition are formalised. Then, the method model and algorithm are detailed. Finally, through a set of experiments, the effectiveness and robustness of our approach are evaluated.

An approach for composite service selection based on the records of request/matching

As the emergence of more and more web services, it becomes more difficult to find the candidate atomic services and composing them to form a matched service process, where not only time is strictly limited, but also user demand must be satisfied. In order to solve this problem, this paper presents a service process selection method, which is driven by historical successful service request/matching solutions. Firstly, the system framework model is analysed and several concepts about service selection and composition are formalised. Then, the method model and algorithm are detailed. Finally, through a set of experiments, the effectiveness and robustness of our approach are evaluated.

A Survey on QoS aware Web Service Selection for Reactive Service Composition

A Survey on QoS aware Web Service Selection for Reactive Service Composition

Simultaneous service selection for multiple composite service requests: A combinatorial auction approach

Emerging developments in service-based systems have contributed to a range of proposals for the rapid development of complex applications by composing existing web services through standards-based interfaces. With the availability of a large number of such services offering identical functionality at varying levels of price and other Quality-of-Service (QoS) parameters, selecting the best web services for composition is critical– the problem referred to as composite service selection (CSS). Extant CSS approaches have tended to offer solutions to the problem for a single composite service. We extend CSS to address the setting involving multiple, simultaneous composite service requests. We propose a service selection mechanism based on combinatorial auctions to solve this problem by simultaneously matching the web service offers and requests and performing extensive evaluations in four market sectors with varying economic sizes and complexity of requests. The results indicate that the proposed simultaneous mechanism achieves a higher success rate in allocating services to requesters compared to sequential matching mechanisms. While the average cost difference in procuring composite services under the two mechanisms is not statistically significant, the simultaneous mechanism achieves more stable and homogenous costs over time.

Data Citation Service for Wikipedia Articles

The citation of big scientific data is crucial not only for scientific activity but also for the scientific discovery and dissemination within scientist network. The main objective of this research is to develop a service-oriented data citation system using data mining techniques for Middle East and North Africa scientists. A novel service oriented framework is proposed to prototype the development of the system that includes query for-malization, service discovery, service composition design, service selection, search space, and service optimization. In this research, Wikipedia scientific-related articles are connected with more than 35 petabyte Pangaea datasets. The output of this work is a web service that takes Wikipedia article information as an input and provides the possible relevant datasets (if exist) related to the article. The evaluation of this research is based on a quantitative assessment performed to the quality of web service metrics, such as number of access and bandwidth utilization; which shows that the framework is robust enough to handle both big data access and its citation.

Fuzzy QoS requirement-aware dynamic service discovery and adaptation

The integration of coherent services plays a potential role in the field of Service Oriented Applications. Achieving this potential standard crucially depends on the ability to recognize and exploit the available services based on user requirements. In general, the user preferences on Quality of Service (QoS) requirements are fuzzy in nature. In addition to that, the QoS requirements are user dependent even if the functional requirements are the same. With a large number of available services, service selection for dynamic composition at run time is a challenge. Functional and non-functional assumptions made at design time may violate at run-time. These violations require run time reaction, by adopting a run-time process. Therefore, dynamic and fuzzy QoS-aware service discovery for run-time composition and continuous adaptation is a strong requirement in service oriented computing. Considering that different users follow different fuzzy reasoning in various contexts at different times, a fuzzy inference based service selection approach has been proposed in this paper. Continuous adaptation is done, as and when a design time assumption violation is reported by a run-time monitoring system. We have implemented and tested the proposed approach and the results show its effectiveness.

Improved adaptive immune genetic algorithm for optimal QoS-aware service composition selection in cloud manufacturing

Developments in new information technology have indicated that single manufacturing services are now unable to satisfy users’ multi-objective demands, especially in the process industry. As a new user-centric, service-oriented, demand-driven manufacturing model, cloud manufacturing can provide high-reliability, low-cost, fast-time, high-ability services. This study presents a new Manufacturers to Users (M2U) mode for cloud manufacturing, aiming at solving the core manufacturing service composition optimal selection (MSCOS) problem. The M2U mode expands the service areas and improves its dynamic optimal allocation capabilities of resources by efficient and flexible management and operation of services. Firstly, a comprehensive mathematical evaluation model with four critical quality of service (QoS)-aware indexes (time, reliability, cost, and ability) is constructed. Secondly, a new information entropy immune genetic algorithm (IEIGA) is proposed for the model solution. Finally, nine MSCOS problems of different scales are illustrated so as to compare the performance of the three algorithms. The results prove the effectiveness and superiority of the proposed algorithm and its suitability for solving large-scale service composition problems.

QoS-aware web service selection based on self-organising migrating algorithm and fuzzy dominance

Web service composition consists of creating a new complex web service by combining existing ones. The selection of composite services is a very complex and challenging task, especially with the increasing number of services offering the same functionality. The web service selection can be considered as a combinatorial problem which focuses on delivering the optimal composition that satisfies the user's requirements (functional and non-functional needs). Several optimisation algorithms have been proposed in the literature to tackle the web service selection. In this work, we propose an approach that adapts a recent stochastic optimisation algorithm called self-organising migrating algorithm (SOMA) for QoS web service selection problem. Furthermore, we propose a fuzzification of the Pareto dominance and use it to improve SOMA by comparing the services within the local search. The proposed approach is applicable to any combinatorial workflow with parallel, choice and loop pattern. We test our algorithm with a set of synthetic datasets and compare it to the most recently used algorithm (PSO). The comparative study shows that SOMA produces promising results, and therefore, it is able to select the user's composition in an efficient manner.

QoS-aware web service selection based on self-organising migrating algorithm and fuzzy dominance

Web service composition consists of creating a new complex web service by combining existing ones. The selection of composite services is a very complex and challenging task, especially with the increasing number of services offering the same functionality. The web service selection can be considered as a combinatorial problem which focuses on delivering the optimal composition that satisfies the user's requirements (functional and non-functional needs). Several optimisation algorithms have been proposed in the literature to tackle the web service selection. In this work, we propose an approach that adapts a recent stochastic optimisation algorithm called self-organising migrating algorithm (SOMA) for QoS web service selection problem. Furthermore, we propose a fuzzification of the Pareto dominance and use it to improve SOMA by comparing the services within the local search. The proposed approach is applicable to any combinatorial workflow with parallel, choice and loop pattern. We test our algorithm with a set of synthetic datasets and compare it to the most recently used algorithm (PSO). The comparative study shows that SOMA produces promising results, and therefore, it is able to select the user's composition in an efficient manner.

Mobile Service Selection for Composition: An Energy Consumption Perspective

Due to the limits of battery capacity of mobile devices, how to select cloud services to invoke in order to reduce energy consumption in mobile environments is becoming a critical issue. This paper addresses the problem of mobile service selection for composition in terms of energy consumption. It formally models this problem and constructs energy consumption computation models. Energy consumption aggregation rules for composite services with different structures are presented. It adopts the genetic algorithm to resolve it. A replanning mechanism is also proposed to deal with the changeable conditions and user behavior. A series of experiments are conducted to evaluate the performance of our method. The results show that our service selection method significantly outperforms traditional methods. Even if the conditions or user behavior is changeable, this method is still effective to recommend services. Moreover, the service selection method performs good scalability as the experimental scale increases.

Clustering Based Service Selection for Dynamic Service Composition

Clustering Based Service Selection for Dynamic Service Composition

Integrated QoUE and QoS approach for optimal service composition selection in internet of services (IoS)

Service Oriented Architecture (SOA) approaches are presently getting to be appropriate to embedded devices that feature embedded processing and communication. As a result these services get the ability to be hosted on high end machines to wireless resource constrained devices and on any physical object supported with communication ability. This creates the Internet of Services (IoS) environment. Services from multiple owners can be assembled into a composite service irrespective of their specific Quality of Service (QoS) and related properties for implementing a complex business process. In this context service consumer comes against the problem of selecting best service and for which providing QoS relevant guarantees leads to many challenging issues. One among them is determining a feasible service composition that fulfils a set of conditions while maintaining a good Quality of User Experience (QoUE). The last goal suggests the requirement to enforce an extra optimality prerequisite on the feasibility problem. In this paper, an optimization strategy oriented to efficient composite service selection for IoS model is designed through use of Particle Swarm Optimization (PSO) technique. Furthermore, prior to optimization, the services are assured of rich QoUE, especially trustworthiness in terms of reputation. The proposed work evaluates QoUE using the fuzzy based inference algorithm for identifying QoUE satisfied composite service. Experimental evaluation on a set of real world web services demonstrates the effectiveness of our proposed methodology.