Large-scale Service Composition Research Articles

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.

An efficient clustering mechanism towards large scale service composition in IoT

An efficient clustering mechanism towards large scale service composition in IoT

An Augmented Geospatial Service Web Based on QoS Constraints and Geospatial Service Semantic Relationships

The service network is capable of addressing large-scale service composition. However, existing service network works still have several limitations. Prior knowledge, such as expert-defined service chains, is not incorporated into the service network. QoS constraints are less considered in the service network, and thus the generated service chain does not always satisfy the optimal QoS constraints. Additionally, some basic services also require outputs to be used directly as inputs, which the service network cannot provide. To address these limitations, this paper proposes a geospatial service web (GSW) model named SR-QoS-GSW that incorporates service semantic relationships and QoS information. The SR-QoS-GSW model consists of atomic services and composite services that consider QoS, processing services, data services, and relationships among them. A SR-QoS-GSW prototype was developed using 570 atomic services and 27 composite services and evaluated using two case studies—a river network extraction and an urban housing selection. Then, the information entropy and time complexity between SR-QoS-GSW and the existing service network were compared. The results show that geospatial service chains can be created more efficiently by incorporating existing service chains as composite services. Integrating QoS information into the GSW would allow service composition algorithms to generate service chains that satisfy optimal QoS constraints. The outputs of services used as new inputs with additional self-matching relationships also give the service network greater flexibility. Finally, the analysis of the information entropy and time complexity verified the increased diversity and decreased the search space of the SR-QoS-GSW.

Improved Eagle Strategy Algorithm for Dynamic Web Service Composition in the IoT: A Conceptual Approach

The Internet of Things (IoT) is now expanding and becoming more popular in most industries, which leads to vast growth in cloud computing. The architecture of IoT is integrated with cloud computing through web services. Recently, Dynamic Web Service Composition (DWSC) has been implemented to fulfill the IoT and business processes. In recent years, the number of cloud services has multiplied, resulting in cloud services providing similar services with similar functionality but varying in Quality of Services (QoS), for instance, on the response time of web services; however, existing methods are insufficient in solving large-scale repository issues. Bio-inspired algorithm methods have shown better performance in solving the large-scale service composition problems, unlike deterministic algorithms, which are restricted. Thus, an improved eagle strategy algorithm method is proposed to increase the performance that directly indicates an improvement in computation time in large-scale DWSC in a cloud-based platform and on both functional and non-functional attributes of services. By proposing the improved bio-inspired method, the computation time can be improved, especially in a large-scale repository of IoT.

Dynamic mutual manufacturing and transportation routing service selection for cloud manufacturing with multi-period service-demand matching.

Recently, manufacturing firms and logistics service providers have been encouraged to deploy the most recent features of Information Technology (IT) to prevail in the competitive circumstances of manufacturing industries. Industry 4.0 and Cloud manufacturing (CMfg), accompanied by a service-oriented architecture model, have been regarded as renowned approaches to enable and facilitate the transition of conventional manufacturing business models into more efficient and productive ones. Furthermore, there is an aptness among the manufacturing and logistics businesses as service providers to synergize and cut down the investment and operational costs via sharing logistics fleet and production facilities in the form of outsourcing and consequently increase their profitability. Therefore, due to the Everything as a Service (XaaS) paradigm, efficient service composition is known to be a remarkable issue in the cloud manufacturing paradigm. This issue is challenging due to the service composition problem’s large size and complicated computational characteristics. This paper has focused on the considerable number of continually received service requests, which must be prioritized and handled in the minimum possible time while fulfilling the Quality of Service (QoS) parameters. Considering the NP-hard nature and dynamicity of the allocation problem in the Cloud composition problem, heuristic and metaheuristic solving approaches are strongly preferred to obtain optimal or nearly optimal solutions. This study has presented an innovative, time-efficient approach for mutual manufacturing and logistical service composition with the QoS considerations. The method presented in this paper is highly competent in solving large-scale service composition problems time-efficiently while satisfying the optimality gap. A sample dataset has been synthesized to evaluate the outcomes of the developed model compared to earlier research studies. The results show the proposed algorithm can be applied to fulfill the dynamic behavior of manufacturing and logistics service composition due to its efficiency in solving time. The paper has embedded the relation of task and logistic services for cloud service composition in solving algorithm and enhanced the efficiency of resulted matched services. Moreover, considering the possibility of arrival of new services and demands into cloud, the proposed algorithm adapts the service composition algorithm.

Integrating reinforcement learning and skyline computing for adaptive service composition

In service computing, combining multiple services through service composition to address complex user requirements has become a popular research topic. QoS-aware service composition aims to find the optimal composition scheme with the QoS attributes that best match user requirements. However, certain QoS attributes may continuously change in a dynamic service environment, so service composition methods need to be adaptive. Furthermore, the large number of candidate services poses a key challenge for service composition, where existing service composition approaches based on reinforcement learning (RL) suffer from low efficiency. To deal with the problems above, in this paper, a new service composition approach is proposed which combines RL with skyline computing where the latter is used for reducing the search space and computational complexity. A WSC-MDP model is proposed to solve the large-scale service composition within a dynamically changing environment. To verify the proposed method, a series of comparative experiments are conducted, and the experimental results demonstrate the effectiveness, scalability and adaptability of the proposed approach.

Adaptive and large-scale service composition based on deep reinforcement learning

In a service-oriented system, simple services are combined to form value-added services to meet users’ complex requirements. As a result, service composition has become a common practice in service computing. With the rapid development of web service technology, a massive number of web services with the same functionality but different non-functional attributes (e.g., QoS) are emerging. The increasingly complex user requirements and the large number of services lead to a significant challenge to select the optimal services from numerous candidates to achieve an optimal composition. Meanwhile, web services accessible via computer networks are inherently dynamic and the environment of service composition is also complex and unstable. Thus, service composition solutions need to be adaptable to the dynamic environment. To address these key challenges, we propose a new service composition scheme based on Deep Reinforcement Learning (DRL) for adaptive and large-scale service composition. The proposed approach is more suitable for the partially observable service environment, making it work better for real-world settings. A recurrent neural network is adopted to improve reinforcement learning, which can predict the objective function and enhance the ability to express and generalize. In addition, we employ the heuristic behavior selection strategy, in which the state set is divided into the hidden and fully observable state sets, to perform the targeted behavior selection strategy when facing with different types of states. The experimental results justify the effectiveness and efficiency, scalability, and adaptability of our methods by showing obvious advantages in composition results and efficiency for service composition.

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.

Multi-population parallel self-adaptive differential artificial bee colony algorithm with application in large-scale service composition for cloud manufacturing

As a new service-oriented smart manufacturing paradigm, cloud manufacturing (CMfg) aims at fully sharing and circulation of manufacturing capabilities towards socialization, in which composite CMfg service optimal selection (CCSOS) involves selecting appropriate services to be combined as a composite complex service to fulfill a customer need or a business requirement. Such composition is one of the most difficult combination optimization problems with NP-hard complexity. For such an NP-hard CCSOS problem, this study proposes a new approach, called multi-population parallel self-adaptive differential artificial bee colony (MPsaDABC) algorithm. The proposed algorithm adopts multiple parallel subpopulations, each of which evolves according to different mutation strategies borrowed from the differential evolution (DE) to generate perturbed food sources for foraging bees, and the control parameters of each mutation strategy are adapted independently. Moreover, the size of each subpopulation is dynamically adjusted based on the information derived from the search process. Different scales of the CCSOS problems are conducted to validate the effectiveness of the proposed algorithm, and the experimental results show that the proposed algorithm has superior performance over other hybrid and single population algorithms, especially for complex CCSOS problems.

Service Composition Optimization Method Based on Parallel Particle Swarm Algorithm on Spark

Web service composition is one of the core technologies of realizing service-oriented computing. Web service composition satisfies the requirements of users to form new value-added services by composing existing services. As Cloud Computing develops, the emergence of Web services with different quality yet similar functionality has brought new challenges to service composition optimization problem. How to solve large-scale service composition in the Cloud Computing environment has become an urgent problem. To tackle this issue, this paper proposes a parallel optimization approach based on Spark distributed environment. Firstly, the parallel covering algorithm is used to cluster the Web services. Next, the multiple clustering centers obtained are used as the starting point of the particles to improve the diversity of the initial population. Then, according to the parallel data coding rules of resilient distributed dataset (RDD), the large-scale combination service is generated with the proposed algorithm named Spark Particle Swarm Optimization Algorithm (SPSO). Finally, the usage of particle elite selection strategy removes the inert particles to optimize the performance of the combination of service selection. This paper adopts real data set WS-Dream to prove the validity of the proposed method with a large number of experimental results.

IFOA4WSC: a quick and effective algorithm for QoS-aware servicecomposition

In the service-oriented environment, large-scale Service Composition (SC) has become an important research, where Quality of Service (QoS) has been applied widely. QoS is generally used to represent non-functional properties of web services and differentiate them with the same functionality. Studying how to select appropriate services for a composition from a very large number of similar candidate services is an NP-hard problem. However, there are limitations among existing methods, e.g. poor scalability and massive calculation in the exhaustion method, slow convergence speed and easy to fall into local optimum in the traditional evolutionary computation method. Thus, in this paper, an improved fruit fly optimisation algorithm called IFOA4WSC is proposed, which is quick and effective for web service composition. We analyse the convergence of fruit fly swarms in IFOA4WSC algorithm, and carry out a large number of simulation experiments in the common and random data sets. Experimental results show that IFOA4WSC is effective and highly efficient, and has better stability against classical PSO, GA and FOA.

An optimization algorithm for service composition based on an improved FOA

Large-scale service composition has become an important research topic in Service-Oriented Computing (SOC). Quality of Service (QoS) has been mostly applied to represent nonfunctional properties of web services and to differentiate those with the same functionality. Many studies for measuring service composition in terms of QoS have been completed. Among current popular optimization methods for service composition, the exhaustion method has some disadvantages such as requiring a large number of calculations and poor scalability. Similarly, the traditional evolutionary computation method has defects such as exhibiting slow convergence speed and falling easily into the local optimum. In order to solve these problems, an improved optimization algorithm, WS FOA (Web Service composition based on Fruit Fly Optimization Algorithm) for service composition, was proposed, on the basis of the modeling of service composition and the FOA. Simulated experiments demonstrated that the algorithm is effective, feasible, stable, and possesses good global searching ability.

P_MOEA:一种多目标决策辅助遗传算法用于服务组合QoS优化

For the QoS management of service composition, one of the most important problems is how to find the optimal solutions for the service composition under certain SLA constraints. There are two main challenges existing for this optimization problem of SLA constraint service composition. At first, since many QoS dimensions are involved to describe the qualities of service composition, it is a typical multi-criteria decision making problem, where effective ranking model is required to rank candidate solutions and to identify optimal solutions. On the other hand, it is also a combinatorial optimization problem that is NP-hard, where the efficient and scalable algorithm is necessary for finding optimal solutions from large scale service composition candidates. In most traditional approaches, two kinds of techniques have been used to define the optimal solutions for service composition, i.e. the linear utility function and Pareto dominance relation. In the former, precise numeric weights are required for the definition of utility function, which is a hard task for users especially when the number of QoS dimensions increase. In the latter, weights assignments are not necessary and a set of skyline solutions are identified as optimal results. However, the scale of results set is uncontrollable, when the problem scale increases the number of skyline solutions will become too many to provide any insights for users to make the final selection. To cope with these drawbacks of traditional methods, in this paper we firstly introduce the PROMETHEE model into this challenging optimization problem. By combining it with Pareto optimization, we redefine optimization objective as top-k PROMETHEE optimal solutions based on skyline computation. Then, we present an efficient evolutionary algorithm: P_MOEA, which can efficiently find the Top-k optimal solutions for large scale service composition problem. We evaluate the effectiveness, efficiency and scalability of our approach with comprehensive analytical and experimental study.

Towards efficiency of QoS-driven semantic web service composition for large-scale service-oriented systems

Quality-of-Service (QoS) performance guarantee for service-oriented systems (SOS) has become a critical problem. With the increasing number of offered services comes the challenge of efficiently building large-scale SOS to meet the required QoS criteria. Optimization of QoS-driven semantic Web service composition is known to be NP-hard. We address the efficiency issue by developing a polynomial time algorithm (QDA) for shortest sequence composition. We use dynamic programming to find service candidates for each execution. When all the services are searched, we use a depth-first trace back to derive the execution plan. We have tested our approach under Web-scale demands 20,000 services and 150,000 semantic concepts. In comparison with existing approaches, our experimental results show that QDA can be used to solve large-scale service composition problem effectively and efficiently with QoS guarantee.