web service composition
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Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 82
Author(s):  
Hassan Tarawneh ◽  
Issam Alhadid ◽  
Sufian Khwaldeh ◽  
Suha Afaneh

Web service composition allows developers to create and deploy applications that take advantage of the capabilities of service-oriented computing. Such applications provide the developers with reusability opportunities as well as seamless access to a wide range of services that provide simple and complex tasks to meet the clients’ requests in accordance with the service-level agreement (SLA) requirements. Web service composition issues have been addressed as a significant area of research to select the right web services that provide the expected quality of service (QoS) and attain the clients’ SLA. The proposed model enhances the processes of web service selection and composition by minimizing the number of integrated Web Services, using the Multistage Forward Search (MSF). In addition, the proposed model uses the Spider Monkey Optimization (SMO) algorithm, which improves the services provided with regards to fundamentals of service composition methods symmetry and variations. It achieves that by minimizing the response time of the service compositions by employing the Load Balancer to distribute the workload. It finds the right balance between the Virtual Machines (VM) resources, processing capacity, and the services composition capabilities. Furthermore, it enhances the resource utilization of Web Services and optimizes the resources’ reusability effectively and efficiently. The experimental results will be compared with the composition results of the Smart Multistage Forward Search (SMFS) technique to prove the superiority, robustness, and effectiveness of the proposed model. The experimental results show that the proposed SMO model decreases the service composition construction time by 40.4%, compared to the composition time required by the SMFS technique. The experimental results also show that SMO increases the number of integrated ted web services in the service composition by 11.7%, in comparison with the results of the SMFS technique. In addition, the dynamic behavior of the SMO improves the proposed model’s throughput where the average number of the requests that the service compositions processed successfully increased by 1.25% compared to the throughput of the SMFS technique. Furthermore, the proposed model decreases the service compositions’ response time by 0.25 s, 0.69 s, and 5.35 s for the Excellent, Good, and Poor classes respectively compared to the results of the SMFS Service composition response times related to the same classes.


2021 ◽  
Author(s):  
◽  
Chen Wang

<p>Automated web service composition is one of the ultimate goals of service-oriented computing. It loosely couples web services to accommodate users' complex requirements. Evolutionary Computation (EC) techniques combined with AI planning have been successfully proposed to efficiently produce composite services with near-optimal Quality of Semantic Matchmaking (QoSM) and/or Quality of Service (QoS), which measure the satisfaction of the functional and non-functional requirements from users, respectively. Despite some recent progress, both the effectiveness and efficiency of existing approaches need further improvement to enhance the competitive advantage of service providers. The overall goal of this thesis is to propose novel EC-based fully automated service composition approaches that can effectively and efficiently solve challenging single-objective, multi-objective, and dynamic service composition problems.  Firstly, this thesis proposes two novel Estimation of Distribution Algorithm (EDA) based approaches (called EDA-NHM and EDA-EHM) and one memetic EDA-based approach with four different local search operators to single-objective fully automated web service composition that jointly optimizes QoSM and QoS. EDA-NHM and EDA-EHM are proposed with novel permutation-based and DAG-based representations to model the distribution of composition solutions with respect to varied service composition workflows. Two sampling techniques are also studied in EDA-NHM and EDA-EHM to effectively and efficiently sample new promising permutations and functionally valid DAGs, respectively. These two EDA-based approaches are compared to state-of-the-art works. The comparisons reveal that EDA-NHM produces better-quality composite services than EDA-EHM and the state-of-the-art works. On the other hand, EDA-EHM achieves the highest efficiency among all the competing EC-based methods, delivering moderate effectiveness. Furthermore, one proposed memetic approaches built upon EDA-NHM (called MEEDA-LOP) pushes the cutting-edge performance in terms of effectiveness and efficiency.   Secondly, this thesis studies two categories of multi-objective service composition problems: one category aims to generate a set of approximated Pareto optimal solutions for users to choose from, while the other category aims to generate multiple composite services for multiple user segments with distinctive preferences on QoSM. To effectively and efficiently handle the first category of problems, a memetic approach based on Non-dominated Sorting Genetic Algorithm II (NSGA-II), called MNSGA2-EDA, is proposed by enhancing NSGA-II with EDA-based local search. The novelty of this method lies in the innovative use of EDA for effective and efficient local improvements, rather than for global exploration. MNSGA2-EDA is compared to state-of-the-art multi-objective works, for studying its performance. We found that MNSGA2-EDA achieves much higher effectiveness and efficiency in finding Pareto optimal solutions. The second category of problems can be naturally treated as multitasking problems. Two novel multi-factorial evolutionary algorithms (called PMFEA and PMFEA-EDA) are proposed to effectively and efficiently solve this category of problems. These two algorithms implicitly or explicitly learn and share the knowledge of good solutions evolved so far for different tasks. We compare PMFEA and PMFEA-EDA with state-of-the-art works. We found that both PMFEA-EDA and PMFEA are performed at the cost of only a fraction of time compared to the single-tasking state-of-the-art works, which solve one task at a time. We also found that PMFEA-EDA yields solutions with the highest quality, confirming that learning and sharing knowledge explicitly is superior to learning and sharing knowledge implicitly.   Thirdly, this thesis studies a new dynamic service composition problem, focusing on handling stochastic service failures. We effectively handle this problem via two stages --- the design stage and the execution stage. Particularly, two accurate robustness measures are proposed based on Monte Carlo sampling and a lower bound estimation, respectively. These robustness measures are utilized in two proposed GA-based approaches (called GA-MC and GA-RE) at the design stage, to generate baseline composite solutions with high robustness. These baseline solutions can cope with the stochastic service failures robustly via a repairing process that supports continued high-quality execution of a composite service at the execution stage. Meanwhile, we propose a GA-2Stage algorithm by introducing a new adaptive evolutionary control mechanism, which supports two sequential evolutionary stages with two different fitness evaluation methods. These approaches are compared to each other to determine the most suitable method. Our experimental comparisons reveal that GA-RE algorithm with lower bound estimation outperforms GA-MC algorithm with Monte Carlo sampling estimation in finding composition solutions with high robustness, regardless of the size of the service repositories. Besides, compared to GA-RE, GA-2Stage achieves the highest efficiency with a negligible impact on the effectiveness at the execution stage, regardless of the service repositories' size.</p>


2021 ◽  
Author(s):  
◽  
Chen Wang

<p>Automated web service composition is one of the ultimate goals of service-oriented computing. It loosely couples web services to accommodate users' complex requirements. Evolutionary Computation (EC) techniques combined with AI planning have been successfully proposed to efficiently produce composite services with near-optimal Quality of Semantic Matchmaking (QoSM) and/or Quality of Service (QoS), which measure the satisfaction of the functional and non-functional requirements from users, respectively. Despite some recent progress, both the effectiveness and efficiency of existing approaches need further improvement to enhance the competitive advantage of service providers. The overall goal of this thesis is to propose novel EC-based fully automated service composition approaches that can effectively and efficiently solve challenging single-objective, multi-objective, and dynamic service composition problems.  Firstly, this thesis proposes two novel Estimation of Distribution Algorithm (EDA) based approaches (called EDA-NHM and EDA-EHM) and one memetic EDA-based approach with four different local search operators to single-objective fully automated web service composition that jointly optimizes QoSM and QoS. EDA-NHM and EDA-EHM are proposed with novel permutation-based and DAG-based representations to model the distribution of composition solutions with respect to varied service composition workflows. Two sampling techniques are also studied in EDA-NHM and EDA-EHM to effectively and efficiently sample new promising permutations and functionally valid DAGs, respectively. These two EDA-based approaches are compared to state-of-the-art works. The comparisons reveal that EDA-NHM produces better-quality composite services than EDA-EHM and the state-of-the-art works. On the other hand, EDA-EHM achieves the highest efficiency among all the competing EC-based methods, delivering moderate effectiveness. Furthermore, one proposed memetic approaches built upon EDA-NHM (called MEEDA-LOP) pushes the cutting-edge performance in terms of effectiveness and efficiency.   Secondly, this thesis studies two categories of multi-objective service composition problems: one category aims to generate a set of approximated Pareto optimal solutions for users to choose from, while the other category aims to generate multiple composite services for multiple user segments with distinctive preferences on QoSM. To effectively and efficiently handle the first category of problems, a memetic approach based on Non-dominated Sorting Genetic Algorithm II (NSGA-II), called MNSGA2-EDA, is proposed by enhancing NSGA-II with EDA-based local search. The novelty of this method lies in the innovative use of EDA for effective and efficient local improvements, rather than for global exploration. MNSGA2-EDA is compared to state-of-the-art multi-objective works, for studying its performance. We found that MNSGA2-EDA achieves much higher effectiveness and efficiency in finding Pareto optimal solutions. The second category of problems can be naturally treated as multitasking problems. Two novel multi-factorial evolutionary algorithms (called PMFEA and PMFEA-EDA) are proposed to effectively and efficiently solve this category of problems. These two algorithms implicitly or explicitly learn and share the knowledge of good solutions evolved so far for different tasks. We compare PMFEA and PMFEA-EDA with state-of-the-art works. We found that both PMFEA-EDA and PMFEA are performed at the cost of only a fraction of time compared to the single-tasking state-of-the-art works, which solve one task at a time. We also found that PMFEA-EDA yields solutions with the highest quality, confirming that learning and sharing knowledge explicitly is superior to learning and sharing knowledge implicitly.   Thirdly, this thesis studies a new dynamic service composition problem, focusing on handling stochastic service failures. We effectively handle this problem via two stages --- the design stage and the execution stage. Particularly, two accurate robustness measures are proposed based on Monte Carlo sampling and a lower bound estimation, respectively. These robustness measures are utilized in two proposed GA-based approaches (called GA-MC and GA-RE) at the design stage, to generate baseline composite solutions with high robustness. These baseline solutions can cope with the stochastic service failures robustly via a repairing process that supports continued high-quality execution of a composite service at the execution stage. Meanwhile, we propose a GA-2Stage algorithm by introducing a new adaptive evolutionary control mechanism, which supports two sequential evolutionary stages with two different fitness evaluation methods. These approaches are compared to each other to determine the most suitable method. Our experimental comparisons reveal that GA-RE algorithm with lower bound estimation outperforms GA-MC algorithm with Monte Carlo sampling estimation in finding composition solutions with high robustness, regardless of the size of the service repositories. Besides, compared to GA-RE, GA-2Stage achieves the highest efficiency with a negligible impact on the effectiveness at the execution stage, regardless of the service repositories' size.</p>


2021 ◽  
Author(s):  
◽  
Alexandre Sawczuk da Silva

<p>Automated Web service composition is one of the holy grails of service-oriented computing, since it allows users to create an application simply by specifying the inputs the resulting application should require, the outputs it should produce, and any constraints it should observe. The composition problem has been handled using a variety of techniques, from AI planning to optimisation algorithms, however no work so far has focused on handling multiple composition facets simultaneously, producing solutions that: (1) are fully functional (i.e. fully executable, with semantically-matched inputs and outputs), (2) employ a variety of composition constructs (e.g. sequential, parallel, and choice constructs), and (3) are optimised according to non-functional Quality of Service (QoS) measurements. The overall goal of this thesis is to propose hybrid Web service composition approaches that consider elements from all three facets described above when generating solutions. These approaches combine elements of AI planning and of Evolutionary Computation to allow for the creation of compositions that meet all of these requirements.  Firstly, this thesis proposes two novel approaches for Web service composition with direct representations. The first one is a tree-based approach where the leaf nodes are the atomic services included in the composition and the inner nodes are the structural constructs that shape the composition workflow. The second one is a graph-based approach where the atomic services are the vertices and the edges connecting them form the composition workflow. The two approaches are compared to determine which is most suitable to the QoS-aware fully automated Web service composition problem.  Secondly, this thesis proposes novel sequence-based approaches for Web service composition that use an indirect representation, i.e. they encode solutions as sequences of services. By representing solutions in this way, it is possible to initialise and evolve them without having to enforce their functional correctness. Then, before evaluating the fitness of each solution, a decoding algorithm is used to transform the sequence into the corresponding composition. The decoding algorithm builds the workflow using the ordering in the sequence as closely as possible when selecting the next service to be added, while at the same time generating a functionally correct structure.  Thirdly, this thesis treats Web service composition as a multi-objective problem, generating a set of trade-off solutions the user can choose from. More specifically, it proposes multi-objective approaches to fully automated Web service composition, which means that conflicting QoS attributes are independently optimised using a variety of representations that support flexible workflow structures. Additionally, a multi-objective and fully automated memetic approach that uses a local search operator to further improve the quality of solutions is proposed.  The following major contributions have been made in this thesis. Firstly, two approaches for Web service composition with direct representations were proposed. When the choice construct is not considered, the graph-based approach produces solutions of higher quality than those of the tree-based approach, but the opposite is true when the choice construct is included. Secondly, indirect representation approaches for Web service composition were proposed. These approaches perform well and can produce solutions with better quality than those found by the graph-based approach. Finally, we propose multi-objective approaches to fully automated service composition, employing different problem representations and a local search operator. The multi-objective approaches using the sequence-based representation were found to produce solutions with better overall quality.</p>


2021 ◽  
Author(s):  
◽  
Alexandre Sawczuk da Silva

<p>Automated Web service composition is one of the holy grails of service-oriented computing, since it allows users to create an application simply by specifying the inputs the resulting application should require, the outputs it should produce, and any constraints it should observe. The composition problem has been handled using a variety of techniques, from AI planning to optimisation algorithms, however no work so far has focused on handling multiple composition facets simultaneously, producing solutions that: (1) are fully functional (i.e. fully executable, with semantically-matched inputs and outputs), (2) employ a variety of composition constructs (e.g. sequential, parallel, and choice constructs), and (3) are optimised according to non-functional Quality of Service (QoS) measurements. The overall goal of this thesis is to propose hybrid Web service composition approaches that consider elements from all three facets described above when generating solutions. These approaches combine elements of AI planning and of Evolutionary Computation to allow for the creation of compositions that meet all of these requirements.  Firstly, this thesis proposes two novel approaches for Web service composition with direct representations. The first one is a tree-based approach where the leaf nodes are the atomic services included in the composition and the inner nodes are the structural constructs that shape the composition workflow. The second one is a graph-based approach where the atomic services are the vertices and the edges connecting them form the composition workflow. The two approaches are compared to determine which is most suitable to the QoS-aware fully automated Web service composition problem.  Secondly, this thesis proposes novel sequence-based approaches for Web service composition that use an indirect representation, i.e. they encode solutions as sequences of services. By representing solutions in this way, it is possible to initialise and evolve them without having to enforce their functional correctness. Then, before evaluating the fitness of each solution, a decoding algorithm is used to transform the sequence into the corresponding composition. The decoding algorithm builds the workflow using the ordering in the sequence as closely as possible when selecting the next service to be added, while at the same time generating a functionally correct structure.  Thirdly, this thesis treats Web service composition as a multi-objective problem, generating a set of trade-off solutions the user can choose from. More specifically, it proposes multi-objective approaches to fully automated Web service composition, which means that conflicting QoS attributes are independently optimised using a variety of representations that support flexible workflow structures. Additionally, a multi-objective and fully automated memetic approach that uses a local search operator to further improve the quality of solutions is proposed.  The following major contributions have been made in this thesis. Firstly, two approaches for Web service composition with direct representations were proposed. When the choice construct is not considered, the graph-based approach produces solutions of higher quality than those of the tree-based approach, but the opposite is true when the choice construct is included. Secondly, indirect representation approaches for Web service composition were proposed. These approaches perform well and can produce solutions with better quality than those found by the graph-based approach. Finally, we propose multi-objective approaches to fully automated service composition, employing different problem representations and a local search operator. The multi-objective approaches using the sequence-based representation were found to produce solutions with better overall quality.</p>


2021 ◽  
Author(s):  
◽  
Yang Yu

<p>Web service composition has become a promising technique to build powerful enterprise applications by making use of distributed services with different functions. In the age of big data, more and more web services are created to deal with a large amount of data, which are called data-intensive services. Due to the explosion in the volume of data, providing efficient approaches to composing data-intensive services will become more and more important in the field of service-oriented computing. Meanwhile, as numerous web services have been emerging to offer identical or similar functionality on the Internet, web service composition is usually performed with end-to-end Quality of Service (QoS) properties which are adopted to describe the non-functional properties (e.g., response time, execution cost, reliability, etc.) of a web service. In addition, the executions of composite web services are typically coordinated by a centralized workflow engine. As a result, the centralized execution paradigm suffers from inefficient communication and a single point of failure. This is particularly problematic in the context of data-intensive processes. To that end, more decentralized and flexible execution paradigms are required for the execution of data-intensive applications.  From a computational point of view, the problems of QoS-aware data-intensive web service composition and execution can be characterised as complex, large-scale, constrained and multi-objective optimization problems. Therefore, genetic programming (GP) based solutions are presented in this thesis to address the problems. A series of simulation experiments are provided to demonstrate the performance of the proposed approaches, and the empirical observations are also described in this thesis.  Firstly, we propose a hybrid approach that integrates the local search procedure of tabu search into the global search process of GP to solving the problem of QoS-aware data-intensive web service composition. A mathematical model is developed for considering the mass data transmission across different component services in a data-intensive service composition. The experimental results show that our proposed approach can provide better performance than the standard GP approach and two traditional optimization methods.  Next, a many-objective evolutionary approach is proposed for tackling the QoS-aware data-intensive service composition problem having more than three competing quality objectives. In this approach, the original search space of the problem is reduced before a recently developed many-objective optimization algorithm, NSGA-III, is adopted to solve the many-objective optimization problem. The experimental results demonstrate the effectiveness of our approach, as well as its superiority than existing single-objective and multi-objective approaches.  Finally, a GP-based approach to partitioning a composite data-intensive service for decentralized execution is put forth in this thesis. Similar to the first problem, a mathematical model is developed for estimating the communication overhead inside a partition and across the partitions. The data and control dependencies in the original composite web service can be properly preserved in the deployment topology generated by our approach. Compared with two existing heuristic algorithms, the proposed approach exhibits better scalability and it is more suitable for large-scale partitioning problems.</p>


2021 ◽  
Author(s):  
◽  
Yang Yu

<p>Web service composition has become a promising technique to build powerful enterprise applications by making use of distributed services with different functions. In the age of big data, more and more web services are created to deal with a large amount of data, which are called data-intensive services. Due to the explosion in the volume of data, providing efficient approaches to composing data-intensive services will become more and more important in the field of service-oriented computing. Meanwhile, as numerous web services have been emerging to offer identical or similar functionality on the Internet, web service composition is usually performed with end-to-end Quality of Service (QoS) properties which are adopted to describe the non-functional properties (e.g., response time, execution cost, reliability, etc.) of a web service. In addition, the executions of composite web services are typically coordinated by a centralized workflow engine. As a result, the centralized execution paradigm suffers from inefficient communication and a single point of failure. This is particularly problematic in the context of data-intensive processes. To that end, more decentralized and flexible execution paradigms are required for the execution of data-intensive applications.  From a computational point of view, the problems of QoS-aware data-intensive web service composition and execution can be characterised as complex, large-scale, constrained and multi-objective optimization problems. Therefore, genetic programming (GP) based solutions are presented in this thesis to address the problems. A series of simulation experiments are provided to demonstrate the performance of the proposed approaches, and the empirical observations are also described in this thesis.  Firstly, we propose a hybrid approach that integrates the local search procedure of tabu search into the global search process of GP to solving the problem of QoS-aware data-intensive web service composition. A mathematical model is developed for considering the mass data transmission across different component services in a data-intensive service composition. The experimental results show that our proposed approach can provide better performance than the standard GP approach and two traditional optimization methods.  Next, a many-objective evolutionary approach is proposed for tackling the QoS-aware data-intensive service composition problem having more than three competing quality objectives. In this approach, the original search space of the problem is reduced before a recently developed many-objective optimization algorithm, NSGA-III, is adopted to solve the many-objective optimization problem. The experimental results demonstrate the effectiveness of our approach, as well as its superiority than existing single-objective and multi-objective approaches.  Finally, a GP-based approach to partitioning a composite data-intensive service for decentralized execution is put forth in this thesis. Similar to the first problem, a mathematical model is developed for estimating the communication overhead inside a partition and across the partitions. The data and control dependencies in the original composite web service can be properly preserved in the deployment topology generated by our approach. Compared with two existing heuristic algorithms, the proposed approach exhibits better scalability and it is more suitable for large-scale partitioning problems.</p>


Author(s):  
Benjia Hu ◽  
Zhiyong Wu ◽  
Dayin Shi ◽  
Ke Meng ◽  
Ning Yuan ◽  
...  

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