composite services
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2022 ◽  
Vol 22 (1) ◽  
pp. 1-28
Author(s):  
Sajib Mistry ◽  
Lie Qu ◽  
Athman Bouguettaya

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


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 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Meng Cai ◽  
Ying Cui ◽  
Yang Yu ◽  
Ying Xue ◽  
Han Luo

The characteristics of service computing environment, such as service loose coupling, resource heterogeneity, and protocol independence, propose higher demand for the trustworthiness of service computing systems. Trust provisioning for composite services has become a hot research spot worldwide. In this paper, quality of service (QoS) planning techniques are introduced into service composition-oriented QoS provisioning architecture. By QoS planning, the overall QoS requirement of the composite service is decomposed into separate QoS requirement for every constituent atom service, the QoS level of which can subsequently be satisfied through well-designed service entity selection policies. For any single service entity, its QoS level is variable when the deployment environment or the load of service node changes. To mitigate the uncertainty, we put forward QoS preprocessing algorithms to estimate the future QoS levels of service entities with their history execution data. Then, based on the modeling of composite service and QoS planning, we design three algorithms, which include the time preference algorithm, cost/availability (C/A) preference algorithm, and Euclidean distance preference algorithm, to select suitable atomic services meeting the user’s requirements. Finally, by combining genetic algorithm and local-search algorithm, we propose memetic algorithm to meet the QoS requirements of composite service. The effectiveness of the proposed methods by which the QoS requirements can be satisfied up to 90% is verified through experiments.


2021 ◽  
Vol 18 (2) ◽  
pp. 76-100
Author(s):  
Yu Wang ◽  
XiaoLin Li ◽  
HuaPing Chen

In the web service marketplace, component-based economy has been proposed for describing participants' behavioral patterns. Composite service networks combine multiple composite services required by various service consumers. With each composite service as a product, web services comprise heterogeneous products. In this study, the pricing behavior of networked individual service providers is investigated. With the objective of service survival or high profitability, service providers compete both on the single-service and service-network levels. Using examples, several mild assumptions are formulated and analyzed. Then, a bi-objective optimization model is proposed based on these assumptions, which attempts to maintain a reasonable effectiveness-fairness trade-off from the individual service providers' perspective. The NP-completeness of the single-objective version is demonstrated by transforming the problem into a subset sum problem, which highlights the challenge of obtaining a pareto set for the bi-objective model. Finally, to validate the proposed model, numerical experimentation and case study are conducted, and both the bi-objective and many-objective versions of the problem are discussed.


Author(s):  
K. Sudhakar ◽  
M.James Stephen ◽  
P.V.G.D. Prasad Reddy

Service-oriented architecture (SOA)[1] is an incessant term to deal with various administrations dependent on solicitations of various clients in various ongoing applications. Still, now, a few people don't have total information about what SOA really has done, they get confounded how SOA identifies with distributed computing. On account of cloud administration usage in SOA need security mindful help creation with finegrained stream control to make sure about web administrations at execution time to share various administrations to various clients in dispersed condition? Routinely various models were acquainted with investigating secure web administrations at execution of various administrations. Because of access control infringement, they will take high execution time and other leader boundaries profoundly, and furthermore they don't control access assurance arrangements in composite administrations, which may deliver bothersome information spillage. To conquer these infringement issues in SOA, we present Integrated Novel Multi-Level Composite Service Model (INMLCSM)[2] to lessen infringement calculation cost dependent on customer authentic and demonstrate customer composite administrations and furthermore perform nearby/distant strategy calculation for highest customers. We acquaint idea of change factor with characterize halfway administrations. Our proposed approach portrays forceful exploratory outcomes.


2021 ◽  
Vol 11 (4) ◽  
pp. 1803
Author(s):  
Félix Francisco Ramos Corchado ◽  
Alan Christian López Fraga ◽  
Rafael Salazar Salazar ◽  
Marco Antonio Ramos Corchado ◽  
Ofelia Begovich Mendoza

Pervasive service composition is useful in many scenarios, for instance, in urban planning or controlled harvest. Currently, there is no standard to develop solutions using pervasive service composition. However, big companies propose their frameworks to develop complex services, but their frameworks are appropriate in specific applications, such as home automation and agriculture. On the other hand, there are different very well-grounded academic proposals for pervasive service composition. However, these do not solve the problems of traditional approaches that are appropriate to specific areas of application, and adaptation is needed to deal with the dynamism of the environment. This article presents a cognitive approach for pervasive service composition where InfoCom devices and the implementation of cognitive functions interact to create pervasive composite services. Our central hypothesis is that cognitive theory can help solve actual problems requiring pervasive service composition, as it addresses the above-mentioned problems. To test our approach, in this article we present a case of urban insecurity. Specifically, in different countries, street robbery using firearms is one of the problems with a high impact because of its frequency. This article proposes to compose a pervasive service for deterring criminals from committing their crimes. The results obtained by simulating our proposal in our case study are promising. However, more research needs to be achieved before applying the proposed approach to actual problems. The research needed ought to address various problems, some of which are discussed in this article.


IoT ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 50-70
Author(s):  
Claudio Marche ◽  
Michele Nitti

The IoT is transforming the ordinary physical objects around us into an ecosystem of information that will enrich our lives. The key to this ecosystem is the cooperation among the devices, where things look for other things to provide composite services for the benefit of human beings. However, cooperation among nodes can only arise when nodes trust the information received by any other peer in the system. Previous efforts on trust were concentrated on proposing models and algorithms to manage the level of trustworthiness. In this paper, we focus on modelling the interaction between trustor and trustee in the IoT and on proposing guidelines to efficiently design trust management models. Simulations show the impacts of the proposed guidelines on a simple trust model.


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