Service Composition Research Articles

An Intelligent Bat Algorithm for Web Service Selection with QoS Uncertainty

Currently, the selection of web services with an uncertain quality of service (QoS) is gaining much attention in the service-oriented computing paradigm (SOC). In fact, searching for a service composition that fulfills a complex user’s request is known to be NP-complete. The search time is mainly dependent on the number of requested tasks, the size of the available services, and the size of the QoS realizations (i.e., sample size). To handle this problem, we propose a two-stage approach that reduces the search space using heuristics for ranking the task services and a bat algorithm metaheuristic for selecting the final near-optimal compositions. The fitness used by the metaheuristic aims to fulfil all the global constraints of the user. The experimental study showed that the ranking heuristics, termed “fuzzy Pareto dominance” and “Zero-order stochastic dominance”, are highly effective compared to the other heuristics and most of the existing state-of-the-art methods.

A service composition evolution method that combines deep clustering and a service requirement context model

A service composition evolution method that combines deep clustering and a service requirement context model

Evolutionary PSO-based emergency monitoring geospatial edge service chain in the emergency communication network

ABSTRACT Emergency communication networks play a vital role in disaster monitoring, transmission, and application during disaster emergency response (DER), however, the performance and stability of edge nodes in the emergency communication networks are often weak due to limited communication and computation resources. This weakness directly affects the quality of service (QoS) of the geospatial edge service (GES) chains involved in emergency monitoring. Existing research predominantly addresses service compositions in stable environments, neglecting the aggregation of efficient and robust GES chains in emergency communication networks. This study proposes an evolutionary particle swarm optimization (EPSO)-based emergency monitoring GES chain in an emergency communication network. It includes a GES chain model of emergency environment monitoring for tailing areas, as well as the designs of the particle chromosome encoding method, fitness evaluation model, and particle chromosome swarm update operators of the EPSO-based GES chain. Finally, the study conducts emergency environment monitoring experiments for tailing areas using the proposed method. Experiments results demonstrate that the proposed method significantly enhances the efficiency, stability, and reliability of emergency monitoring GES chains in the emergency communication network. This is crucial to providing fast and reliable services for DER during natural disasters.

Norm-based reinforcement learning for QoS-driven service composition

QoS-aware service composition is challenging due to a high number of QoS attributes, component services, and candidate services. Realistic service composition applications operate in uncertain environments where QoS values may change dynamically. Moreover, user requirements on QoS attributes should be considered, and their different nature can make it difficult to express them by adopting relative weights. Reinforcement Learning is proposed as a viable approach in order to deal with the complexity and variability of the environment. In this paper, we propose a novel approach that integrates traditional reinforcement learning with a norm-based paradigm to consider cases where component services may have a different number and types of QoS attributes. In such a way, it is possible to consider additional local requirements that may hold only for specific service components still pursuing a global optimization. Norms allow using a uniform formalism to express qualitative and quantitative as well as hard and soft user requirements. The approach has been tested on a real dataset of 2500 web services showing its performance, scalability, and adaptability properties.

Evaluating the accessibility and value of U.S. ambulatory care among Medicaid expansion states and non-expansion states, 2012–2015

BackgroundWhile the Affordable Care Act’s Medicaid expansion improved healthcare coverage and access for millions of uninsured Americans, less is known about its effects on the overall accessibility and quality of care across all payers. Rapid volume increases of newly enrolled Medicaid patients might have unintentionally strained accessibility or quality of care. We assessed changes in physician office visits and high- and low-value care associated with Medicaid expansion across all payers.MethodsPrespecified, quasi-experimental, difference-in-differences analysis pre and post Medicaid expansion (2012–2015) in 8 states that did and 5 that did not choose to expand Medicaid. Physician office visits sampled from the National Ambulatory Medical Care Survey, standardized with U.S. Census population estimates. Outcomes included visit rates per state population and rates of high or low-value service composites of 10 high-value measures and 7 low-value care measures respectively, stratified by year and insurance.ResultsWe identified approximately 143 million adults utilizing 1.9 billion visits (mean age 56; 60% female) during 2012–2015. Medicaid visits increased in expansion states post-expansion compared to non-expansion states by 16.2 per 100 adults (p = 0.031 95% CI 1.5–31.0). New Medicaid visits increased by 3.1 per 100 adults (95% CI 0.9–5.3, p = 0.007). No changes were observed in Medicare or commercially-insured visit rates. High or low-value care did not change for any insurance type, except high-value care during new Medicaid visits, which increased by 4.3 services per 100 adults (95% CI 1.1–7.5, p = 0.009).ConclusionsFollowing Medicaid expansion, the U.S. healthcare system increased access to care and use of high-value services for millions of Medicaid enrollees, without observable reductions in access or quality for those enrolled in other insurance types. Provision of low-value care continued at similar rates post-expansion, informing future federal policies designed to improve the value of care.

DebtCom: Technical Debt-Aware Service Recomposition in SaaS Cloud

Given the changing workloads from the tenants, it is not uncommon for a service composition running in the multi-tenant SaaS cloud to encounter under-utilization and over-utilization on the component services. Both cases are undesirable and it is therefore nature to mitigate them by recomposing the services to a newly optimized composition plan once they have been detected. However, this ignores the fact that under-/over-utilization can be merely caused by temporary effects, and thus the advantages may be short-term, which hinders the long-term benefits that could have been created by the original composition plan, while generating unnecessary overhead and disturbance via recomposition. In this paper, we propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DebtCom</monospace> , a framework that determines whether to trigger recomposition based on the technical debt metaphor and time-series prediction of workload. In particular, we propose a service debt model, which has been explicitly designed for the context of service composition, to quantify the debt. Our core idea is that recomposition can be unnecessary if the under-/over-utilization only cause temporarily negative effects, and the current composition plan, although carries debt, can generate greater benefit in the long-term. We evaluate <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DebtCom</monospace> on a large scale service system with up to 10 abstract services, each of which has 100 component services, under real-world dataset and workload traces. The results confirm that, in contrast to the state-of-the-art, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DebtCom</monospace> achieves better utility while having lower cost and number of recompositions, rendering each composition plan more sustainable.

Dynamic cloud manufacturing service composition with re-entrant services: an online policy perspective

Cloud manufacturing (CMfg) emerges as a promising manufacturing paradigm, where service composition (SC) is a critical process concentrating on matching tasks and services. Existing studies usually ignore the dynamic nature of the CMfg environment, where task information is not always known before. Moreover, CMfg services are re-entrant, i.e. after being occupied for a period of service time, these services re-enter the CMfg platform (i.e. be available again). Re-entrant services significantly complicate CMfg platform revenue management. In this regard, we study the dynamic SC problem of CMfg (CMfg-DSC) incorporating re-entrant services within an online setting for the first time. CMfg-DSC is reformulated as an online packing problem. If a task is accepted, each requested service will be occupied until service time terminates. We propose online policies with performance guarantees, namely, static online packing policy (Static), opportunity-cost-based policy (Oppo), and dual-based policy with/without known distribution (Dual-k & Dual-u). Experiment results show that (1) Static is applicable for most cases; (2) Oppo has the potential for decent performance but at the cost of time; (3) Dual-u is reliable when only past observations are available; (4) Dual-k performs well given abundant service provision, but its performance would deteriorate if we lower the reward-cost threshold.

Establishing the grouping principle of public services based on the analysis of similarity coefficients

The object of this study is the process of identifying implicit relationships between public services and the process of forming portfolios and groups of services determined on the basis of the formulated principle of combining services. The task to identify groups of interconnected services, which is relevant in view of the processes of digital transformation in the field of public services, was addressed. Expanding portfolios of public services thru the formed groups of interconnected services could make it possible to better take into account the interests of users, as well as simplify the process of development and reengineering of public services. The proposed approach to form groups of services would reduce the cost and complexity of reengineering services or converting them into electronic form. The result of research is a new principle of combining or grouping services, which is based on the methods of intelligent analysis. The basis of the proposed principle of forming groups of services is a simple and effective mathematical apparatus of applying similarity coefficients to establish the similarity of sets, which has not yet been used in the field of public services. The presence of interrelationships between services was established based on the analysis of Jaccard and Sorensen similarity coefficients. An algorithm for forming groups of services based on a new principle has been developed. The relationship between services was established as a result of the analysis of the composition of services, that is, the documents or data necessary for the provision of these services. This allows applying the proposed approach without restrictions and additional conditions. The developed algorithm also allows for «What-if» analysis of the obtained results. An illustrative example was considered: for a set of 10 services, 4 groups of services are formed. Suggestions for applying the proposed approach are provided

Thermal-oxidation degradation of polylactic acid/cellulose nanocrystal composites: Effects of surface chemistry

Cellulose nanocrystal (CNC) is one of the most attractive renewable nano-fillers for polylactic acid (PLA). Depending on the production routes and starting cellulose sources, CNC possesses different surface chemistry, surface charge density, and morphology. However, the effect of the CNC surface chemistry on the thermal-oxidation degradation behaviors of PLA/CNC composites is still elusive, although it is a crucial factor in determining the hot processing and service of composites. In this work, sulfated (CNC-S), carboxylated (CNC-O), and uncharged CNC (CNC-Cl) were produced and compounded with PLA via a two-step masterbatch method, respectively. The thermal-oxidation degradation behaviors of the obtained composites were systematically investigated to elucidate their degradation mechanisms. Results show that the surface uncharged CNC produced by hydrochloric acid hydrolysis contributes to the thermal-oxidation stability of composites, but is more prone to agglomerate due to the lack of electrostatic repulsion. Compared with uncharged CNC, negatively charged CNCs (CNC-S and CNC-O) appear better dispersion state in the PLA matrix. However, PLA/CNC-S composites present relatively lower weight loss temperature and degradation activation energy due to the acceleration of desulfation process on thermal-oxidation degradation. Contrary to expectations, the addition of carboxylated CNC has almost no negative effect on the thermal oxygen stability of PLA because the decarboxylation reaction during melt-processing converts carboxyl groups to volatile by-products. This study sheds light on the thermal-oxidation degradation mechanism of PLA/CNC composites and will be a valuable reference for further research into the degradation of other bio-based polymeric materials.

Compliance Checking of Cloud Providers: Design and Implementation

The recognition of capabilities supplied by cloud systems is presently growing. Collecting or sharing healthcare data and sensitive information especially during the Covid-19 pandemic has motivated organizations and enterprises to leverage the upsides coming from cloud-based applications. However, the privacy of electronic data in such applications remains a significant challenge for cloud vendors to adapt their solutions with existing privacy legislation standards such as general data protection regulation (GDPR). This article first proposes a formal model and verification for data usage requests of providers in a cloud composite service using a model checking tool. A cloud pharmacy scenario is presented to illustrate the connectivity of providers in the composite service and the stream of their requests for both collection and movement of patient data. A set of verifications is then undertaken over the pharmacy service in accordance with three significant GDPR obligations, namely user consent, data access, and data transfer. Following that, the article designs and implements a cloud container virtualization based on the verified formal model realizing GDPR requirements. The container makes use of some enforcement smart contracts to only proceed with the providers’ requests that are compliant with GDPR. Finally, several experiments are provided to investigate the performance of our approach in terms of time, memory, and cost.

Skyline-Enhanced Deep Reinforcement Learning Approach for Energy-Efficient and QoS-Guaranteed Multi-Cloud Service Composition

Cloud computing has experienced rapid growth in recent years and has become a critical computing paradigm. Combining multiple cloud services to satisfy complex user requirements has become a research hotspot in cloud computing. Service composition in multi-cloud environments is characterized by high energy consumption, which brings attention to the importance of energy consumption in cross-cloud service composition. Nonetheless, prior research has mainly focused on finding a service composition that maximizes the quality of service (QoS) and overlooks the energy consumption generated during service invocation. Additionally, the dynamic nature of multi-cloud environments challenges the adaptability and scalability of cloud service composition methods. Therefore, we propose the skyline-enhanced deep reinforcement learning approach (SkyDRL) to address these challenges. Our approach defines an energy consumption model for cloud service composition in multi-cloud environments. The branch and bound skyline algorithm is leveraged to reduce the search space and training time. Additionally, we enhance the basic deep Q-network (DQN) algorithm by incorporating double DQN to address the overestimation problem, incorporating Dueling Network and Prioritized Experience Replay to speed up training and improve stability. We evaluate our proposed method using comparative experiments with existing methods. Our results demonstrate that our approach effectively reduces energy consumption in cloud service composition while maintaining good adaptability and scalability in service composition problems. According to the experimental results, our approach outperforms the existing approaches by demonstrating energy savings ranging from 8% to 35%.

Using an Estimation of Distribution Algorithm to Achieve Multitasking Semantic Web Service Composition

Web service composition composes existing web services to accommodate users’ requests for required functionalities with the best possible quality of services (QoS). Due to the computational complexity of this problem, evolutionary computation (EC) techniques have been employed to efficiently find composite services with near-optimal functional quality (i.e., quality of semantic matchmaking, QoSM for short) or non-functional quality (i.e., QoS) for each composition request individually. With a rapid increase in composition requests from a growing number of users, solving one composition request at a time can hardly meet the efficiency target anymore. Driven by the idea that the solutions obtained from solving one request can be highly useful for tackling other related requests, multitasking service composition approaches have been proposed to efficiently deal with multiple composition requests concurrently. However, existing attempts have not been effective in learning and sharing knowledge among solutions for multiple requests. In this paper, we model the problem of collectively handling multiple service composition requests as a new multi-tasking service composition problem and propose a new Permutation-based Multi-factorial Evolutionary Algorithm based on an Estimation of Distribution Algorithm (EDA), named PMFEA-EDA, to effectively and efficiently solve this problem. In particular, we introduce a novel method for effective knowledge sharing across different service composition requests. For that, we develop a new sampling mechanism to increase the chance of identifying high-quality service compositions in both the single-tasking and multitasking contexts. Our experiment shows that our proposed approach, PMFEA-EDA, takes much less time than existing approaches that process each service request separately, and also outperforms them in terms of both QoSM and QoS.

Trust–SIoT: Toward Trustworthy Object Classification in the Social Internet of Things

The recent emergence of the promising paradigm of the Social Internet of Things (SIoT) is a result of an intelligent amalgamation of the social networking concepts with the Internet of Things (IoT) objects (also referred to as “things”) in an attempt to unravel the challenges of network discovery, navigability, and service composition. This is realized by facilitating the IoT objects to socialize with one another, i.e., similar to the social interactions amongst human beings. A fundamental issue that mandates careful attention is to thus establish, and over time, maintain trustworthy relationships amongst these IoT objects. Therefore, a trust framework for SIoT must include object-object interactions, the aspects of social relationships, credible recommendations, etc., however, the existing literature has only focused on some aspects of trust by primarily relying on the conventional approaches that govern linear relationships between input and output. In this paper, an artificial neural network-based trust framework, Trust–SIoT, has been envisaged for identifying the complex non-linear relationships between input and output in a bid to classify trustworthy objects. Moreover, Trust–SIoT has been designed for capturing a number of key trust metrics as input, i.e., direct trust by integrating both current and past interactions, reliability and benevolence of an object, credible recommendations, and the degree of relationship by employing knowledge graph embedding. Finally, we have performed extensive experiments to evaluate the performance of Trust–SIoT vis-á-vis state-of-the-art heuristics on two real-world datasets. The results demonstrate that Trust–SIoT achieves a higher F1-score and lower MAE and MSE scores.

Formal modelling and verification of scalable service composition in IoT environment

A system based on the internet of things (IoT) consists of services deployed across several devices that collaborate to fulfil IoT system goals. The growth in the number of IoT services that has occurred concurrently with the growth in the number of IoT devices is posing a significant difficulty for the process of service composition. In order to satisfy increasing demand and rapid expansion while keeping a certain level of quality of service, IoT systems need a scalable IoT service composition. However, building the correct scalable service composition is not guaranteed in IoT systems. This paper proposes formal modeling and verification of the scalability in IoT service composition at design time based on Event-B formal method. To fulfil the requirements of IoT service composition, the proposed model addresses more key qualities, mainly availability and interoperability. Further, by relying on the refinement technique, we create our model sequentially from the requirements analysis level to the target level. Finally, we validate and verify the correctness of the proposed formal model using of the Rodin platform and several proof obligations. Our verified model of the scalable IoT service composition has met its 44 proof obligations, and the Rodin prover was responsible for automatically addressing all of these proof obligations.

Service-oriented model-based fault prediction and localization for service compositions testing using deep learning techniques

As service-oriented computing systems become more buoyant and complex, the occurrence of faults dramatically increases. Fault prediction plays a crucial role in the service-oriented computing paradigm, aiming to reduce testing cost while maximizing testing quality to utilize testing resources effectively and increase the reliability of service compositions. Although various fault prediction techniques were considered in software testing, service-oriented systems were less fortunate, in which most of the studies have focused on single web services testing rather than service compositions. Moreover, mainly the detection of faulty/non-faulty services was addressed, ignoring the estimate of faults count, their severity, as well as predicting when and where such faults would occur. In this paper, a multilateral model-based fault prediction and localization approach is proposed using deep learning techniques for web service compositions testing rather than single web service testing, which uniquely predicts not only faulty services, but also their count and severity level, location of faults, and time at which faults would occur. Three deep learning models are investigated: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and a proposed hybrid model based on both CNN and RNN. The proposed approach is language-independent, as it adopts process metrics rather than code metrics to overcome the code unavailability concern of services. The experimental analysis adopted main performance metrics on multiple public datasets to evaluate its efficiency and effectiveness. The results indicated that the hybrid CNN_RNN model achieves an average accuracy range of 84%–95.7%, where the RNN and CNN models individually achieve 75%–90% and 70%-79.3% respectively. Thus, the hybrid model increases the accuracy level by 5%–10% and 15%–20%, while achieving the least mean square error of 30% and 60% compared to the RNN and CNN models respectively. In terms of time, the RNN model consumes less average time as of 30–50 ms for the different datasets of variant sizes compared to the CNN and hybrid CNN_RNN models that consume 79–102 and 177–224 ms respectively. Thus, RNN model consumes around 50%–80% less time than those of the CNN and hybrid models respectively.

Cloud manufacturing architectures: State-of-art, research challenges and platforms description

The increasing incorporation of digital technologies in industrial systems in recent years, and their corresponding connection to the Internet of Things, has made it possible to offer production services through the cloud, giving rise to the Cloud Manufacturing production paradigm. This paradigm allows customers and production service providers to meet on a virtual platform, and from there, to generate business opportunities. In turn, different production service providers can associate with each other, in such a way that each one can enhance the level of service they offer or even take advantage of their idle capacity by offering it to other providers. These advantages have increased the interest in Cloud Manufacturing, yielding a wide number of architectures and systems that enable these interactions. In this paper, we present a comprehensive study about these architectures, as well as a review of the current literature on the subject. For this, a systematic review of the literature is reported. Based on an in-depth review of the selected works, we were able to identify the main contributions of the current state of the literature about Cloud Manufacturing architectures. Four lines of research were identified that should be strengthened and expanded in the future to continue with the development of this paradigm. These lines are: (i) digital twins developments, (ii) scheduling and logistics, (iii) services composition and resources sharing and (iv) interoperability. Finally, it is presented an open-source project for Cloud Manufacturing architectures and their implementation, which emerge from the literature review as a promising tool to leverage Cloud Manufacturing technologies.

The Reintegration of victims of human trafficking: Approaches and Practices in Nepal

This research paper tries to analyze the shelter services and care supports to reintegrating the victims of human trafficking at the family and community level. Primarily, the study applied the qualitative approach by adopting both primary and secondary sources of information. For substantiating secondary information, 40 qualitative in-depth and key informant interviews were conducted. It was found that priorities of shelters are crucial for providing composite and comprehensive services for sustainable and dignified reintegration of victims of trafficking. The shelter care and services mainly orient to victim-centric approach to provide composite service package to victims. Immediate, short-term and long-term services play crucial role for successful reintegration in family and community. The multiple packages in shelter's lone effort was observed to be the best for delivering the best care and services. The widely applied approach of shelters was found victim centered approach, shelter based approach and the community approach. Among these approach, shelter based comprehensive service oriented approach found suitable for sustainable and dignified reintegration of victims of trafficking.

Service Re-Selection for Disruptive Events in Mobile Environments: A Heuristic Technique for Decision Support at Runtime

Modern service-based processes in mobile environments are highly complex due to the necessary spatial–temporal coordination between multiple participating users and the consideration of context information. Due to the dynamic nature of mobile environments, disruptive events occur at runtime, which require a re-selection of the planned service compositions respecting multiple users and context-awareness. Thereby, when re-selecting services the features performance, solution quality, solution robustness and alternative solutions are essential and contribute to the efficacy of service systems. This paper presents an optimization-based heuristic technique based on a stateful representation that uses a region-based approach to re-select services considering multiple users, context information and in particular disruptive events at runtime. The evaluation results, which are based on a real-world scenario from the tourism domain, show that the proposed heuristic is superior compared to competing artifacts.

A parallel approach for user-centered QoS-aware services composition in the Internet of Things

The Internet of Things (IoT) refers to an infrastructure of interconnected smart devices that aim to provide various services. The proliferation of IoT objects and devices offering functionally equivalent services but differing in their quality of service (QoS) levels makes the issue of services composition one of the biggest challenges for the service computing community. Various evolutionary-based approaches have been proposed in the literature to find sub-optimal service compositions in a reasonable computation time. However, most of these approaches have high composition time and/or a limited composition quality as they rely on a sequential exploration of the composition search space using a fixed size population. To address these limitations, a parallel differential evolution-based approach with population size reduction for QoS-aware service composition (PDE-QSC) is proposed in this paper. Unlike existing evolutionary-based approaches, the proposed approach is characterized by a parallel exploration of the composition space through a population size reduction strategy. Specifically, in this approach, the composition population is divided into two sub-populations. To reduce the composition time and improve the quality of the composition, the composition sub-populations evolve simultaneously using different evolution processes and are then merged to form a single population, thus increasing the population diversity. To further improve the performance in terms of composition time and composition quality, a linear reduction strategy is proposed to adaptively reduce the size of the composition population by eliminating compositions that do not meet the QoS requirements. Simulations based on real datasets demonstrate the superiority of the PDE-QSC approach over five baseline approaches and its suitability for large-scale IoT environments.

A Graph Neural Network and Pointer Network-Based Approach for QoS-Aware Service Composition

Quality-of-service (QoS)-aware service composition aims to aggregate multiple existing services to meet users&#x0027; complex functional and nonfunctional requirements that cannot be met by simple services. The accumulation of user tasks and service composition solutions makes it possible to mine empirical rules from those historical compositions to reduce the search space and thus improve the composition efficiency. Traditional empirical rule-based methods focus on mining with well-designed rules, ignoring the underlying correlations between tasks and between services. Meanwhile, infrequently used services are not valued by these methods, but these services may still be used in constructing optimal service solutions. In addition, many methods use reinforcement learning to compose services to efficiently construct service solutions, but they do not achieve the same effect as traditional metaheuristic methods. In view of the above shortcomings, considering the ability of graphs to express relationships, we first construct tasks and services as graphs and then use a graph neural network (GNN) to mine underlying correlations and predict the probability that each service will be used to construct the solution corresponding to the task. Next, based on these high-probability services, we utilize pointer network (PN)-based reinforcement learning to efficiently construct the initial service solution. The PN is often used to solve combinatorial optimization problems and is noninferior to metaheuristics for small-scale data. To increase the generalization ability of the network, we superimpose another layer on the PN. Finally, to take advantage of infrequently used services, we use the local whale optimization algorithm (WOA) to fine-tune the initial solution and obtain a high-quality solution. The experimental results show that our approach outperforms several existing methods in terms of both composition efficiency and solution quality.