Showing papers on "Services computing published in 2021"

Privacy Preserving Location-Aware Personalized Web Service Recommendations

Abstract: The personalized Web service recommendation based on Quality of Service (QoS) is gaining increasing popularity due to its promising ability to help users find high quality services. Studies suggest that it is beneficial to use Collaborative Filtering (CF)-based techniques to facilitate Web service recommendations which can achieve high accuracy in predicting the QoS for unobserved Web services. With the QoS, location of users and Web services has been another significant factor in predicting the QoS values. The more factors that are available to the service providers, the more accurate predictions can be generated. However these factors are privacy sensitive and therefore it is risky to disclose them to any third party service provider. To address this challenge, in this paper we develop a privacy preserving protocol to predict missing QoS values and thereby providing Web service recommendations based on past QoS experiences and locations of users. Our protocol is able to achieve user privacy by means of encrypting the QoS and location as well as to select suitable Web services for users without disclosing any private information. We conduct extensive experimental analysis on publicly available data sets and prove that our method is both secure and practical.

An internet of things service roadmap

Abstract: We propose a roadmap for leveraging the tremendous opportunities the Internet of Things (IoT) has to offer. We argue that the combination of the recent advances in service computing and IoT technology provide a unique framework for innovations not yet envisaged, as well as the emergence of yet-to-be-developed IoT applications. This roadmap covers: emerging novel IoT services, articulation of major research directions, and suggestion of a roadmap to guide the IoT and service computing community to address key IoT service challenges.

Reliability-Aware and Deadline-Constrained Mobile Service Composition Over Opportunistic Networks

Abstract: An opportunistic link between two mobile devices or nodes can be constructed when they are within each other’s communication range. Typically, cyber–physical environments consist of a number of mobile devices that are potentially able to establish opportunistic contacts and serve mobile applications in a cost-effective way. Opportunistic mobile service computing is a promising paradigm capable of utilizing the pervasive mobile computational resources around the users. Mobile users are thus allowed to exploit nearby mobile services to boost their computing capabilities without investment in their resource pool. Nevertheless, various challenges, especially its quality-of-service and reliability-aware scheduling, are yet to be addressed. Existing studies and related scheduling strategies consider mobile users to be fully stable and available. In this article, we propose a novel method for reliability-aware and deadline-constrained service composition over opportunistic networks. We leverage the Krill–Herd-based algorithm to yield a deadline-constrained, reliability-aware, and well-executable service composition schedule based on the estimation of completion time and reliability of schedule candidates. We carry out extensive case studies based on some well-known mobile service composition templates and a real-world opportunistic contact data set. The comparison results suggest that the proposed approach outperforms existing ones in terms of success rate and completion time of composed services. Note to Practitioners —Recently, the rapid development of mobile devices and mobile communication leads to the prosperity of mobile service computing. Services running on mobile devices within a limited range are allowed to be composed to coordinate through wireless communication technologies and perform complex tasks and business processes. Despite its great potential, mobile service compositions remains a challenge since the mobility of users and devices imposes high unpredictability on the execution of tasks. A careful investigation into existing methods has found their various limitations, e.g., assuming time-invariant availability of mobile services. This article presents a novel reliability-aware and deadline-constrained service composition method for mobile opportunistic networks. Instead of assuming time-invariant availability of mobile nodes, the proposed method is capable of estimating service availability at run-time and leveraging a Krill–Herd-based algorithm to yield the deadline-constrained, reliability-aware, and well-executable service composition schedules. Case studies based on well-known service composition templates and real-world data sets suggest that it outperforms traditional ones in terms of success and completion time of composed services. It can thus aid the design and optimization of composite services as well as their smooth execution in a mobile environment. It can help practitioners better manage the reliability and performance of real-world applications built upon mobile services.

Service Computing for Industry 4.0: State of the Art, Challenges, and Research Opportunities

Abstract: Recent advances in the large-scale adoption of information and communication technologies in manufacturing processes, known as Industry 4.0 or Smart Manufacturing, provide us a window into how the ...

MHO: meta heuristic optimization applied task scheduling with load balancing technique for cloud infrastructure services

Abstract: The cloud computing provides on demand access to shared resources over internet in a cloud platform powerfully adaptable and metered way. Cloud computing empowers the user get to wherever to a shared pool of configurable resources and gives different administrations to the resource assignment like scientific operations, services computing through virtualization. To give guaranteed productive execution to clients, tasks ought to be proficiently mapped to accessible resources. In this manner, Task Scheduling is noteworthy issue in the cloud infrastructure administrations. The essential target of task execution planning includes reserving the infrastructure assets and limiting the goal of the execution plan. In this research work, we proposed metaheuristic optimization technique with load balancing to enhance the cloud infrastructure service provider’s performance there by depleting the scheduling issues. The proposed technique is pertinent for static and dynamic task condition, where static methods VM parameters are fixed, dynamic means parameters are chosen runtime. The proposed algorithm consists of two phases MHOS-S and MHO-D for dealing with static and dynamic properties of the task submitted. The result analysis by comparing with few traditional metaheuristic algorithms proves that the proposed technique performs better in complex environments.

Meta heuristic QoS based service composition for service computing

Abstract: In Service Computing, Service selection plays a vital role in delivering an appropriate service to the end user based on the request. Service composition methodology is the major factor affecting the appropriate need for the user or the consumer. The proposed technique involve the use of hybrid meta heuristics genetic algorithm with tabu search to retrieve the best suitable web service to the end user based on the Quality of service parameters. The existing techniques use the parameters response time, cost and reliability. The technique used in hybrid algorithm is used computes the availability of service, response time, throughput and interoperability between the services. Hence the result of service composition gives high reliable service to the end user with maximum throughput and interoperability. The location based service selection mechanism is considered for service composition as almost 90% of the services are available in cloud.

Cloud Computing—Security, Issues, and Solutions

Abstract: Security is the most concerned aspect of cloud computing because data is located in different places around the globe and new threats are arising day by day. Data privacy and security protection are the most important concerns in cloud computing technology and are related to both hardware and software. This paper gives an overview of security issues, challenges, and proposed solutions. A very clear and classified overview is presented in this paper with respect to cloud security. Today’s cloud computing provides the best and most efficient solutions to the Information and Communication Technology (ICT) industry, but the security problems are like nightmare for the cloud service providers as well as for the customers. We have also described various service and deployment models and identified major issues and challenges. This paper has also proposed some of the vital solutions with respect to privacy and security and also focus on various vulnerabilities and known security threats and attacks.

A Web Service Clustering Method Based on Semantic Similarity and Multidimensional Scaling Analysis

Abstract: Clustering web services is an effective method to solving service computing problems. The key insight behind it is to extract the vectors based on the service description documents. However, the brevity of natural language service description documents typically complicates the vector construction process. To circumvent the difficulty, we propose a novel web service clustering method to vectorize documents based on the semantic similarity, which can be calculated via WordNet and multidimensional scaling (WMS) analysis. We utilize the dataset from the ProgrammableWeb to conduct extensive experiments and achieve prominent advances in precision, recall, and F-measure.

Service Coordination in the Space-Air-Ground Integrated Network

Abstract: The space-air-ground integrated network (SAGIN) is regarded as a promising approach for providing ubiquitous Internet access anytime and anywhere. With virtualization technologies and multi-access edge computing, data transmission and data processing in SAGINs are abstracted as services. Space-air-ground service computing flexibly integrates and manages these services in SAGINs based on service-oriented architecture. However, it is significant but very challenging to provide Internet of Things service with high QoS in space-air-ground service computing due to the distributed service management, and the mobility of both infrastructures and users. Therefore, in this article, we investigate service coordination to guarantee the QoS in space-air-ground service computing. In particular, we first introduce three service coordination scenarios: fine-grained, medium-grained, and coarse-grained service coordination. Then we design a service coordination framework that contains three tiers: edge node tier, service function routing tier, and global control tier. After that, we propose a service coordination approach to reduce the service delay at low cost, which considers the selection with foresight and updates based on threshold. Experimental results show the advantages of our service coordination approach in terms of service delay and cost.

Optimized Web Service Composition Using Evolutionary Computation Techniques

Abstract: In service computing, Quality of Service (QoS)-aware web service composition is considered as one of the influential traits. To embrace this, an optimal method for predicting QoS values of web service is implemented where credibility evaluation is computed by accumulating reputation and trustworthiness. An automatic approach for weight calculation is invoked to calculate the weight of QoS attributes; it improves WS QoS values. QoS value is optimized by using Genetic Algorithm. Services with high QoS values are taken as candidate services for service composition. Instead of just selecting services randomly for service composition, cuckoo-based algorithm is used to identify optimal web service combination. Cuckoo algorithm realizes promising combinations by replacing the best service in lieu of worst service and by calculating the fitness score of each composition. A comparative study proved that it can provide the best service to end-users, as cuckoo selects only service composition with high fitness score.

NTSC: a novel trust-based service computing scheme in social internet of things

Abstract: Recently, data-based services have played a significant role in satisfying various of requirements of people in social IoT. Since data is the basis of data-based service, therefore, it is significant to obtain trust data to enhance trust services. Few researches optimized trust issue of services from this point of view before. Therefore, based on this domain, this paper proposes a novel service computing framework to fundamentally form trust-based services via trust-based data in the social IoT, which mainly consists of two schemes. Data trustworthy is relevant to trustworthy of data providers in the social networks, therefore, we propose a trust evaluation scheme to compute trustworthy for data providers with assistance of trusted static sensor devices in edge computing, that comprehensively computes trustworthy by considering both temporal and quality factors. This process ensures to generate trust services from data source. Then, to improve precision of service evaluation in the updating process, a trust-based service evaluation scheme is proposed to compute service evaluation by fully considering trustworthy of users, which consists of two evaluation phases, local and global evaluation respectively. Finally, extensive experiment is conducted to validate efficiencies of the proposed scheme. Compared to traditional scheme, data trustworthiness can be improved by 26.4% and thus service trustworthy is improved by 26.4% accordingly. The precision of service evaluation is improved by 20.79%.

Architecting digital products and services

Abstract: Enterprises are currently transforming their strategy, processes, and their information systems to extend their degree of digitalization. The potential of the Internet and related digital technologies, like Internet of Things, services computing, cloud computing, artificial intelligence, big data with analytics, mobile systems, collaboration networks, and cyber physical systems both drives and enables new business designs. Digitalization deeply disrupts existing businesses, technologies and economies and fosters the architecture of digital environments with many rather small and distributed structures. This has a strong impact for new value producing opportunities and architecting digital services and products guiding their design through exploiting a Service-Dominant Logic. The main result of the book chapter extends methods for integral digital strategies with value-oriented models for digital products and services which are defined in the framework of a multi-perspective digital enterprise architecture reference model.

Towards big services: a synergy between service computing and parallel programming

Abstract: Over the last years, cloud computing has emerged as a natural choice to host, manage, and provide various kinds of virtualized resources (e.g., software, business processes, databases, platforms, mobile and social applications, etc.) as on-demand services. This “servicelization” across various domains has produced a huge volume of data, leading to the emergence of a new service model, called big service. This latter consists of the encapsulation, abstraction and the processing of big data, allowing then to hide their complexity. However, this promising approach still lacks management facilities and tools. Indeed, due to the highly dynamic and uncertain nature of their hosting cloud environments, big services together with their accessed data need continuous management operations, so that to maintain a moderate state and high quality of their execution. In this context, frameworks for designing, composing, executing and managing big services become a major need. The purpose of this paper is to provide an understanding of the new emerging big service model from the lifecycle management phases’ point of view. We also study the role of big data frameworks and multi-cloud strategies in the provisioning of big services. A research road map on this topic will be summarized at the end of this paper.

A review on the integration of deep learning and service-oriented architecture

Abstract: In recent years, machine learning has been used for data processing and analysis, providing insights to businesses and policymakers. Deep learning technology is promising to further revolutionize this processing leading to better and more accurate results. Current trends in information and communication technology are accelerating widespread use of web services in supporting a service-oriented architecture (SOA) consisting of services, their compositions, interactions, and management. Deep learning approaches can be applied to support the development of SOA-based solutions, leveraging the vast amount of data on web services currently available. On the other hand, SOA has mechanisms that can support the development of distributed, flexible, and reusable infrastructures for the use of deep learning. This paper presents a literature survey and discusses how SOA can be enabled by as well as facilitate the use of deep learning approaches in different types of environments for different levels of users.

Electronic mortgage brokering and monitoring

Abstract: Provided is a financial transaction arrangement 1 which generally comprises a distributed processing arrangement and includes an identification service computing system 2, a financial institution computing system 3, a property registry computing system 4, a lender computing system 5, an appraiser 6, and a mortgage brokering computing system 8. All of these computing systems 2, 3, 4, 5 and 8 are interconnected by means of communications network 200 which incorporates a blockchain. Via a number of transactions, the mortgage brokering computing system 8 generates aggregate blockchains on the network 200 able to provide a demonstrable and auditable history for captured identification details and subsequent transactions required for automatically brokering a mortgage.

ServiceBERT: A Pre-trained Model for Web Service Tagging and Recommendation

Abstract: Pre-trained models have shown their significant values on a number of natural language processing (NLP) tasks. However, there is still a lack of corresponding work in the field of service computing to effectively utilize the rich knowledge accumulated in the Web service ecosystem. In this paper, we propose ServiceBERT, which learns domain knowledge of Web service ecosystem aiming to support service intelligence tasks, such as Web API tagging and Mashup-oriented API recommendation. The ServiceBERT is developed with the Transformer-based neural architecture. In addition to using the objective of masked language modeling (MLM), we also introduce the replaced token detection (RTD) objective for efficiently learning pre-trained model. Finally, we also implement the contrastive learning to learn noise-invariant representations at the sentence level in pre-training stage. Comprehensive experiments on two service-related tasks successfully demonstrate the better performance of ServiceBERT through the comparison with a variety of representative methods.

Enhancing service maintainability by monitoring and auditing SLA in cloud computing

Abstract: Enforcing Service Level Agreements (SLA) on service provisioning is a challenge in cloud computing environments. This paper proposes an architecture for multiparty (provider and client) auditing in cloud computing to identify SLA deviations. The architecture uses inspectors (software agents) and an independent auditor (third party) to collect SLA metrics from these parties. Privacy is preserved by using the separation of duties for all associated entities (inspectors and auditors). Additionally, service computing surges are automatically detected and handled using machine learning, avoiding performance bottlenecks and misinterpretation of measured SLA items. Thus, this paper improves service maintainability by avoiding service design changes when the service faces performance issues.

Architecting Intelligent Digital Systems and Services

Abstract: Our paper gives first answers on a fundamental question: How can the design of architectures of intelligent digital systems and services be accomplished methodologically? Intelligent systems and services are the goals of many current digitalization efforts today and part of massive digital transformation efforts based on digital technologies Digital systems and services are the foundation of digital platforms and ecosystems Digitalization disrupts existing businesses, technologies, and economies and promotes the architecture of open environments This has a strong impact on new value-added opportunities and the development of intelligent digital systems and services Digital technologies such as artificial intelligence, the Internet of Things, services computing, cloud computing, big data with analytics, mobile systems, and social enterprise networks systems are important enablers of digitalization The current publication presents our research on the architecture of intelligent digital ecosystems of products and services influenced by the service-dominant logic We present original methodological extensions and a new reference model for digital architectures with an integral service and value perspective to model intelligent systems and services that effectively align digital strategies and architectures with artificial intelligence as main elements to support intelligent digitalization

SRaSLR: A Novel Social Relation Aware Service Label Recommendation Model

Abstract: With the rapid development of new technologies such as cloud, edge and mobile computing, the number and diversity of available services are dramatically exploding and services have become increasingly important to people's daily work and life. As a consequence, using service label recommendation techniques to automatically categorize services plays a crucial role in many service computing tasks, such as service discovery, service composition, and service organization. There have been many service label recommendation studies that have achieved remarkable performance. However, these studies mainly focus on using the text information in service profiles to recommend labels for services while overlooking those social relations that widely exist among services. We argue that such social relations can help to obtain more precise recommendation results. In this paper, we propose a novel Social Relation aware Service Label Recommendation model called SRaSLR, which combines text information in service profiles and social network relations among services. A deep learning based model is constructed based on feature fusion of the two perspectives. We conduct extensive experiments on the real-world Programmable Web dataset, and the experiment results show that SRaSLR yields better performance than existing methods. Additionally, we discuss how service social network affects service label recommendation performance based on the experiment results.

Edge Intelligence as a Service

Abstract: Edge Intelligence is the umbrella term for new types of applications, which are being created due to the advent of Internet of Things and the resulting Edge Computing paradigm. Computing resources are pushed to the edge of the network to overcome the massive amounts of generated data, enable ultra low latency applications and guarantee privacy. Edge Intelligence use cases are based on context-aware AI applications and generate new knowledge from heterogeneous data sources. While the promise of these new applications sounds appealing, the reality is that we are still in the infant stage of building such platforms. We motivate the design of this platform by presenting a motivating use case that spans from Food Computing to Smart Health. Based on this, we identify main tasks encountered in the development of AI applications, describe issues related to Edge Intelligence, and present our vision of an Edge Intelligence as a Service platform.

Cloud as platform for monetizing complementary data for AI-driven services: A two-sided cooperative game

Abstract: In this paper, we design a strategic game that aims to deliver complementary data services among multiple data providers over a cloud intermediary platform. More specifically, we formalize the problem as an extended two-sided market model by courting on one side some influential data providers in order to attract other data providers on the same side to form a bundling of data services.

Software Services Engineering Manifesto - A Cross-Cutting Declaration

Abstract: As we have entered the Internet-of-Things (IoT) era, further blessed with rapid advances in several key technological areas including DevOps, AI/ML, 5G/6G/, neurocomputing, to name a few, it is imperative we think big and aim high. This new venture will require professionals in both software engineering and services computing to collaborate with an unprecedented intensity, and jointly develop the new interdisciplinary field hereby named Software Services Engineering (SSE). In SSE, the ever-deepening system dynamics emerging from both environments and humans in varying contexts are imposing steep challenges to both researchers and practitioners. Humans, both developers and the vast number of end users, are embedded ever closer to IoT environments, and are being afforded ample opportunities to continuously inject inputs during system development and after deployment. In fact, humans are increasingly playing the roles of both sensor and actuator. Traditional requirements engineering researchers are being lured more than ever into exploiting the IoT environments where human users are deeply embedded, to gather contextual information that inevitably introduces lots of ambiguity and uncertainty. Provisioning of highly adaptable and scalable microservices would be key to timely meeting ever-changing human desires and ever-evolving system requirements in the nimblest manner. As such, an ultra-agile and field-programmable development methodology and environment will be imperative to achieving such ultrafine grained microservices provisioning. Such ultra-agility and ultrafine granularity requirements imposed to the services industry obligate company executives to expect extreme manageability assurance to become the centroid of system operations and administration. The ultimate goal in pursuit of such a noble dream will be to provide genuinely individualized and trustworthy service, possibly enabled by AI, but it should be both explainable and ethical. Facing such grand challenges, this declaration samples a subset of burning issues in SSE through observations in seven themes, only meant to be starting points for the SSE community to further investigate. Through our declarations we also call for heightened attention to an assorted array of existing, barely emerging or non-existent services computing and software engineering methods for a concerted effort to research and explore.

Definition and Induction of a Specification Order Relation between Capabilities

Abstract: Capability-driven engineering plays a key role in advanced information systems. Service discovery and business process reuse, for instance, are firstly based on the functionalities they achieve. Semantic models based on state shift fail to provide scalable solutions for the discovery and reuse of software artifacts based on their functionalities. In this paper, we are interested in defining and inferring a specification partial order relation that enables building capability-based indexing structures. We rely on our previously developed capability model which describes capabilities via ontological features that characterize and specify the functionality achieved by a given service. We formally define a partial order specification relation between two capabilities. Moreover, we define a set of inference rules enabling the deduction of such partial order relation. The implementation details of our approach are presented, with an in-depth evaluation on shipping services, for a fair assessment of its usefulness and convenience.

Forecasting Time-aware Nonfunctional Service Attributes Using Deep Learning

Abstract: Service Oriented Architecture (SOA), as a new architecture paradigm, has been dramatically developed. A growing number of Web services have been developed and published on the Web, which increases the burden of service selection in system construction. Nonfunctional attributes of services have an essential impact on service selection. Users may observe the dynamic nonfunctional property values of different web services in various locations due to the dynamic attributes of various web services. How to accurately predict user perception of nonfunctional attributes of services has become an important problem in services computing. Towards this issue, this paper improves the personalized LSTM model by adding the attention mechanism to obtain more accurate vectorization representations of users and services, so as to better forecast missing nonfunctional service attributes. The proposed approach is evaluated on a real-world dataset, and experiment results show its feasibility.

Automatic Hierarchical Reinforcement Learning for Reusing Service Process Fragments

Abstract: Prevailing research trend is to use Web services for data publishing and sharing among organizations, but existing works often fall short of service reuse. Developing efficient solutions to achieve composite services has drawn significant attention in services computing. Services and service process fragments reuse is critical to improve the efficiency of software development and economize on human and material resources, meanwhile Reinforcement Learning (RL) is one commonly used approach in services computing. However, in service composition and service process fragments (SPFs) reusing scenarios, traditional RL methods cannot guarantee good efficiency for large-scale service processes construction problems. In this paper, we present a novel SPF reusing framework that combines automatic Hierarchical Reinforcement Learning (HRL) and extended Cocke-Kasami-Younger (CKY) algorithm. This framework has the ability to reuse any granularity of SPFs. We firstly get action models and trajectories by means of analysis on historical service process fragments. Furthermore, the “Causal Analysis” identifies the causal relationships among the actions in a trajectory, i.e. returning a causally annotated trajectory (CAT). Then, we utilize the SPF-Hierarchy algorithm to discover a coherent task hierarchy for each service process fragment. Finally, we map the hierarchy obtained from the previous stage to the HRL-CKY algorithm, which can fulfill the reuse and retrieval of any granularity of SPFs. The effectiveness and robustness of our approach are evaluated through a set of experiments.

Optimization of service addition in multilevel index model for edge computing

Abstract: With the development of Edge Computing and Artificial Intelligence (AI) technologies, edge devices are witnessed to generate data at unprecedented volume. The Edge Intelligence (EI) has led to the emergence of edge devices in various application domains. The EI can provide efficient services to delay-sensitive applications, where the edge devices are deployed as edge nodes to host the majority of execution, which can effectively manage services and improve service discovery efficiency. The multilevel index model is a well-known model used for indexing service, such a model is being introduced and optimized in the edge environments to efficiently services discovery whilst managing large volumes of data. However, effectively updating the multilevel index model by adding new services timely and precisely in the dynamic Edge Computing environments is still a challenge. Addressing this issue, this paper proposes a designated key selection method to improve the efficiency of adding services in the multilevel index models. Our experimental results show that in the partial index and the full index of multilevel index model, our method reduces the service addition time by around 84% and 76%, respectively when compared with the original key selection method and by around 78% and 66%, respectively when compared with the random selection method. Our proposed method significantly improves the service addition efficiency in the multilevel index model, when compared with existing state-of-the-art key selection methods, without compromising the service retrieval stability to any notable level.

Distributed Service Composition in Internet of Services

Abstract: To meet coarse-grained user requirements, many service composition approaches have been put forward in the past 20 years. The service composition constructs a service workflow by aggregating services to meet users’ functional and non-functional requirements. In the era of the Internet of Services (IoS), service solutions are constituted by services deployed on distributed platforms and owned by different providers. For reasons such as commercial confidentiality, different providers or platforms (called "IoS nodes") generally do not share complete information of services to other nodes. Thus, how to efficiently construct optimal service composite solutions for complex user requirements under this distributed service environment is a great challenge. In this paper, we propose a three-stage distributed service composition method with coordination protocols and local algorithms. In the first stage, when an IoS node receives a requirement (the initial node), it uses a centralized service composition algorithm and broadcasts sub-requirements it can’t meet to all other nodes through a requirement broadcasting protocol. In the second stage, the initial node uses a node selection algorithm to assign a node to provide the service for each sub-requirement. In the third stage, the nodes coordinate their services with other nodes through a constraint coordination protocol based on the upper confidence bound algorithm and the initial node obtains a composite service solution. The experimental results demonstrate that our method outperforms other distributed service composition methods in terms of efficiency and solution quality.