Service Discovery Model Research Articles

Enabling Space-Aware Service Discovery Model in Home Networks through a Compatible Extension to mDNS/DNS-SD

The advent of the smart home, fueled by the rapid emergence of tiny embedded IoT devices and sensors, brings the consideration of space and location information to the forefront of service discovery. In a smart home environment, spaces often have composite capabilities and complex structures. Since residents may have varying preferences based on their location within different spaces, traditional service discovery results may be suboptimal without considering the spatial context. This paper introduces a seamless approach to integrate space and location information with mDNS/DNS-SD, a widely utilized service discovery protocol in home networks. We provide a formal specification for this approach, develop a prototype, and perform a series of experiments to evaluate the efficacy and potential of the proposed method.

A Self-Adaptive Service Discovery Model for Smart Cities

City services are frequently supported by software services that are managed by service-oriented architectures. However, a large number of software services is likely to cause performance issues when discovering software services. The distributed organisation of services information improves discovery performance. Existing research proposes to organise services information according to service location, domains, or city context, keeping that organisation constant under an assumption that cities do not change. However, cities are dynamic environments where entities interact, causing events that in turn, effect changes in the city. The organisation of services information must evolve or it will become outdated, negatively impacting discovery performance. We propose a self-adaptive service model for smart cities to support service discovery. This model adapts the organisation of services information according to city events. We introduce a self-adaptive architecture that keeps track of the discovery metrics and moves information about services between registries to maintain the discovery efficiency. We evaluate the proposed model in simulated environments and a real IoT testbed. Results show that our model outperforms competitors when reactive adaptation is triggered by a specific event. However, proactive adaptation needs further research. Results from the real IoT testbed present the costs of the proposed model.

A new SLA-aware method for discovering the cloud services using an improved nature-inspired optimization algorithm.

Cloud computing is one of the most important computing patterns that use a pay-as-you-go manner to process data and execute applications. Therefore, numerous enterprises are migrating their applications to cloud environments. Not only do intensive applications deal with enormous quantities of data, but they also demonstrate compute-intensive properties very frequently. The dynamicity, coupled with the ambiguity between marketed resources and resource requirement queries from users, remains important issues that hamper efficient discovery in a cloud environment. Cloud service discovery becomes a complex problem because of the increase in network size and complexity. Complexity and network size keep increasing dynamically, making it a complex NP-hard problem that requires effective service discovery approaches. One of the most famous cloud service discovery methods is the Ant Colony Optimization (ACO) algorithm; however, it suffers from a load balancing problem among the discovered nodes. If the workload balance is inefficient, it limits the use of resources. This paper solved this problem by applying an Inverted Ant Colony Optimization (IACO) algorithm for load-aware service discovery in cloud computing. The IACO considers the pheromones’ repulsion instead of attraction. We design a model for service discovery in the cloud environment to overcome the traditional shortcomings. Numerical results demonstrate that the proposed mechanism can obtain an efficient service discovery method. The algorithm is simulated using a CloudSim simulator, and the result shows better performance. Reducing energy consumption, mitigate response time, and better Service Level Agreement (SLA) violation in the cloud environments are the advantages of the proposed method.

SARL: A reinforcement learning based QoS-aware IoT service discovery model

Abstract The IoT environment includes the enormous amount of atomic services with dynamic QoS compared with traditional web services. In such an environment, in the service composition process, discovering a requested service meeting the required QoS is a di cult task. In this work, to address this issue, we propose a peer-to-peer-based service discovery model, which looks for the information about services meeting the requested QoS and functionality on an overlay constructed with users of services versus service nodes, with probably constrained resources. However, employing a plain discovery algorithm on the overlay network such as flooding, or k-random walk could cause high message overhead or delay. This necessitates an intelligent and adaptive discovery algorithm, which adapts itself based on users’ previous queries and the results. To fill this gap, the proposed service discovery approach is equipped with a reinforcement learning-based algorithm, named SARL. The reinforcement learning-based algorithm enables SARL to significantly reduce delay and message overhead in the service discovery process by ranking neighboring nodes based on users’ service request preferences and the service query results. The proposed model is implemented on the OMNet simulation platform. The simulation results demonstrate that SARL remarkably outperforms the existing approaches in terms of message overhead, reliability, timeliness, and energy usage efficiency.

Efficient service discovery in mobile social networks for smart cities

Mobile social networks (MSNs) play an important role in the process of the development of smart cities. Citizens can interact and engage with services provided by MSNs. Smart city services enhance their quality of life. With the popularity of smart phones, mobile social activities have become an important component of citizens’ daily life. People can post their social contents to their remote friends and can access shared information in the cycles of friends anytime and anywhere through their mobile devices. This human-centered social approach generates enormous amounts of social data that are distributed across various smart devices. Efficient service discovery from such cycles of friends is a fundamental challenge for MSNs. This paper proposes a friends’ cycle service discovery (FCSD) model for searching social services in MSNs based on human sociological theories and social strategies. In the proposed FCSD network, intelligent network nodes with common social interests can self-organize to interact and form social cycles with other potential nodes, and further can co-operate autonomously to identify and discover useful services from cycles of friends and cycles of friends’ friends. The proposed model has been simulated and evaluated in a decentralized mobile social environment with an evolving network. The experimental results show that the FCSD model exhibits better performance compared with relevant state-of-the-art services search methods.

HBSD

Enterprise cloud bus (ECBS) is a multi-agent-based abstraction layer framework, responsible for publishing and discovery of services in an Inter-cloud environment. Our work focuses on the service discovery model (HBSD) using Hadoop that leads to the challenges of automatic web service discovery patterns. It has been observed that the RDBMS can handle only data sizes up to a few Terabytes but fails to scale beyond that, so Apache Hadoop can be used for parallel processing of massive datasets. This article provides a novel Hadoop based Service Discovery (HBSD) approach that can handle vast amount of datasets generated from heterogeneous cloud services. The novelty of the proposed architecture coordinates cloud participants, automate service registration pattern, reconfigure discover services and focus on aggregating heterogeneous services from Inter-cloud environments. Moreover, this particle states a novel and efficient algorithm (HBSDMCA) for finding the appropriate service as per user's requirements that can provide higher QoS to the user request for web services.

Development of a mobile web services discovery and composition model

Mobile computing, a challenging paradigm aiming at using and providing services anywhere and anytime, places great challenges on dynamic service discovery and composition. These challenges are specially related to mobile context (terminal constraints, user mobility, network connection, etc.) In fact, the discovery model began with semantically rewriting and enriching services and user query with OWL-S ontology and its non-functional properties (context, QoWS and user preferences). Then a similarity calculation step has been performed between the enriched user query and the set of advertised web services to generate the most relevant ones. Furthermore, the resulting services will be the inputs of a mobile web service composition process. Mobile web service composition is based on a formal model aiming to compute dynamic context (Location, Bandwidth) uncertainty and web service sensitivity. These values contribute to select web services that will form the composition plan. The web service composition algorithm suggests to the end user, the most relevant plans sorted in ascending order according to their total sensitivity value. The implementation and comprehensive simulation experiments show the efficiency of the mobile web service discovery and composition models.

Tagging augmented neural topic model for semantic sparse Web service discovery

SummarySearch engine based Web service discovery model suffers from the semantic sparsity problem due to the fact that Web services are described in short texts, which in turn leads to poor recall. To address this issue, external information that enriches the semantics of the Web service and improves discovery performance has been highly concerned. In light of this, we propose a novel Web service discovery approach that uses the neural topic model, which seamlessly integrates tagging information and word embedding for semantic sparsity problem. More specifically, instead of clustering Web services as done in most existing service discovery approaches, we use word embedding to map the words as continuous embeddings to embody external semantics of the service description. We also leverage the neural topic model in service discovery, which takes continuous word distribution as the input and interprets the Web service description as a hierarchical model. Based on the neural topic model and word embedding, we propose an efficient Web service query and ranking approach. Experiments conducted on a real‐world Web service dataset demonstrate the effectiveness of the proposed approach.

Enhanced Service Discovery Model for Wireless Mesh Networks

Wireless mesh networks (WMNs) are the only cost-effective networks that support seamless connectivity, wide area network (WAN) coverage, and mobility features. However, the rapid increase in the number of users on these networks has brought an upsurge in competition for available resources and services. Consequently, factors such as link congestion, data collisions, link interferences, etc. are likely to occur during service discovery on these networks. This further degrades their quality of service (QoS). Therefore, the quick and timely discovery of these services becomes an essential parameter in optimizing the performance of service discovery on WMNs. In this paper, we present the design and implementation of an enhanced service discovery model that solves the performance bottleneck incurred by service discovery on WMNs. The proposed model integrates the particle swarm optimization (PSO) and ant colony optimization (ACO) algorithms to improve QoS. We use the PSO algorithm to assign different priorities to services on the network. On the other hand, we use the ACO algorithm to effectively establish the most cost-effective path whenever each transmitter has to be searched to identify whether it possesses the requested service(s). Furthermore, we design and implement the link congestion reduction (LCR) algorithm to define the number of service receivers to be granted access to services simultaneously. We simulate, test, and evaluate the proposed model in Network Simulator 2 (NS2), against ant colony-based multi constraints, QoS-aware service selection (QSS), and FLEXIble Mesh Service Discovery (FLEXI-MSD) models. The results show an average service discovery throughput of 80%, service availability of 96%, service discovery delay of 1.8 s, and success probability of service selection of 89%.

A novel service discovery algorithm via service repository centre in internet of things

In recent years, the internet of things (IoT) has attracted more and more attention, and IoT services have increased rapidly. The service discovery which has important implications for the service integration of IoT services has become more difficult in large-scale registration. In traditional matching method, in order to get a better match results, all the matching parameters for services had to be calculated together, thus it would waste a lot of computing resources and computing time. This paper propose a centralise four-layer model for service discovery via multi-stage semantic service matching to improve the performance of service discovery, namely interactive interface layer, parsing annotation layer, service matching layer and data semantic layer, so as to meet the requirement of service discovery in large-scale IoT services. We also propose a hybrid service matching degree measurement by synthetically calculating the concept logic and semantic similarity for each layer singly. Experimental analysis confirms the effectiveness of the proposed mechanism in improving the precision and recall.

A novel service discovery algorithm via service repository centre in internet of things

In recent years, the internet of things (IoT) has attracted more and more attention, and IoT services have increased rapidly. The service discovery which has important implications for the service integration of IoT services has become more difficult in large-scale registration. In traditional matching method, in order to get a better match results, all the matching parameters for services had to be calculated together, thus it would waste a lot of computing resources and computing time. This paper propose a centralise four-layer model for service discovery via multi-stage semantic service matching to improve the performance of service discovery, namely interactive interface layer, parsing annotation layer, service matching layer and data semantic layer, so as to meet the requirement of service discovery in large-scale IoT services. We also propose a hybrid service matching degree measurement by synthetically calculating the concept logic and semantic similarity for each layer singly. Experimental analysis confirms the effectiveness of the proposed mechanism in improving the precision and recall.

Decentralized service discovery and selection in Internet of Things applications based on artificial potential fields

The Internet of Things (IoT) vision involves a future Internet integrated with real-world objects that can commonly offer their functionality trough services. In such pervasive environments of IoT networks, locating and invoking suitable services is quite challenging and traditional service discovery and selection approaches have been proven inadequate. In this paper, taking inspiration from natural metaphors, a decentralized service discovery and selection model is proposed. The model is based on artificial potential fields (APFs) which are formed upon each user service request and become active at points where services can be provided. Such points are termed as service provision nodes (SPNs). The strength of each APF depends on the percentage of requested services that can be provided by the respective SPN, as well as on SPN service load and availability with the aim to balance service load among SPNs. Service discovery and selection is then driven by artificial forces applied among user service requests and SPNs. Simulation results indicate that the proposed approach maintains satisfactory performance and scalability as the number of SPNs in an IoT network increase and efficient load balancing of the requested services among the SPNs in comparison with other approaches.

A Socioecological Model for Advanced Service Discovery in Machine-to-Machine Communication Networks

The new development of embedded systems has the potential to revolutionize our lives and will have a significant impact on future Internet of Thing (IoT) systems if required services can be automatically discovered and accessed at runtime in Machine-to-Machine (M2M) communication networks. It is a crucial task for devices to perform timely service discovery in a dynamic environment of IoTs. In this article, we propose a Socioecological Service Discovery (SESD) model for advanced service discovery in M2M communication networks. In the SESD network, each device can perform advanced service search to dynamically resolve complex enquires and autonomously support and co-operate with each other to quickly discover and self-configure any services available in M2M communication networks to deliver a real-time capability. The proposed model has been systematically evaluated and simulated in a dynamic M2M environment. The experiment results show that SESD can self-adapt and self-organize themselves in real time to generate higher flexibility and adaptability and achieve a better performance than the existing methods in terms of the number of discovered service and a better efficiency in terms of the number of discovered services per message.

An Efficient Cluster Based Service Discovery Model for Mobile Ad hoc Network

The use of web service has been increased rapidly, with an increase in the number of available services, finding the exact service is the challenging task. Service discovery is the most significant job to complete the service discoverers needs. In order to achieve the efficient service discovery, we focus on designing a cluster based service discovery model for service registering and service provisioning among all mobile nodes in a mobile ad hoc network (MANETs). A dynamic backbone of nodes (i.e. cluster heads) that forms a service repository to which MANET nodes can publish their services and/or send their service queries. The designed model is based on storing services with their service description on cluster head nodes that are found in accordance with the proposed cluster head election model. In addition to identifying and analyzing the system parameters for finding the effectiveness of our model, this paper studies the stability analysis of the network, overhead of the cluster, and bandwidth utilization and network traffic is evaluated using analytic derivations and experimental evaluation has been done.

A Web Service Discovery based on QoS Negotiation Approach

of Service (QoS) support in web services plays a great role for the success of web service selection. There exist many web services which has similar functional characteristics. Therefore, the massive growth of web services and also the forked view of services by both the parties (service provider and service consumer) make the task of service selection a complex job. To resolve this issue on divergent view of services, and to select an appropriate services, this paper propose Service discovery and selection model based on QoS Negotiation model that aims to facilitate the service selection process to consumer .QoS Negotiation model is responsible for creating negotiation with the service provider about the services that accept negotiation on its QoS attributes. After the success of negotiation process the service provider update the value of services attribute in the UDDI. The results show that the negotiation model enhances the discovery system performance and usability.

Cross-Language Semantic Web Service Discovery to Improve the Selection Mechanism by using Data Mining Techniques

Web service became one of the important methods for communication through the internet and its usage increased in the levels of users and developers. Semantic web service represents the second generation of web services that contains more description and information about its contents. Searching and dealing with web service is done through process called web service discovery which returns a Semantic Web Service Description Language file (SWSDL) for each web service. This research aims to expand the semantic web service usage through adding the multilanguage capability to the web service’s discovery process and through recommending other web services to the user based on his history in using web services. These aims were achieved by modifying the web service discovery model through adding two important techniques the Cross Language Information Retrieval (CLIR) technique and the data mining association rules technique. This research proposed two sub models, the first sub model proposed the application of CLIR techniques and information retrieval method to support Bilingual Web service discovery process the second language that proposed here is Arabic. Text mining techniques were applied on SWSDL content and user’s query to be ready for CLIR methods, this sub model was tested on a curated catalogue of Life Science Web Services http://www.biocatalogue.org/ and achieving 99.38 % accuracy and 87.23 precision of the effectiveness of the monolingual system. The second sub model proposed a process of web service recommendation by applying the data mining techniques to suggest another web service beside the one he got from the discovery process based on the user’s history. This sub model was tested on the mention curated web services site and the results were 65 % of users chose services from the services that recommended by the proposed sub model.

A Normalized Approach for Service Discovery

In today's world web services are the known perception to all the users who uses the internet. The Web Service process involves service discovery, selection and ranking. Discovery is the process of matchmaking of user query with advertisements in the repository. Our motivation is to develop a model for web service discovery with the combined approach of service selection and ranking. In this paper, we have proposed a technique for web service discovery process, combining the keyword search and semantic search and ranking the services. The implementation results show that the proposed model performs better for the web service discovery process.

A Trustworthy Reputation Approach for Web Service Discovery

Abstract Trust and Reputation for Web Services arises as an important research issue in Web Service discovery. To the best of our knowledge, current Web Service Reputation models do not include the trust factor in the calculation of Web Service Reputation. In this paper, we propose a Web Service discovery and selection model based on reputation model that can overcome such limitations by considering consumer trust factor when calculating Web Service Reputation. In addition, the trust Negotiation approach is included in the selection process. The proposed approach is implemented and test. The results show that including the consumer trust factor in calculating the Web Service Reputation affects positively as it allows the system to behave systematically and gives more trustworthy results in the discovery process.

A Dynamic Services Discovery Model for Better Leveraging BPM and SOA Integration

This work addresses the problem of BPM-SOA integration using the services discovery activity as the main driver for that. The developed model is able to select the most adequate (web) services for given business transactions that are modeled at BPM level, regarding their business' contexts and QoS restrictions. It considers an inter-organization scenario, where services are published and made available by disparate providers over largely distributed and heterogeneous repositories, which can also be accessed under the SaaS model. Key elements of the model include a business processes catalog and an ontology based on the UBL open standard, and a QoS ontology. The implemented framework strongly relies on open ICT standards at all tackled levels, facilitating interoperability and the integration of services discovery environment in wider enterprise architecture projects. A software prototype has been implemented and results are described at the end. In spite of a set of assumptions, it was realized that a focus on the dynamic services discovery activity can be a feasible approach for better levering BPM-SOA integration, providing a more flexible basis for enterprises when executing their daily business transactions.

A Trustworhty Model for Reliable Cloud Service Discovery

computing is a new model for delivering new applications and services. Its adoption is gaining ground because most of the services provided by the cloud are of low cost and readily available for use. Despite many promises by the cloud service providers, users remain much concerned about the general risk associated with the adoption of the cloud. The availability of many cloud service providers on one hand promotes competition in the cloud market and gives end users more freedom to choose the best cloud provider however it became a tedious and time consuming task for potential cloud users to evaluate and compare the available cloud offerings in the market. Hence, discovering a reliable service is a daunting task. This research proposed a trustworthy model for reliable cloud service discovery.