An Adaptive Multi Agent Service Discovery for Peer to Peer Cloud Services

Background/Objectives: This article proposes a novel methodology based on rubrics and feature-based schemes for the appraisal and comparison of approaches to semantic Web services composition and discovery. Methods/Statistical analysis: In order to evaluate the Semantic Web services composition and discovery approaches we created a new framework called RFSWS. This framework is the combination of traditional feature-based evaluation schemes and newly developed analytic rubrics tables. Five recently introduced prominent Semantic Web services composition approaches were identified, explained, and then evaluated/compared using RFSWS. Findings: In this work we determined aspects of Semantic Web services composition and discovery processes that can be evaluated as performance criteria in rubric tables. This is a novel application of rubrics, which have traditionally been used for grading student performance by teachers. We created a novel framework called RFSWS consisting of rubric tables and a feature-based evaluation scheme for the evaluation and comparison of Semantic Web service discovery and composition approaches, and applied it in the evaluation of five recently introduced prominent Semantic Web services composition approaches. Considering the shortcomings of existing Semantic Web services composition approaches that were discovered through this evaluation, we proposed an idealized dynamic Semantic Web service discovery and composition method, a yardstick by which all future Semantic Web services composition approaches can be evaluated. Application/ Improvements: Our novel RFSWS framework can be applied in the comprehensive and systematic evaluation/comparison of Semantic Web services discovery and composition approaches.

With the increasing use of web services in everyday tasks, we are entering an era of Internet of Services (IoS). Service discovery and selection has become critical issue in the area of web services. Traditional service discovery approaches are often supported by centralized registries that could suffer from single point failure, performance bottleneck, and scalability issues in large scale systems. To address these issues, in this paper we propose a context-aware service discovery and selection approach in a decentralized peer-to-peer environment. In the approach homophily similarity was used for bootstrapping and distribution of nodes. The discovery process is based on the similarity of nodes, previous interaction and behaviour of the nodes, which will help the discovery process in a dynamic environment. Our approach is not only considering service discovery, but also the selection of the best available web service by taking into account the QoS properties of the web services. Experimental results were based on real world semantic web service dataset, the results showed that the approach achieved better performance and efficiency in both discovery and selection processes.

Dynamic network models are widely employed for system analysis and performance evaluation of computer and communication systems. In dynamic networks each arc is weighted with a capacity and a transit time. The former is the maximum amount of flow that can travel through the arc per unit time, the later is the time period needed for flow to travel through the arc. In this article, an additional weight operation probability, is assigned to each arc to note the capability of the arc's successful transition. The performance of dynamic networks is defined by the probability that the maximum dynamic flow from source to sink within required time horizon is no less than specified threshold. To the best of our knowledge, no existing article had considered dynamic flow and network reliability simultaneously. Based on facts that there is always a maximum dynamic flow among the temporally repeated flows and the maximum temporally repeated flow can be obtained via an equivalent minimum cost maximum flow problem, we present a pioneering algorithm to compute performance of dynamic networks by solving minimum cost maximum flow and two-terminal capacity reliability at the same time.

Due to the emergence of Internet technologies and service-oriented computing, there is a rapid growth in the quantity and variety of services on the web. Discovering the web services as per the request is not an easy task because of the advancement of service-oriented computing which includes web services, cloud services, mobile services, etc. These services are dynamic and published according to the emerging standards in repositories. Web service clustering plays a crucial role in web service discovery. When services are grouped according to the similarity, then it reduces the search space and time, so that services can be discovered efficiently. Many eminent researchers have proposed approaches for efficient web service discovery by incorporating web service clustering. In this paper, we review different approaches that are proposed for web service clustering to enhance the discovery process. A comparison among existing approaches is carried out based on the vector representation approach, dimensionality reduction technique, method to capture semantic relationship among features, clustering technique, type of input dataset, number of web services in dataset and service repository. This review will help the researchers to understand the existing techniques to group services in similar clusters to improvise service discovery and the scope for improvement by future directions.KeywordsService-oriented architectureWeb services/Web APIWeb service clusteringWeb service discovery

A workflow system is often composed of a number of subtasks in its pattern. In services computing environment, a dynamic cross-organizational workflow can be implemented by assigning services to its subtasks. It is often enabled by a service discovery process on Internet. Traditional service discovery approaches are centralized, and suffer from many problems such as one-point failure and weak scalability. Thus, decentralized P2P technique is a promising approach for service publishing and discovery. It is quite probable that there are more than one candidates which have exactly the same function after a service discovery process. It is often a challenging effort to select a qualified service from a group of candidates, especially on P2P networks. In view of this challenge, a QoS-aware service selection approach on unstructured P2P networks is presented in this paper. It aims at discovering and selecting services on two-layered unstructured P2P networks according to QoS parameters of services and preference of service requesters. This approach is applied to a case study based on a simplified P2P network with some virtual services.

With the rapid growth of wireless communication technologies, the demands for large-scale wireless networks have been emerged. One promising solution is the hierarchical architecture that consists of global and local wireless networks. The service discovery is a key issue to meet a vast amount of service demands from mobile nodes. However, under dynamic wireless networks, node failures severely affect the performance of service discovery protocols. Therefore, in this paper, we propose the directory hopping (DH) protocol for efficient service discoveries in dynamic large-scale networks. Basically, DH utilizes the directory for low-overhead service discoveries and periodically moves the directory to other operating nodes. In addition, it provides a fast and efficient recovery mechanism for node failures in dynamic network environments. Through extensive simulations, we show that the proposed DH protocol outperforms existing service discovery schemes.

Service discovery and selection approaches are often done using a centralized registry-based technique, which only captures common Quality of Service criteria. With more and more services offered via social networks, these approaches are not able to evaluate trust in service providers and often fail to comply with new requester's expectations. This is because theses approaches are not able (i) to take into consideration the social dimension and (ii) to capitalize on information resulting from previous experiences. To address these challenges, we propose the use of multi-agent systems as they have demonstrated the capability to use previous interactions, knowledge representation and distributed reasoning, as well as social metaphors like trust. More precisely, in this paper, we enhance service discovery and selection processes by integrating the societal view in trust modeling. Based on relationships between agents, their previous experiences and extracted information from social network, we define a trust model built upon social, expert and recommender-based components. The social-based component judges whether the provider is worthwhile pursuing before using his services (viz. trust in sociability). The expert-based component estimates whether the service behaves well and as expected (viz. trust in expertise). The recommender-based component assesses for an agent whether one's can rely on its recommendations (viz. trust in recommendation). However, when searching for a service in a social network, agents (service requester and service providers) may have no direct interactions or previous experiences. This requires a method to infer trust between them. Based on a probabilistic model, we estimate trust between non adjacent agents while taking into account roles (recommender or provider) of intermediate agents. Moreover, we propose a distributed algorithm for trustworthy service discovery and selection using referral systems in social networks. Experiments demonstrate that our approach is effective and outperforms existing ones, and can deliver more trustworthy results.

The increasingly growing number of services available on the web, along with advances in information and communication technologies, has given rise to the development of new approaches for service discovery, devoted to retrieve services advertised by a service provider and satisfying users requests. Traditional service discovery attempts are devoted to enable a matching between advertised service names and the keywords of the user request. Some of these methods use also semantic approaches, however an improvement in terms of precision has been obtained with the adoption of a semantic-based approach, together with the use of contextual information. This research aims at facing the problem of discovering context-aware services by proposing a semantic similarity model for enabling the matching between advertisements and requests personalized according to the context information (such as user position, user preferences, available resources). The method proposed has been validated by performing a set of experiments for evaluating the recall and precision of the method.

Service-oriented computing is gaining wider acceptance. For Web services to become practical, an infrastructure needs to be supported that allows users and applications to discover, deploy, compose and synthesize services automatically. For this automation to be effective, formal semantic descriptions of Web services should be available. In this paper we formally define the Web service discovery and composition problem and present an approach for automatic service discovery and composition based on semantic description of Web services. We also report on an implementation of a semantics-based automated service discovery and composition engine that we have developed. This engine employs a multi-step narrowing algorithm and is efficiently implemented using the constraint logic programming technology. The salient features of our engine are its scalability, i.e., its ability to handle very large service repositories, and its extremely efficient processing times for discovery and composition queries. We evaluate our engine for automated discovery and composition on repositories of different sizes and present the results.

Dynamic service discovery is one of the main concepts which define the Service Oriented Architectures (SOA). This mechanism ensures loosely coupled services. It supports the reusability of services and the flexibility of applications. This paper provides a new service discovery approach which enhances the number of services potentially discovered and thus, it multiplies the number of candidate services. This multiplication ensures a better selection of the most suitable services and more alternatives is case of failures.

In this paper, we have proposed a generalized large-scale optical cross-connect (OXC) architecture utilizing waveband selective switches (WBSS) for realizing future cost-effective, bandwidth-abundant and flexible optical networks. The developed architecture implements multiple WBSSs for each incoming fiber and small size wavelength selective switches (WSSs) for dropping optical paths while simply deploying 1´2 WSSs or 1´2 optical couplers for realizing the adding function. Thanks to the use of WBSSs, which are more cost-effective and simpler devices, the developed architecture enables a significant hardware scale reduction. The WBSS-based OXC, however, suffers from a limited routing capability, which relies on the inner node parameter (i.e., the WBSS number per input fiber) and the waveband granularity of WBSSs. We, therefore, evaluate the hardware scale requirement of our developed architecture in comparison with that of conventional WSS-based OXC. It is verified that a substantial hardware scale reduction can be achieved by using the proposed architecture, especially for high port count OXCs or when applying coarser granular WBSSs. Moreover, we also assess the performance of dynamic optical networks based on the proposed OXC. Numerical simulations show that the network offers a substantial necessary hardware scale reduction at the cost of a small performance offset comparing to that of the network using conventional WSS-based OXC.

In cloud computing context services present the dynamic and evolving features, which greatly affects the accuracy and efficiency of service discovery. It is the prerequisite for supporting service discovery to capture and represent these features during the process of services organization and management. This paper proposes a field-based model to describe the dynamic and evolving features of cloud services in both qualitative and quantitative way, which is inspired by the Bohr atom model. The concept of energy level in Bohr model is used to represent the services status and demarcate cloud services, electrons jumping mechanism in Bohr model is used to depict services' dynamic and evolving features and analyze how services status are changed from one energy level to another. The field model of services provides abstractions to classify a set of services according to their status and mechanism to explain their changes and demarcation according to their potential energy variation. The concept of user acceptable services region is proposed to represent the search scope and method of user service discovery request in cloud services field model. The algorithms to generate field model of services and form user acceptable services region are designed, with which field-based service discovery algorithm is proposed. Based on QWS dataset, we conduct a series of experiments to evaluate and validate the effectiveness of field-based service model for organizing and discovering cloud services.

In the vision of ubiquitous computing, there are billions of context sources that continuously publish their contextual information, and even more user agents that search for and consume such information. These context producers and consumers form a large-scale, wide-area, context-aware network which is both dynamic and heterogeneous. Existing service discovery mechanisms do not address the unique challenges imposed by such environments. In this paper we identify these challenges and propose a large scale context service discovery infrastructure for context-aware computing. We describe an approach for context service discovery and description that addresses these challenges.

Past communication and computing trends are inadequate to deal with today's complex, distributed, and diverse network environments. With the recent development of small-size tetherless communication/computing devices and the increasing diversity in their capabilities, autonomic ubiquitous communication environments are emerging. In such environments, the execution of a complex task does not necessarily make use of preconfigured devices or networks, but requires instead the selection of suitable computing elements on-the-fly, based on the task requirements and device characteristics (i.e., networking-on-demand or task-centric networking). Research is currently being carried out in various dimensions in this emerging domain, including, among other approaches for context awareness, service discovery and self-management. In this article we concentrate on proposing a distributed mechanism for discovering suitable service elements on-the-fly.

With the proliferation of available Web services, new approaches are needed to search for the most appropriate services matching user requirements. Existing UDDI based discovery techniques fail to recognize similarities and differences between Web service capabilities and thus greatly limit the scope of service discovery. Hence, in this paper we present a framework for discovering the vast amount of Web services present in Internet based on a novel semantic approach to automatically discover the most appropriate services to user requirements. Our framework is based on a natural language query to facilitate usage of our implemented discovery framework. For this purpose, we first apply some computational linguistics techniques such as part-of-speech tagging and word sense disambiguation to format and extract useful semantic information from user query. Then, we propose an innovative semantic matchmaking technique based on significant concepts properties in the referenced domain ontology and we show that it improves discovery efficiency compared to a similar related work.