Ranking Composite Services

A framework for modeling Semantic Web Service is proposed. It is based on Description Logic (DL), hence it is endowed with a formal semantics and, in addition, it allows for expressing constraints in service descriptions of different strengths, i.e. Hard and Soft Constraints. Semantic service discovery can be performed by matching DL descriptions, expressing both Hard and Soft constraints, and exploiting DL inferences. Additionally, a method for solving the problem of ranking services is proposed which is based on the use of a semantic similarity measure for DL. This method can rank (matched) service descriptions on the grounds of their semantic similarity w.r.t. the service request, by preferring those that are able to better satisfy both Hard and Soft Constraints.

The advances in mobile technologies enable us to consume or even provide services through powerful mobile devices anytime and anywhere. Services running on mobile devices within limited range can be composed to coordinate together through wireless communication technologies and perform complex tasks. However, the mobility of users and devices in mobile environment imposes high risk on the execution of the tasks. This paper targets reducing this risk by constructing a dependable service composition after considering the mobility of both service requesters and providers. It first proposes a risk model and clarifies the risk of mobile service composition; and then proposes a service composition approach by modifying the simulated annealing algorithm. Our objective is to form a service composition by selecting mobile services under the mobility model and to ensure the service composition have the best quality of service and the lowest risk. The experimental results demonstrate that our approach can yield near-optimal solutions and has a nearly linear complexity with respect to a problem size.

Energy-aware service composition in multi-Cloud

Web Services are materialized as a major technology carried out for automated interaction between distributed and miscellaneous applications. It is defined as a software service that provides business solutions consumed by different service requester. It can be accessed by a standard web protocol. Service composition is the mechanism used for selecting, reusing and combining existing web services to build new web services. With immense increase in web services, quality assessment plays an essential role in the selection approach. QoS is defined as the ability to guarantee the requirements like latency, reliability, bandwidth, etc. in order to satisfy a service level agreement between an application provider and end-user. QoS based dynamic service composition leads to the upward growth of an organization that implements multiple services to provide its business solution. The proposed research work elaborates the research work on different methodologies of QoS prediction with its implications to provide a proper road map for future research on efficient service composition.

We propose a novel dynamic metaheuristic optimization approach to compose an optimal set of IaaS service requests to align with an IaaS provider’s long-term economic expectation. This approach is designed for the context that the IaaS provisioning subjects to resource and QoS constraints. In addition, the IaaS service requests have the features of dynamic resource and QoS requirements and variable arrival times. A new economic model is proposed to evaluate the similarity between the provider’s long-term economic expectation and a composition of service requests. The evaluation incorporates the factors of dynamic pricing and operation cost modeling of the service requests. An innovative hybrid genetic algorithm is proposed that incorporates the economic inter-dependency among the requests as a heuristic operator and performs repair operations in local solutions to meet the resource and QoS constraints. The proposed approach generates dynamic global solutions by updating the heuristic operator at regular intervals with the runtime behavior data of an existing service composition. Experimental results preliminarily prove the feasibility of the proposed approach.

Background/Objectives: The primary objective of the study is to evaluate the performance of Composite Web Service based on the parameters relating to Quality of Service by using statistical methodologies. Methods/Statistical Analysis: By using Web Service Crawler Engine we have collected data on eleven parameters of interest from a more number of Web Services. Among the eleven parameters we have selected six parameters of importance using the sampling of thirty Web Services. We have constructed the weight matrix for the parameters. By using Web Services Relevancy Function ranking has been done for three different scenarios. Using Spearman's Rank Correlation Coefficient we have compared the scenarios. Findings: The graphical representation of the three scenarios shows varying pattern. The Spearman's Rank Correlation coefficient of the Web Services Relevancy Function varied widely among the sampled Web Services. F-Test revealed that there are no significant differences in respect of weights of Quality of Web Services for three different scenarios. We concluded that weight assigned for Quality of Service parameters in Scenario I is preferred than the scenarios II and III through the evaluations. Application/Improvements: The technique developed in this research paper can be applied for the comparison of Web Services during discovery to enhance the performance of the Composite Web Service.

Towards runtime discovery, selection and composition of semantic services

Current approaches for service composition (assemblies of atomic services) require developers to use: (a) domain-specific semantics to formalize services that restrict the vocabulary for their descriptions, and (b) translation mechanisms for service retrieval to convert unstructured user requests to strongly-typed semantic representations. In our work, we argue that effort to developing service descriptions, request translations, and matching mechanisms could be reduced using unrestricted natural language; allowing both: (1) end-users to intuitively express their needs using natural language, and (2) service developers to develop services without relying on syntactic/semantic description languages. Although there are some natural language-based service composition approaches, they restrict service retrieval to syntactic/semantic matching. With recent developments in Machine learning and Natural Language Processing, we motivate the use of Sentence Embeddings by leveraging richer semantic representations of sentences for service description, matching and retrieval. Experimental results show that service composition development effort may be reduced by more than 44% while keeping a high precision/recall when matching high-level user requests with low-level service method invocations.

Dynamic service composition has emerged as a promising approach to build complex runtime-adaptable applications as it allows for binding service providers only shortly before service execution. However, the dynamic and ad hoc nature of mobile computing environments poses a significant challenge for dynamic service composition. In particular, the lack of central control and the potential volatility of service providers increase the complexity and failure probability of the composition process. Although, current research has led to decentralised composition algorithms and failure recovery strategies, the key question of how to reduce the failure probability of a composition still remains. We address this question and propose opportunistic service composition, an optimised execution model for complex service requests. The model merges the execution phase into the dynamic binding phase and supports the immediate fulfilment of partially composed service requests. We evaluated our model in mobile ad hoc network simulations. The results show an improvement over a baseline approach regarding composition success, response time, and communication effort.

In software markets of the future, customer-specific software will be developed on demand based on distributed software and hardware services. Based on a customer-specific request, available service offers have to be discovered and composed into sophisticated IT services that fulfill the customer's request. A prerequisite of this vision are rich service descriptions, which comprise structural as well as behavioral aspects of the services, otherwise an accurate service discovery and composition is not possible. However, automatic matching of service requests and offers specified in rich service descriptions for the purpose of service discovery is a complex task, due to the multifaceted heterogeneity of the service partners. This heterogeneity includes the use of different specification languages, different underlying ontologies, or different levels of granularity in the specification itself. In this article, we present a comprehensive approach for service discovery and composition, which overcomes the underlying heterogeneity of the service partners. Based on a realistic case study of our industrial partner from the e-tourism domain, we first introduce an automatic matching mechanism for service requests and offers specified in a rich service description language. In addition, we propose an automatic service composition approach, which determines possible service compositions by composing the service protocols through a composition strategy based on labeled transition systems.

Developments in information technology and networking are rapidly driving the enhancement of networked appliances in the home environment. Users can request services from networked appliances via the home network framework and makes it possible to provide new concomitant capability by composing a virtual appliance. However, due to the vast number of home networked appliances and their complicated services, such composition can prove challenging for the non-technical home user. In this paper, we present the components of our framework for providing automatic service composition in networked appliances that uses both centralized and decentralized mechanisms the hardware implementation details for the framework, and demonstrate our hardware initial implementation.

A SOUND salary program has two equally important objectives. The first is to provide adequate compensation for the services of employees; the second is to provide a method of effective salary control. A socially conscious and morally responsible administration will keep these two objectives constantly in view, and will make every effort to avail itself of the best methods of salary classification at its command. The material contained herein should facilitate intelligent discussion leading to the development of more adequate salary schedules than now exist in many of the land-grant colleges. The following principles are basic to any sound salary classification program: First, various classes or grades of instruction representing, in a rough way, the general value of that instruction to the university should be established and differentiated one from the other in terms of salary, length of service, security of tenure, and administrative recognition of relative professional competence. Second, for each grade or rank of instructional service there should be established a minimum salary below which no one holding that rank should be paid, representing the minimum economic value of this service to society, and a maximum salary above which no one holding that rank should be paid, representing the maximum economic value of this service to society. Third, within and between the minimum and maximum limits of each instructional class, compensation to the individual should vary according to ability either demonstrated or rated, or both. The application of these principles will aid materially in establishing a salary system which will provide not only adequate incentive for faculty members, but will greatly facilitate administrative salary control. In order to put these principles to work, however, it is necessary to perform two separate and distinct, yet interrelated, operations. The first of these is the pricing of the separate ranks of instruction: setting minimum, maximum, and intermediate values; and determining the relationships of the prices for each rank to each other. The second operation is the development of some type of appraisal system whereby the ability of each individual may be evaluated. The first operation, it is obvious, has to do with the general economic value

Cloud manufacturing is a new service-oriented manufacturing model that integrates distributed manufacturing resources to provide on-demand manufacturing services over the Internet. Service composition that builds larger-granularity, value-added services by combining a number of smaller-granularity services to satisfy consumers’ complex requirements is an important issue in cloud manufacturing. Meta-heuristic algorithms such as genetic algorithm, particle swarm optimization, and ant colony algorithm are frequently employed for addressing service composition issues in cloud manufacturing. However, these algorithms require complex design flows and lack adaptability to dynamic environment. Deep reinforcement learning provides an alternative approach for solving cloud manufacturing service composition issues. This chapter proposes a deep Q-network (DQN) based approach for service composition in cloud manufacturing, which is able to find optimal service composition solutions through repeated training and learning. Results of experiments that take into account changes of service scales and service unavailability reveal the scalability and robustness of the DQN algorithm-based service composition approach.

To manage the delivery of services competitively on a large, global scale, an IT (information technology) service provider must efficiently use service delivery resources-in particular, skilled service delivery teams. Service requests form a large and important component of the management of a client's IT infrastructure. Currently, the fulfillment of IT service requests is often managed on a per-account basis. Service-delivery teams fulfill service requests according to account-specific processes by using an account-specific service-request-management environment, making it difficult to leverage the skills of the various delivery teams for multiple accounts. The service delivery management platform (SDMP) uses reusable service components that can be performed by multiple delivery teams and can be assembled into service compositions to which multiple clients can subscribe. The SDMP catalog is the information repository that manages service components, composition, providers, and subscriptions and is used by the service-request runtime environment to implement specific customer service requests. In this paper, we describe a catalog-based architecture for service delivery management and demonstrate how its use can provide a global service-delivery organization with a platform for achieving significant productivity gains.

Adult economic model and values survey: Cross-national differences in economic beliefs