QoS-based Service Composition Research Articles

Location-aware deep learning-based framework for optimizing cloud consumer quality of service-based service composition

<span>The expanding propensity of organization users to utilize cloud services urges to deliver services in a service pool with a variety of functional and non-functional attributes from online service providers. brokers of cloud services must intense rivalry competing with one another to provide quality of service (QoS) enhancements. Such rivalry prompts a troublesome and muddled providing composite services on the cloud using a simple service selection and composition approach. Therefore, cloud composition is considered a non-deterministic polynomial (NP-hard) and economically motivated problem. Hence, developing a reliable economic model for composition is of tremendous interest and to have importance for the cloud consumer. This paper provides “A location-aware deep learning framework for improving the QoS-based service composition for cloud consumers”. The proposed framework is firstly reducing the dimensions of data. Secondly, it applies a combination of the deep learning long short-term memory network and particle swarm optimization algorithm additionally to considering the location parameter to correctly forecast the QoS provisioned values. Finally, it composes the ideal services need to reduce the customer cost function. The suggested framework's performance has been demonstrated using a real dataset, proving that it superior the current models in terms of prediction and composition accuracy.</span>

Retraction Note to: Meta heuristic QoS based service composition for service computing

Retraction Note to: Meta heuristic QoS based service composition for service computing

A Trade-Off Negotiation Strategy for Pareto-Optimal Service Composition with Additive QoS-constraints

QoS-based service composition enables the development of complex business applications. They are composed of distributed services characterized by QoS attributes representing non-functional characteristics, such as cost, execution time, and reliability. With the proliferation of services on the Internet, more candidates for each component service may be available with different QoS values. Hence, the ones satisfying QoS global constraints required for the application have to be selected. Automated negotiation is adopted to select component services in a dynamic market of services where provided QoS values may vary according to different market strategies. Nevertheless, when dealing with multiple QoS attributes, multiple service providers, and not shared information, it is difficult to guarantee the formal properties of the negotiation outcomes. In the present work, we propose a trade-off negotiation strategy that allows reaching a Pareto-optimal agreement, if it exists. The agreement consists of the QoS values of component services that are the ones selected to provide the complete application. The strategy exploits both the competition, that is due to multiple services providing the same functionality with different QoS values, and the cooperation among the providers of the different component services, that are necessary to meet the required end-to-end QoS constraints.

RETRACTED ARTICLE: Meta heuristic QoS based service composition for service computing

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.

A deep learning based framework for optimizing cloud consumer QoS-based service composition

The service composition problem in Cloud computing is formulated as a multiple criteria decision-making problem. Due to the extensive search space, Cloud service composition is addressed as an NP-hard problem. In addition, it is a long term based and economically driven. Building an accurate economic model for service composition has great attention to interest and importance for the Cloud consumer. A deep learning based service composition (DLSC) framework has been proposed in this paper. The proposed DLSC framework is considered an amalgamation between the deep learning long short term memory (LSTM) network and particle swarm optimization (PSO) algorithm. The LSTM network is applied to accurately predict the Cloud QoS provisioned values, and the output of LSTM network is fed to PSO algorithm to compose the best Cloud providers to contract with them for composing the needed services to minimize the consumer cost function. The proposed DLSC framework has been implemented using a real QoS dataset. According to the comparative results, it is found that the performance of the proposed framework outperforms the existing models with respect to the predictive accuracy and composition accuracy.

A Trust Evaluation Model on QoS based Services Composition for IoT Environments

A Trust Evaluation Model on QoS based Services Composition for IoT Environments

Edge cloud computing service composition based on modified bird swarm optimization in the internet of things

The rapid development of cloud platforms provides a large amount of cloud service resources, which have similar functions and different values of QoS attribute. QoS-based service composition in the edge cloud computing environment faces the main problem that how to combine different cloud services to make global QoS value of service composition to reach the maximization, which is under the premise of meeting the local QoS requirements of edge users. In this paper, the modified bird swarm optimization algorithm is put forward, which introduces the two-order oscillating equation and the historical position memory of the birds on the basis of basic birds swarm optimization. It improves the dynamic parameter mechanism of bird feeding and migration, and enriches the diversity of the birds when moving, and improves the global search ability of the algorithm. By analyzing the results of service combination simulation without local QoS restriction and local QoS restriction, the algorithm can minimize the overall execution time cost of the request under the QoS restriction.

Selecting Dynamic Skyline Services for QoS-based Service Composition

With the growing adoption of web services on the Internet, service selection becomes an important issue of service-oriented computing (SOC). Appropriate services selection algorithm is the fundamental guarantee to compose complex services from single- function components effectively. The quality of selected component services is crucial for the performance of the service composition. Therefore, it has become a hot issue to select the best services from a set of services with similar functionality. Recently, skyline has been introduced to solve the problem by selecting skyline services as the best candidate services. In this paper, we focus on selecting skyline services in dynamic environment, where new services may appear, original services may invalidate and QoS of services may change. We propose a skyline service model as well as a novel skyline algorithm to maintain dynamic skyline services. An extensive performance study is propoesed to verify the effectiveness and effi ciency of our approach.

A chaos control optimal algorithm for QoS-based service composition selection in cloud manufacturing system

A chaos control optimal algorithm for QoS-based service composition selection in cloud manufacturing system

Top-<formula formulatype="inline"><tex Notation="TeX">${\rm k}$</tex></formula> Automatic Service Composition: A Parallel Method for Large-Scale Service Sets

Quality-of-Service (QoS)-aware web service composition is of great importance to assemble individual services into a composite one meeting functional and nonfunctional requirements. Given a large number of candidate services, automatic composition is essential so as to derive a composite service efficiently. Most existing methods return one solution that is optimal in some given criteria. This is somewhat rigid in terms of flexibility. In case some component service in the optimal composition becomes unavailable, the composition algorithm has to run again to find another optimal solution. Also, in a lot of circumstances users prefer multiple alternatives over a single one. Therefore, providing top- k service compositions according to their QoS is becoming more desirable. On another aspect, from the perspective of computation efficiency, due to the explosion of the searching space, single-threaded methods are usually not capable of handling a large number of candidate services. This paper tackles these two issues together, i.e., large-scale, QoS-based services composition yielding top- k solutions. The composition algorithm is based on the combination of backtrack search and depth-first search, which can be executed in a parallel way. Experiments are carried out based on the datasets provided by the WS-Challenge competition 2009 and China Web Service 2011. The results show that our approach can not only find the same optimal solution as the winning systems from these competitions, but also provide alternative solutions together with the optimal QoS.

A chaos control optimal algorithm for QoS-based service composition selection in cloud manufacturing system

This article investigates the problem of cloud service composition optimal-selection (CSCOS) in cloud manufacturing (CMfg). The categories of cloud services and their QoS (quality of service) indexes are established. From the perspective of QoS indexes, the relationship among QoS key factors for different kinds of cloud services are analysed and elaborated, and the corresponding objective functions and constraints of CSCOS are proposed. A new chaos control optimal algorithm (CCOA) is designed to address the CSCOS problem, and the simulation results demonstrate that the proposed algorithm can search better solutions with less time-consumption than widely used algorithms such as genetic algorithm (GA) and typical chaotic genetic algorithm (CGA).

Correlation-aware web services composition and QoS computation model in virtual enterprise

When establishing a virtual enterprise (VE), how some enterprises with different resources and advantages form the alliances by providing respective services to compose the VE for certain profits, it is actually a process of services composition. There always exist a lot of web services with similar functions but different non-functionality properties in the process of web services composition. In order to enhance the quality of service composition, non-functionality properties, i.e., quality of service (QoS) are usually considered. However, most existing works about QoS-based service composition treat the services involved in composition as independent from each other, and their correlations are usually ignored. In fact, most services have some correlations with each other, and the correlations can affect the whole quality of service composition badly. In this paper, three kinds of correlations in services composition are investigated, and a correlation-aware QoS model is studied. The impact of each kind of correlation on the whole QoS of services composition is investigated. A case study indicates that higher quality of services composition can be achieved when considering the correlations among composite services.