Developing a Robust and Flexible Web Service Framework for Medical Care

The rapid growth of Internet technologies and availability of web tools created an opportunity to develop a robust and user-friendly web service model for medical care, and it demands urgent solutions as the uncertainty of disease spread threaten humanity. With changing Quality of Service principles, many existing web services need to offer specific medical services that suit practical needs. The provision of an effective service selection and recommendation features that best meet the user's requirements will be able to improve the quality of web service model. The Quality of Service metrics should be calculated and analyzed before optimizing a recommendation technique. Evaluation therefore forms an important part of the process for designing and implementing recommendation systems. Further, predicting Quality of Service indicators accurately from historical background dataset under complex scenarios and combination of conditions is useful. In this perspective, lots of web service methods are studied, and this paper presents our comprehensive analysis mainly focusing on the development of better web service based framework for medical applications.

Providing Predictable Quality of Service in a Cloud-Based Web System

Cloud-computing web systems and services revolutionized the web. Nowadays, they are the most important part of the Internet. Cloud-computing systems provide the opportunity for businesses to undergo digital transformation in order to improve efficiency and reduce costs. The sudden shutdown of schools and offices during the pandemic of Covid 19 significantly increased the demand for cloud solutions. Load balancing and sharing mechanisms are implemented in order to reduce the costs and increase the quality of web service. The usage of those methods with adaptive intelligent algorithms can deliver the highest and a predictable quality of service. In this article, a new HTTP request-distribution method in a two-layer architecture of a cluster-based web system is presented. This method allows for the provision of efficient processing and predictable quality by servicing requests in adopted time constraints. The proposed decision algorithms utilize fuzzy-neural models allowing service times to be estimated. This article provides a description of this new solution. It also contains the results of experiments in which the proposed method is compared with other intelligent approaches such as Fuzzy-Neural Request Distribution, and distribution methods often used in production systems.

Weighted Bayesian Runtime Monitor: A Novel QoS Monitoring Approach Sensitive to Environmental Factors

How to assure Quality of Service (QoS) of the third-party services is very important for the SOA. Effective monitoring technique towards QoS, which is an important measurement for third-party service quality, is necessary to ensure quality of Web service. Current monitoring approaches do not consider the influences of environment factors such as the position of server, user usage, and the load at runtime. Ignoring these influences, which do exist among the monitoring process, may cause existing monitoring approaches producing unpredictable monitoring results. In order to overcome this limitation, this paper proposes a novel Web Service QoS (WS-Qos) monitoring approach sensitive to environmental factors called weighted Bayesian Runtime Monitor (wBSRM) based on weighted naïve Bayesian classifiers and Term Frequency-Inverse Document Frequency (TF-IDF) algorithm. wBSRM constructs weighted naïve Bayesian classifier by learning a part of samples to classify the monitoring results. The results meeting QoS standard are classified as [Formula: see text] and the one that does not meet is classified as [Formula: see text]. Classifier can also output ratio between posterior probability of [Formula: see text] and [Formula: see text], and consequently the analysis can lead to three monitoring results including [Formula: see text], [Formula: see text] or inconclusive. A set of dedicated experiments are conducted to validate wBSRM. The experiments are based on a public dataset and a simulated dataset under the given standard. The experimental results demonstrate that wBSRM is better than previous approaches.

Research on GRU-based credibility prediction of Web services

The service system is based on the SOA architecture, and its component services are usually deployed by third-party service providers in an open network environment. This openness also brings confusion to service system while extending functions. Unavailability of a single service may result in the unavailability of the entire service system. This paper uses Web service credibility as a standard to measure whether Web service is available. Web service credibility is calculated by 12 factors that affect quality of Web service. According to time series of Web service credibility in the past, credibility at next time period can be predicted. This paper proposes a Gated Recurrent Unit (GRU) algorithm which uses grid search algorithm and adaptive moment estimation (Adam) to solve above problem. In this algorithm, grid search algorithm is used to get the best hyper-parameters of network and Adam is used to correct the gradient in the gradient descent. Finally, based on a large number of real Web services, the GRU prediction algorithm is verified by experiments. Experimental results show that the GRU algorithm has higher prediction accuracy than other methods in Web service credibility prediction.

Dynamically Predicting the Quality of Service: Batch, Online, and Hybrid Algorithms

This paper studies the problem of dynamically modeling the quality of web service. The philosophy of designing practical web service recommender systems is delivered in this paper. A general system architecture for such systems continuously collects the user-service invocation records and includes both an online training module and an offline training module for quality prediction. In addition, we introduce matrix factorization-based online and offline training algorithms based on the gradient descent algorithms and demonstrate the fitness of this online/offline algorithm framework to the proposed architecture. The superiority of the proposed model is confirmed by empirical studies on a real-life quality of web service data set and comparisons with existing web service recommendation algorithms.

Selection of Logistic Web Services Based on Fuzzy Evaluation on Principal Component of Quality of Service

The current results on logistic Web services selection are not optimal due to some key quality indexes of logistic Web services excluded, in order to resolve the above problem, an evaluation system on quality of service is established by use of principal component analysis based on quality of logistic service, quality of Web service, and satisfaction of customers. The values of quality of service with subjective uncertainty in the evaluation system are given with trapezoidal fuzzy number according to the definition of logistic business and evaluation from domain experts and customers, besides, the weight on each quality of service is given by pairwise comparison, and an algorithm based on analytic hierarchy process for logistic Web service selection is established. The optimal service is got by adopting the algorithm in the logistic scenario on automotive transportation, which proves that the way on service selection in this paper is feasible and effective.