Web Service Classification Based on Reinforcement Learning and Structured Representation Learning

Sheng, Hankang; Li, Zhangbing; Liu, Jianxun; Zhang, Xiao

doi:10.1109/aibt53261.2021.00011

2021 International Conference on Artificial Intelligence and Blockchain Technology (AIBT) 2021

DOI: 10.1109/aibt53261.2021.00011

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Web Service Classification Based on Reinforcement Learning and Structured Representation Learning

Hankang Sheng¹,

Zhangbing Li²,

Jianxun Liu

et al.

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Cited by 2 publications

References 17 publications

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Boosting the visibility of services in microservice architecture

Tokmak,

Akbulut,

Catal

2023

Cluster Comput

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Monolithic software architectures are no longer sufficient for the highly complex software-intensive systems, which modern society depends on. Service Oriented Architecture (SOA) surpassed monolithic architecture due to its reusability, platform independency, ease of maintenance, and scalability. Recent SOA implementations made use of cloud-native architectural approaches such as microservice architecture, which has resulted in a new challenge: the discovery difficulties of services. One way to dynamically discover and route traffic to service instances is to use a service discovery tool to locate the Internet Protocol (IP) address and port number of a microservice. In the event that replicated microservice instances are found to provide the same function, it is crucial to select the right microservice that provides the best overall experience for the end-user. Parameters including success rate, efficiency, delay time, and response time play a vital role in establishing a microservice’s Quality of Service (QoS). These assessments can be performed by means of a live health-check service, or, alternatively, by making a prediction of the current state of affairs with the application of machine learning-based approaches. In this research, we evaluate the performance of several classification algorithms for estimating the quality of microservices using the QWS dataset containing traffic data of 2505 microservices. Our research also analyzed the boosting algorithms, namely Gradient Boost, XGBoost, LightGBM, and CatBoost to improve the overall performance. We utilized parameter optimization techniques, namely Grid Search, Random Search, Bayes Search, Halvin Grid Search, and Halvin Random Search to fine-tune the hyperparameters of our classifier models. Experimental results demonstrated that the CatBoost algorithm achieved the highest level of accuracy (90.42%) in predicting microservice quality.

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Boosting the visibility of services in microservice architecture

Tokmak,

Akbulut,

Catal

2023

Cluster Comput

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A Semi-Supervised Learning Approach to Quality-Based Web Service Classification

Bonab,

Tanha,

Masdari

2024

IEEE Access

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The Internet provides a platform for sharing services, and web service brokers help users to choose the suitable service among similar services based on ranking. The quality of service is important in evaluating the services the user needs. However, finding a quality-based data label in many fields can be time-consuming and difficult. Thus, machine learning is required to classify and choose the best service in this field. The selection process is done through analysis and recommendations by the system. This article introduces the SSL-WSC algorithm, which classifies unlabeled data through semi-supervised self-training learning using a small amount of labeled data. This algorithm labels the data using a two-step method of calculating a score for each service and dynamic thresholding. The quality features of web services obtained from the QWS dataset were used to evaluate the performance of the proposed algorithm. The experimental results in different scenarios showed that using proposed semi-supervised learning algorithms to create classification models led to better results, so it improved the F1-score, accuracy, and precision, on average, by 11.26%, 9.43% and 9.53%, respectively, as compared to the supervised method.

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Web Service Classification Based on Reinforcement Learning and Structured Representation Learning

Cited by 2 publications

References 17 publications

Boosting the visibility of services in microservice architecture

Boosting the visibility of services in microservice architecture

A Semi-Supervised Learning Approach to Quality-Based Web Service Classification

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