基于机器学习的服务组合技术研究进展

Research Progress of Service Composition Based on Machine Learning

服务组合是服务计算领域内的经典研究问题,在工业及学术领域内受到广泛关注。随着云原生和微服务技术不断普及,服务组合领域涌现出一系列富有创新性的研究。随着计算机技术和人工智能的快速发展,深度学习、强化学习等机器学习算法被越来越多地应用于传统服务组合问题中。文中介绍了服务组合问题常见的分类及面临的挑战,并对近年来涌现的机器学习算法在服务组合问题中的应用进行了归纳介绍。此外,文中还总结了基于机器学习算法解决服务组合问题时所面临的问题,并对今后的发展方向进行了展望。

Service composition is a classic research problem in the field of service computing,which has received extensive attention in both industrial and academic fields.With the increasing popularity of cloud-native and micro-service technologies,a series of innovative researches have emerged in the field of service composition.With the rapid development of computer technology and artificial intelligence,machine learning algorithms such as deep learning and reinforcement learning are increasingly applied to traditional service composition problems.This study introduces the common classification and challenges of service composition problems,and summarizes the application of machine learning algorithms emerging in service composition problems in recent years.Additionally,the proposed study also summarizes the problems faced in the direction of solving service composition problems based on machine learning algorithms,and looks forward to the future development direction.