计算机科学
服务质量
强化学习
Web服务
服务(商务)
移动QoS
面向服务的体系结构
差异化服务
分布式计算
服务交付框架
计算机网络
服务设计
人工智能
万维网
经济
经济
作者
Hongbing Wang,Jiajie Li,Qi Yu,Tianjing Hong,Yan Jia,Wei Zhao
标识
DOI:10.1016/j.future.2020.02.030
摘要
In the service oriented architecture (SOA), software and systems are abstracted as web services to be invoked by other systems. Service composition is a technology, which builds a complex system by combining existing simple services. With the development of SOA and web service technology, massive web services with the same function begin to spring up. These services are maintained by different organizations and have different QoS (Quality of Service). Thus, how to choose the appropriate service to make the whole system to deliver the best overall QoS has become a key problem in service composition research. Furthermore, because of the complexity and dynamics of the network environment, QoS may change over time. Therefore, how to adjust the composition system dynamically to adapt to the changing environment and ensure the quality of the composed service also poses challenges. To address the above challenges, we propose a service composition approach based on QoS prediction and reinforcement learning. Specifically, we use a recurrent neural network to predict the QoS, and then make dynamic service selection through reinforcement learning. This approach can be well adapted to a dynamic network environment. We carry out a series of experiments to verify the effectiveness of our approach.
科研通智能强力驱动
Strongly Powered by AbleSci AI