A Hybrid TLBO-TS Algorithm Based Mobile Service Selection for Composite Services

• Published in: Algorithms and Architectures for Parallel Processing Vol. 13155; pp. 237 - 256
• Main Authors: Xie, Runbin, Liu, Jianxun, Kang, Guosheng, Cao, Buqing, Wen, Yiping, Xiang, Jiayan
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2022; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Hybrid algorithm; Mobile network; Mobile service selection; Quality of Service; Service composition
• ISBN: 3030953831; 9783030953836
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-95384-3_16

Service selection for composite service has been a hot research issue in service computing field. With the proliferation of mobile devices, service selection confronts new challenges in the mobile environment due to the mobility, unpredictability, and variation of signal strength of mobile networks, since quality of service (QoS) is closely related to these factors. In this work, we aim to address the problem of mobile service selection for composite service in terms of QoS. Specifically, based on the mobility model and mobility-aware QoS computation rule, we propose a hybrid service composition optimization algorithm, named TLBO-TS, by integrating Teaching-Learning-Based Optimization (TLBO) algorithm and Tabu Search (TS) algorithm. Through the optimization of service selection with TLBO-TS algorithm, the global QoS of the generated mobile service composition is approximately optimal. Extensive experiments are conducted and the experimental results show that the proposed approach can derive more optimized mobile service composition with acceptable scalability compared with the traditional approach and other baselines.