| Title | A Trust-Based Agent Learning Model for Service Composition in Mobile Cloud Computing Environments |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Authors | Li, Wenjuan, Cao, Jian, Hu, Keyong, Xu, Jie, Buyya, Rajkumar |
| Journal | IEEE Access |
| Volume | 7 |
| Pagination | 34207–34226 |
| ISSN | 2169-3536 |
| Keywords | cloud computing, composability, compositionality, Computational modeling, Computing Theory and Trust, customers service preferences, fuzzy comprehensive evaluation method, fuzzy set theory, learning (artificial intelligence), mobile cloud computing, mobile cloud computing environments, mobile computing, multi-agent systems, multi-agent technology, multiagent platform JADE, preference learning, Processor scheduling, pubcrawl, QoS provision, quality of service, quality of service provision, scheduling, serious security risks, service brokers, service composition, service request, Task Analysis, three-layered trust-enabled service composition model, transaction success rate, Trust management, trust-based agent learning model, Trusted Computing, user experience, user experiences, user preference learning, user satisfaction |
| Abstract | Mobile cloud computing has the features of resource constraints, openness, and uncertainty which leads to the high uncertainty on its quality of service (QoS) provision and serious security risks. Therefore, when faced with complex service requirements, an efficient and reliable service composition approach is extremely important. In addition, preference learning is also a key factor to improve user experiences. In order to address them, this paper introduces a three-layered trust-enabled service composition model for the mobile cloud computing systems. Based on the fuzzy comprehensive evaluation method, we design a novel and integrated trust management model. Service brokers are equipped with a learning module enabling them to better analyze customers' service preferences, especially in cases when the details of a service request are not totally disclosed. Because traditional methods cannot totally reflect the autonomous collaboration between the mobile cloud entities, a prototype system based on the multi-agent platform JADE is implemented to evaluate the efficiency of the proposed strategies. The experimental results show that our approach improves the transaction success rate and user satisfaction. |
| DOI | 10.1109/ACCESS.2019.2904081 |
| Citation Key | li_trust-based_2019 |
A Trust-Based Agent Learning Model for Service Composition in Mobile Cloud Computing Environments