TY - GEN
T1 - A Resource-Constrained Multi-level SLA Customization Approach Based on QoE Analysis of Large-Scale Customers
AU - Li, Min
AU - Xu, Hanchuan
AU - Xu, Xiaofei
AU - Wang, Zhongjie
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - The user-centered service paradigm has attracted extensive attention from academia and industry. It advocates taking customer requirements as the orientation, maximizing customer satisfaction as the objective, then targeting to carry out market segmentation and multi-level SLA customization. There are two main challenges: personalized preferences of large-scale customers and resource constraints. In this paper, we propose a resource-constrained multi-level SLA customization approach based on QoE analysis of large-scale customers. With a deep generative network, we fit satisfaction mapping functions and infer the customer’s personalized preference interval for each QoS. Then, based on the theory of granular computing, a multi-level, multi-perspective and multi-scale granular structure for service customization is constructed. Finally, the best match between users with personalized preferences and resources with differentiated qualities is mined to obtain a reduced and balanced multi-level SLA customization scheme. This paper conducts experiments based on the real data of a hotel booking platform and proves that the method performs well in service customization granularity, preference coverage and matching accuracy. The method is an on-demand optimization and avoids over-optimization. The final customized solutions can not only meet the personalized preferences but also give play to the advantages of different quality resources.
AB - The user-centered service paradigm has attracted extensive attention from academia and industry. It advocates taking customer requirements as the orientation, maximizing customer satisfaction as the objective, then targeting to carry out market segmentation and multi-level SLA customization. There are two main challenges: personalized preferences of large-scale customers and resource constraints. In this paper, we propose a resource-constrained multi-level SLA customization approach based on QoE analysis of large-scale customers. With a deep generative network, we fit satisfaction mapping functions and infer the customer’s personalized preference interval for each QoS. Then, based on the theory of granular computing, a multi-level, multi-perspective and multi-scale granular structure for service customization is constructed. Finally, the best match between users with personalized preferences and resources with differentiated qualities is mined to obtain a reduced and balanced multi-level SLA customization scheme. This paper conducts experiments based on the real data of a hotel booking platform and proves that the method performs well in service customization granularity, preference coverage and matching accuracy. The method is an on-demand optimization and avoids over-optimization. The final customized solutions can not only meet the personalized preferences but also give play to the advantages of different quality resources.
KW - Granular computing
KW - Multi-level SLA customization
KW - Personalized preferences
KW - Satisfaction mapping functions
UR - https://www.scopus.com/pages/publications/85163997307
U2 - 10.1007/978-3-031-34560-9_35
DO - 10.1007/978-3-031-34560-9_35
M3 - 会议稿件
AN - SCOPUS:85163997307
SN - 9783031345593
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 594
EP - 610
BT - Advanced Information Systems Engineering - 35th International Conference, CAiSE 2023, Proceedings
A2 - Indulska, Marta
A2 - Reinhartz-Berger, Iris
A2 - Cetina, Carlos
A2 - Pastor, Oscar
PB - Springer Science and Business Media Deutschland GmbH
T2 - 35th International Conference on Advanced Information Systems Engineering, CAiSE 2023
Y2 - 12 June 2023 through 16 June 2023
ER -