@inproceedings{59370d4413f34beb9b03be815c989a41,
title = "Leveraging User Heterogeneities to Maximize Profits in the Cloud",
abstract = "The main goal of a cloud service provider is to make profits by providing services to users. Existing pricing strategies adopt a single revenue function for different types of service requests, which ignores the heterogeneity of users and leads to low profits. In this paper, we propose a heterogeneity-aware request scheduling scheme that maximizes profits of service providers by exploiting the heterogeneity of users. Specifically, we first model charge functions of requests at the granularity of individual users to capture their heterogeneity. Then an integer linear programming (ILP)-based optimal scheduling algorithm is designed to maximize profits, which is followed by an approximate but lightweight genetic algorithm (GA)-based profit improvement scheme. The GA-based scheme is particularly tailored for applications of high scheduling resolution. Extensive simulation results show that our schemes improve profits by at least 22.46\% compared to benchmarking methods while achieving at least 13.51 times of speedup.",
keywords = "Cloud computing, profit maximization, user heterogeneity, user requests scheduling",
author = "Guo Xu and Tongquan Wei and Junlong Zhou and Mingsong Chen",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 24th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2018 ; Conference date: 11-12-2018 Through 13-12-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/PADSW.2018.8644983",
language = "英语",
series = "Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS",
publisher = "IEEE Computer Society",
pages = "170--177",
booktitle = "Proceedings - 2018 IEEE 24th International Conference on Parallel and Distributed Systems, ICPADS 2018",
address = "美国",
}