@inproceedings{f04bc0e91e61492787a386af7f9600bc,
title = "Profit-aware spatial task scheduling in distributed green clouds",
abstract = "More and more large-scale enterprises choose distributed green clouds (DGCs) to cost-effectively deploy their applications. The significant increase of users' tasks makes it highly challenging to achieve profit maximization for a DGC provider under the fact that prices of power grid, revenues, and the amount of wind and solar energy in DGCs all change with different sites. This work develops a Profit-Aware Spatial Task Scheduling (PASTS) method for the profit maximization of a DGC provider. PASTS well investigates such spatial differences of these mentioned factors, and it smartly schedules tasks to meet their response time constraints. A nonlinear constrained program is designed and tackled by a hybrid meta-heuristic algorithm that combines particle swarm optimization with genetic mechanism and simulated annealing. Realistic data-based results prove that PASTS provides higher profit and throughput than two recent typical algorithms.",
keywords = "Data centers, Distributed clouds, Green computing, Meta-heuristic optimization, Task scheduling",
author = "Haitao Yuan and Jing Bi",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 ; Conference date: 06-10-2019 Through 09-10-2019",
year = "2019",
month = oct,
doi = "10.1109/SMC.2019.8914022",
language = "英语",
series = "Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "421--426",
booktitle = "2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019",
address = "美国",
}