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https://hdl.handle.net/10356/144760
Title: | User experience-enhanced and energy-efficient task scheduling on heterogeneous multi-core mobile systems | Authors: | Huang, Yanting Liu, Weichen Li, Mengquan Chen, Peng Yang, Lei Xiao, Chunhua Ye, Yaoyao |
Keywords: | Engineering::Computer science and engineering | Issue Date: | 2019 | Source: | Huang, Y., Liu, W., Li, M., Chen, P., Yang, L., Xiao, C., & Ye, Y., (2018). User experience-enhanced and energy-efficient task scheduling on heterogeneous multi-core mobile systems. Proceedings of 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS), 283-290. doi:10.1109/PADSW.2018.8645024 | Project: | NAP M4082282 SUG M4082087 |
Conference: | 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS) | Abstract: | Heterogeneous Multi-Core Mobile Systems has been widely used to improve performance. However, it faces with the challenge of tradeoff between energy saving and user experience. ARM big. LITTLE architecture, a heterogeneous computing architecture, is a power-optimization technology. In most big. LITTLE devices, however, it still cannot achieve excellent user experience and higher energy saving. In this paper, we propose an improved task scheduling (UCES-GTS) by introducing the concept of user-centric task on big. LITTLE mobile device. In order to enhance user experience, the response time of user-centric tasks is shortened with reducing slack time of them properly. We then present a detailed algorithm to compute appropriate frequency and allocate the CPU resources to each task. The experimental evaluation results show that our improved global task scheduling model can achieve 17 % and 8 % energy saving average compared with the clustered switching scheduling and the original global task scheduling respectively. And the response time of user-centric tasks can decrease 27 % average, which means excellent user experience. | URI: | https://hdl.handle.net/10356/144760 | ISBN: | 978-1-5386-7308-9 | DOI: | 10.1109/PADSW.2018.8645024 | Schools: | School of Computer Science and Engineering | Rights: | © 2018 IEEE. All rights reserved. | Fulltext Permission: | none | Fulltext Availability: | No Fulltext |
Appears in Collections: | SCSE Conference Papers |
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