Energy-Efficient Resource Allocation Strategy Based on Task Classification in Data Center
Hongjian Li, Shiwang Ding, Pengfei Zhang, Jun Lai
Available Online May 2018.
- https://doi.org/10.2991/amcce-18.2018.20How to use a DOI?
- data center; workload characterization; task classification; energy efficiency; resource allocation
- In view of the fact that the research on energy efficiency in data centers has not fully considered the heterogeneity of workload tasks, a data center resource allocation scheme based on task classification is proposed. Through cluster analysis, tasks are classified into subsets of similar resources and performance requirements. The same types of tasks to configure a reasonable type of virtual machine to improve the compatibility between workload requirements and configuration resources. While ensuring the QoS requirements of different types of tasks, resources preemption of tasks of the same resource requirement type is avoided, enabling energy-efficient resource allocation with low-power and QoS guarantees in the data center. Simulation experimental results find that, compared with the traditional resource allocation algorithm, this scheme effectively improves the data center energy efficiency.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Hongjian Li AU - Shiwang Ding AU - Pengfei Zhang AU - Jun Lai PY - 2018/05 DA - 2018/05 TI - Energy-Efficient Resource Allocation Strategy Based on Task Classification in Data Center BT - 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018) PB - Atlantis Press SN - 2352-5401 UR - https://doi.org/10.2991/amcce-18.2018.20 DO - https://doi.org/10.2991/amcce-18.2018.20 ID - Li2018/05 ER -