Efficient compute-intensive job allocation in data centers via deep reinforcement learning
Reducing the energy consumption of the servers in a data center via proper job allocation is desirable. Existing advanced job allocation algorithms, based on constrained optimization formulations capturing servers' complex power consumption and thermal dynamics, often scale poorly with the data...
Main Authors: | Yi, Deliang, Zhou, Xin, Wen, Yonggang, Tan, Rui |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Journal Article |
Language: | English |
Published: |
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/161048 |
Similar Items
-
Toward efficient compute-intensive job allocation for green data centers : a deep reinforcement learning approach
by: Yi, Deliang
Published: (2019) -
Bandwidth-Guaranteed Resource Allocation and Scheduling for Parallel Jobs in Cloud Data Center
by: Zhen Li, et al.
Published: (2018-04-01) -
Characteristics of Co-Allocated Online Services and Batch Jobs in Internet Data Centers: A Case Study From Alibaba Cloud
by: Congfeng Jiang, et al.
Published: (2019-01-01) -
A Two-Stage Framework for the Multi-User Multi-Data Center Job Scheduling and Resource Allocation
by: Jianpeng Lin, et al.
Published: (2020-01-01) -
Joint IT-facility optimization for green data centers via deep reinforcement learning
by: Zhou, Xin, et al.
Published: (2022)