Name
Abstract | ||
|---|---|---|
Achieving data center resource optimization and QoS guarantee driven by high energy efficiency has become a research hotspot. However, QoS information directly sampled from the cloud environment will inevitably be affected by a small amount of structured noise. This paper proposes a deep reinforcement learning model based on QoS feature learning to optimize data center resource scheduling. In the deep learning stage, we propose a QoS feature learning method based on improved stacked denoising autoencoders to extract more robust QoS characteristic information. In the reinforcement learning stage, we propose a multi-power machines (PMs) collaborative resource scheduling algorithm based on reinforcement learning. Extensive experiments show that compared with other excellent resource scheduling strategies, our method can effectively reduce the energy consumption of cloud data centers while maintaining the lowest service level agreement (SLA) violation rate. A good balance is achieved between energy-saving and QoS optimization. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.future.2021.07.023 | Future Generation Computer Systems |
Keywords | DocType | Volume |
Energy efficiency,QoS guarantee,Denoising autoencoder,q-learning,Feature learning | Journal | 125 |
ISSN | Citations | PageRank |
0167-739X | 2 | 0.36 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Wang | 1 | 27 | 7.94 |
| Fagui Liu | 2 | 23 | 6.06 |
| Weiwei Lin | 3 | 147 | 13.95 |