Add new papers: CloudSimPy

Author: HaiyangDING

README.md

@@ -33,12 +33,17 @@ Last but not least, we are always open to work together with researchers to impr
 
 The fundemental idea of our releasing cluster data is to enable researchers & practitioners doing resaerch, simulation with more realistic data and thus making the result closer to industry adoption. It is a huge encouragement to us to see more works using our data. Here is a list of existing works using Alibaba cluster data. If your paper uses our trace, it would be great if you let us know by sending us email ([aliababa-clusterdata](mailto:alibaba-clusterdata@list.alibaba-inc.com)).
 
-* Who Limits the Resource Efficiency of My Datacenter: An Analysis of Alibaba Datacenter Traces, Jing Guo, Zihao Chang, Sa Wang, Haiyang Ding, Yihui Feng, Liang Mao, Yungang Bao, IEEE/ACM International Symposium on Quality of Service, IWQoS 2019
-* LegoOS: A Disseminated, Distributed OS for Hardware Resource Disaggregation, Yizhou Shan, Yutong Huang, Yilun Chen, and Yiying Zhang, Purdue University. OSDI'18 (Best paper award!)
-* The Elasticity and Plasticity in Semi-Containerized Co-locating Cloud Workload: a View from Alibaba Trace, Qixiao Liu and Zhibin Yu. SoCC2018
-* Zeno: A Straggler Diagnosis System for Distributed Computing Using Machine Learning, Huanxing Shen and Cong Li, Proceedings of the Thirty-Third International Conference, ISC High Performance 2018
-* Characterizing Co-located Datacenter Workloads: An Alibaba Case Study, Yue Cheng, Zheng Chai, Ali Anwar. APSys2018
-* [Imbalance in the Cloud: an Analysis on Alibaba Cluster Trace, Chengzhi Lu et al. BIGDATA 2017](http://cloud.siat.ac.cn/~ye/Imbalance_Ye_2017.pdf)
+* cluster trace v2018
+  * Who Limits the Resource Efficiency of My Datacenter: An Analysis of Alibaba Datacenter Traces, Jing Guo, Zihao Chang, Sa Wang, Haiyang Ding, Yihui Feng, Liang Mao, Yungang Bao, IEEE/ACM International Symposium on Quality of Service, IWQoS 2019
+  * [DeepJS: Job Scheduling Based on Deep Reinforcement Learning in Cloud Data Center](https://github.com/RobertLexis/CloudSimPy/blob/master/playground/paper/F0049-4.19.pdf), by Fengcun Li and Bo Hu.
+    * There is a interesting simulator released with this paper: CloudSimPy. You can check it at [CloudSimPy](https://github.com/RobertLexis/CloudSimPy)
+
+* cluster trace v2017
+  * LegoOS: A Disseminated, Distributed OS for Hardware Resource Disaggregation, Yizhou Shan, Yutong Huang, Yilun Chen, and Yiying Zhang, Purdue University. OSDI'18 (Best paper award!)
+  * The Elasticity and Plasticity in Semi-Containerized Co-locating Cloud Workload: a View from Alibaba Trace, Qixiao Liu and Zhibin Yu. SoCC2018
+  * Zeno: A Straggler Diagnosis System for Distributed Computing Using Machine Learning, Huanxing Shen and Cong Li, Proceedings of the Thirty-Third International Conference, ISC High Performance 2018
+  * Characterizing Co-located Datacenter Workloads: An Alibaba Case Study, Yue Cheng, Zheng Chai, Ali Anwar. APSys2018
+  * [Imbalance in the Cloud: an Analysis on Alibaba Cluster Trace, Chengzhi Lu et al. BIGDATA 2017](http://cloud.siat.ac.cn/~ye/Imbalance_Ye_2017.pdf)
 
 ### Tech reports and projects on analysing the trace