update README

README.md

@@ -5,4 +5,86 @@ task_categories:
 pretty_name: cloud
 size_categories:
 - 100M<n<1B
----
+---
+
+# Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain
+[Paper](https://arxiv.org/abs/2310.05063) | [Code](https://github.com/SalesforceAIResearch/pretrain-time-series-cloudops)
+
+Datasets accompanying the paper "Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain".
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset('Salesforce/cloudops_tsf', 'azure_vm_traces_2017')
+```
+
+### azure_vm_traces_2017
+```python
+DatasetDict({
+    train_test: Dataset({
+        features: ['start', 'target', 'item_id', 'feat_static_cat', 'feat_static_real', 'past_feat_dynamic_real'],
+        num_rows: 17568
+    })
+    pretrain: Dataset({
+        features: ['start', 'target', 'item_id', 'feat_static_cat', 'feat_static_real', 'past_feat_dynamic_real'],
+        num_rows: 159472
+    })
+})
+```
+
+### borg_cluster_data_2011
+```python
+DatasetDict({
+    train_test: Dataset({
+        features: ['start', 'target', 'item_id', 'feat_static_cat', 'past_feat_dynamic_real'],
+        num_rows: 11117
+    })
+    pretrain: Dataset({
+        features: ['start', 'target', 'item_id', 'feat_static_cat', 'past_feat_dynamic_real'],
+        num_rows: 143386
+    })
+})
+```
+
+### alibaba_cluster_trace_2018
+```python
+DatasetDict({
+    train_test: Dataset({
+        features: ['start', 'target', 'item_id', 'feat_static_cat', 'past_feat_dynamic_real'],
+        num_rows: 6048
+    })
+    pretrain: Dataset({
+        features: ['start', 'target', 'item_id', 'feat_static_cat', 'past_feat_dynamic_real'],
+        num_rows: 58409
+    })
+})
+```
+
+## Acknowledgements
+The datasets were processed from the following original sources. Please cite the original sources if you use the datasets.
+* Azure VM Traces 2017
+  * Bianchini. Resource central: Understanding and predicting workloads for improved resource
+  management in large cloud platforms. In Proceedings of the 26th Symposium on Operating Systems
+  Principles, pp. 153–167, 2017.
+  * https://github.com/Azure/AzurePublicDataset
+
+* Borg Cluster Data 2011
+  * John Wilkes. More Google cluster data. Google research blog, November 2011. Posted at http:
+  //googleresearch.blogspot.com/2011/11/more-google-cluster-data.html.
+  * https://github.com/google/cluster-data
+
+* Alibaba Cluster Trace 2018
+  * Jing Guo, Zihao Chang, Sa Wang, Haiyang Ding, Yihui Feng, Liang Mao, and Yungang Bao. Who
+  limits the resource efficiency of my datacenter: An analysis of alibaba datacenter traces. In
+  Proceedings of the International Symposium on Quality of Service, pp. 1–10, 2019.
+  * https://github.com/alibaba/clusterdata
+
+## Citation
+```
+@article{woo2023pushing,
+  title={Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain},
+  author={Woo, Gerald and Liu, Chenghao and Kumar, Akshat and Sahoo, Doyen},
+  journal={arXiv preprint arXiv:2310.05063},
+  year={2023}
+}
+```