-
Notifications
You must be signed in to change notification settings - Fork 1
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
3f7cc9f
commit ee1ed3f
Showing
2 changed files
with
1 addition
and
2 deletions.
There are no files selected for viewing
Binary file not shown.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
|
@@ -45,10 +45,9 @@ His Email: [email protected]; liulei2010@{buaa.edu.cn; ict.ac.cn} | |
|
||
1. **Intelligent Operating Systems** | ||
|
||
(1) | ||
(1) We want to have a new design method for OS, which is based on using AI and ML technologies. The latest thinking and efforts are in **<span style="color:#953734;">ACM TOS-2024</span>**. | ||
|
||
(2) Designing new scheduling mechanisms on large-scale servers to improve performance, QoS, resource utilization, and save energy. Our scheduling mechanism works well in the modern Cloud environment, which embraces many Latency-critical applications, best-effort applications, and microservices. The latest study and a series of our efforts can be accessed via this [**Link**](https://arxiv.org/abs/1911.13208). | ||
- We want to have a new design method for OS, which is based on using AI and ML technologies. The latest thinking and efforts are in **<span style="color:#953734;">ACM TOS-2024</span>**. | ||
- The latest study is accepted by **<span style="color:#953734;">FAST-2023</span>**. | ||
- The work on using ML to enhance thread scheduling on platforms with a hybird DRAM-NVM memory system is in CCF THPC-2022. [**Link**](https://rdcu.be/cXmO6) | ||
- On 26/Nov 2019, the technique report about OSML is available via this [**Link**](/files/OSML___Lei_Liu.pdf). OSML uses multiple collaborative ML models to schedule multiple interactive resources for co-located latency-critical services. | ||
|