A framework for dynamically meeting performance objectives on a service mesh
We present a framework for achieving end-to-end management objectives for multiple
services that concurrently execute on a service mesh. We apply reinforcement learning (RL) …
services that concurrently execute on a service mesh. We apply reinforcement learning (RL) …
云网边端协同云控制研究进展及挑战
夏元清， 王晁， 高润泽， 詹玉峰， 孙中奇， 戴荔， 翟弟华 - 信息与控制, 2024 - xk.sia.cn
针对网络化控制系统通信与计算资源不足, 云控制系统难以完全保证复杂任务实时控制的问题,
在云控制系统研究基础上, 云网边端协同云控制利用云控制平台层, 网络传输层, 边缘控制层 …
在云控制系统研究基础上, 云网边端协同云控制利用云控制平台层, 网络传输层, 边缘控制层 …
Interless: Interference-Aware Deep Resource Prediction for Serverless Computing
Serverless is an emerging cloud computing paradigm that allows functions to share
resources. However, function resource sharing introduces interference, which results in …
resources. However, function resource sharing introduces interference, which results in …
Octopus: An End-to-end Multi-DAG Scheduling Method Based on Deep Reinforcement Learning
With the rapid growth of cloud computing, more and more vendors are deploying their
services to the cloud. Efficient job scheduling is essential for enhancing system operation …
services to the cloud. Efficient job scheduling is essential for enhancing system operation …