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VMAgent: Scheduling Simulator for Reinforcement Learning

2021-12-09 · Junjie Sheng, Shengliang Cai, Haochuan Cui, Wenhao Li, Yun Hua, Bo Jin, Wenli Zhou, Yiqiu Hu, Lei Zhu, Qian Peng, Hongyuan Zha, Xiangfeng Wang

A novel simulator called VMAgent is introduced to help RL researchers better explore new methods, especially for virtual machine scheduling. VMAgent is inspired by practical virtual machine (VM) scheduling tasks and provides an efficient simulation platform that can reflect the real situations of cloud computing. Three scenarios (fading, recovering, and expansion) are concluded from practical cloud computing and corresponds to many reinforcement learning challenges (high dimensional state and action spaces, high non-stationarity, and life-long demand). VMAgent provides flexible configurations for RL researchers to design their customized scheduling environments considering different problem features. From the VM scheduling perspective, VMAgent also helps to explore better learning-based scheduling solutions.

📄 PDF Abstract BibTeX arXiv:2112.04785

Code (2)

mail-ecnu/vmagent 공식 구현 pytorch

huaweicloud/vm-placement-dataset

Tasks

Cloud Computingreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Scheduling

Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing

2021-10-18 · Pinyarash Pinyoanuntapong, Tagore Pothuneedi, Ravikumar Balakrishnan, Minwoo Lee 외

Federated Learning (FL) over wireless multi-hop edge computing networks, i.e., multi-hop FL, is a cost-effective distributed on-device deep learning paradigm. This paper presents FedEdge simulator, a high-fidelity Linux-…

Edge-computing Federated Learning Reinforcement Learning (RL)Scheduling+1

Railway Operation Rescheduling System via Dynamic Simulation and Reinforcement Learning

2022-01-17 · Shumpei Kubosawa, Takashi Onishi, Makoto Sakahara, Yoshimasa Tsuruoka

The number of railway service disruptions has been increasing owing to intensification of natural disasters. In addition, abrupt changes in social situations such as the COVID-19 pandemic require railway companies to mod…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Scheduling

Reinforcement Learning for Assignment problem

2020-11-08 · Filipp Skomorokhov, George Ovchinnikov

This paper is dedicated to the application of reinforcement learning combined with neural networks to the general formulation of user scheduling problem. Our simulator resembles real world problems by means of stochastic…

Q-Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

QoS-Aware Scheduling in New Radio Using Deep Reinforcement Learning

2021-07-14 · Jakob Stigenberg, Vidit Saxena, Soma Tayamon, Euhanna Ghadimi

Fifth-generation (5G) New Radio (NR) cellular networks support a wide range of new services, many of which require an application-specific quality of service (QoS), e.g. in terms of a guaranteed minimum bit-rate or a max…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

The Pump Scheduling Problem: A Real-World Scenario for Reinforcement Learning

2022-10-20 · Henrique Donâncio, Laurent Vercouter, Harald Roclawski

Deep Reinforcement Learning (DRL) has demonstrated impressive results in domains such as games and robotics, where task formulations are well-defined. However, few DRL benchmarks are grounded in complex, real-world envir…

Deep Reinforcement Learning Offline RL reinforcement-learning Reinforcement Learning (RL)+2