Deep reinforcement learning-based methods for resource scheduling in cloud computing: a review and future directions
University of Electronic Science and Technology of China · The University of Melbourne · +2 more institutions
Abstract
Abstract With the acceleration of the Internet in Web 2.0, Cloud computing is a new paradigm to offer dynamic, reliable and elastic computing services. Efficient scheduling of resources or optimal allocation of requests is one of the prominent issues in emerging Cloud computing. Considering the growing complexity of Cloud computing, future Cloud systems will require more effective resource management methods. In some complex scenarios with difficulties in directly evaluating the performance of scheduling solutions, classic algorithms (such as heuristics and meta-heuristics) will fail to obtain an effective scheme. Deep reinforcement learning (DRL) is a novel method to solve scheduling problems. Due to the…
Citation impact
- FWCI
- 51.65
- Percentile
- 100%
- References
- 159
Authors
5- GZGuangyao Zhou
University of Electronic Science and Technology of China
- WTWenhong Tian
University of Electronic Science and Technology of China
- RBRajkumar Buyya
The University of Melbourne
- RXRuini Xue
University of Electronic Science and Technology of China
- LSLiang SongCorresponding
Tsinghua Sichuan Energy Internet Research Institute, Tsinghua University
Topics & keywords
- Computer science
- Cloud computing
- Reinforcement learning
- Distributed computing
- Scheduling (production processes)
- Heuristics
- Dynamic priority scheduling
- Two-level scheduling