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SMig-RL
- Source :
- ACM Transactions on Internet Technology. 20:1-18
- Publication Year :
- 2020
- Publisher :
- Association for Computing Machinery (ACM), 2020.
-
Abstract
- Service migration is an often-used approach in cloud computing to minimize the access cost by moving the service close to most users. Although it is effective in a certain sense, the service migration in existing research still suffers from some deficiencies in its evolutionary abilities in scalability , sensitivity , and adaptability to effectively react to the dynamically changing environments. This article proposes an evolutionary framework based on deep reinforcement learning for virtual service migration in large-scale mobile cloud centers. To enhance the spatio-temporal sensitivity of the algorithm, we design a scalable reward function for virtual service migration, redefine the input state, and add a Recurrent Neural Network ( RNN ) to the learning framework. Additionally, in order to enhance the adaptability of the algorithm, we also decompose the action space and exploit the network cost to adjust the number of virtual machine (VMs). The experimental results show that, compared with the existing results, the migration strategy generated by the algorithm can not only significantly reduce the total service cost and achieve the load balancing at the same time, but also address the burst situations with low cost in dynamic environments.
- Subjects :
- Service (systems architecture)
Computer Networks and Communications
business.industry
Computer science
Distributed computing
Q-learning
020206 networking & telecommunications
Cloud computing
02 engineering and technology
Load balancing (computing)
computer.software_genre
Recurrent neural network
Virtual machine
Scalability
0202 electrical engineering, electronic engineering, information engineering
Reinforcement learning
020201 artificial intelligence & image processing
business
computer
Subjects
Details
- ISSN :
- 15576051 and 15335399
- Volume :
- 20
- Database :
- OpenAIRE
- Journal :
- ACM Transactions on Internet Technology
- Accession number :
- edsair.doi...........87902a8a3db64dc190ca2f58bd25f477