Publication: vrAIn: a deep learning approach tailoring computing and radio resources in virtualized RANs
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2019-09-05
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Association for Computing Machinery
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Abstract
The virtualization of radio access networks (vRAN) is the
last milestone in the NFV revolution. However, the complex
dependencies between computing and radio resources make
vRAN resource control particularly daunting. We present
vrAIn, a dynamic resource controller for vRANs based on
deep reinforcement learning. First, we use an autoencoder
to project high-dimensional context data (traffic and signal
quality patterns) into a latent representation. Then, we use a
deep deterministic policy gradient (DDPG) algorithm based
on an actor-critic neural network structure and a classifier
to map (encoded) contexts into resource control decisions.
We have implemented vrAIn using an open-source LTE
stack over different platforms. Our results show that vrAIn
successfully derives appropriate compute and radio control
actions irrespective of the platform and context: (i) it provides
savings in computational capacity of up to 30% over
CPU-unaware methods; (ii) it improves the probability of
meeting QoS targets by 25% over static allocation policies
using similar CPU resources in average; (iii) upon CPU capacity
shortage, it improves throughput performance by 25%
over state-of-the-art schemes; and (iv) it performs close to optimal
policies resulting from an offline oracle. To the best of
our knowledge, this is the first work that thoroughly studies
the computational behavior of vRANs, and the first approach
to a model-free solution that does not need to assume any
particular vRAN platform or system conditions.
Description
Proceeding of: 25th Annual International Conference on Mobile Computing and Networking (MobiCom'19), October 21-25, 2019, Los Cabos, Mexico.
Keywords
RAN virtualization, Resource management, Machine learning, Network algorithms, Mobile networks
Bibliographic citation
Proceedings of the 25th Annual International Conference on Mobile Computing and Networking (MobiCom'19). New York: ACM, cop. 2019. Article nº 30, [16] pp.