Learning-Based Resource Allocation in Cloud Data Center Using Advantage Actor-Critic
Chen, Z; Hu, J; Min, G
Date: 15 July 2019
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Publisher DOI
Abstract
Due to the ever-changing system states and various
user demands, resource allocation in cloud data center is faced
with great challenges in dynamics and complexity. Although
there are solutions that focus on this problem, they cannot
effectively respond to the dynamic changes of system states and
user demands since they depend on ...
Due to the ever-changing system states and various
user demands, resource allocation in cloud data center is faced
with great challenges in dynamics and complexity. Although
there are solutions that focus on this problem, they cannot
effectively respond to the dynamic changes of system states and
user demands since they depend on the prior knowledge of
the system. Therefore, it is still an open challenge to realize
automatic and adaptive resource allocation in order to satisfy
diverse system requirements in cloud data center. To cope
with this challenge, we propose an advantage actor-critic based
reinforcement learning (RL) framework for resource allocation
in cloud data center. First, the actor parameterizes the policy
(allocating resources) and chooses continuous actions (scheduling
jobs) based on the scores (evaluating actions) from the critic.
Next, the policy is updated by gradient ascent and the variance
of policy gradient can be significantly reduced with the advantage
function. Simulations using Google cluster-usage traces show the
effectiveness of the proposed method in cloud resource allocation.
Moreover, the proposed method outperforms classic resource
allocation algorithms in terms of job latency and achieves faster convergence speed than the traditional policy gradient method.
Computer Science
Faculty of Environment, Science and Economy
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