A DRL-Based Container Placement Scheme with Auxiliary Tasks |
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Authors: | Ningcheng Yuan Chao Jia Jizhao Lu Shaoyong Guo Wencui Li Xuesong Qiu Lei Shi |
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Affiliation: | 1.Beijing University of Posts and Telecommunications, Beijing, 100876, China.
2 China Electronics Standardization Institute, Beijing, China.
3 Communication Operation Center, State Grid Henan Electric Power Company Information & Telecommunication Company, Zhengzhou, China.
4 Institute of Technology Carlow, Carlow, Ireland. |
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Abstract: | Container is an emerging virtualization technology and widely adopted in the
cloud to provide services because of its lightweight, flexible, isolated and highly portable
properties. Cloud services are often instantiated as clusters of interconnected containers.
Due to the stochastic service arrival and complicated cloud environment, it is challenging
to achieve an optimal container placement (CP) scheme. We propose to leverage Deep
Reinforcement Learning (DRL) for solving CP problem, which is able to learn from
experience interacting with the environment and does not rely on mathematical model or
prior knowledge. However, applying DRL method directly dose not lead to a satisfying
result because of sophisticated environment states and huge action spaces. In this paper,
we propose UNREAL-CP, a DRL-based method to place container instances on servers
while considering end to end delay and resource utilization cost. The proposed method is
an actor-critic-based approach, which has advantages in dealing with the huge action
space. Moreover, the idea of auxiliary learning is also included in our architecture. We
design two auxiliary learning tasks about load balancing to improve algorithm
performance. Compared to other DRL methods, extensive simulation results show that
UNREAL-CP performs better up to 28.6% in terms of reducing delay and deployment
cost with high training efficiency and responding speed. |
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Keywords: | Container placement deep reinforcement learning auxiliary learning |
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