Container Scheduling with Dynamic Computing Resource for Microservice Deployment in Edge Computing

With the massive increase of Internet of Things devices and their data, executing applications by using microservice architecture has emerged as the predominant trend.

As container technology evolves, microservices can be lightweightly deployed in resource-constrained edge nodes. However, existing container scheduling algorithms often overlook the allocation of computing resources on edge servers. When multiple containers are assigned to an edge node, it is usually assumed that they share a uniform CPU frequency, which is unrealistic.

In this paper, we first formulate an online container-based microservice scheduling problem with dynamic computing power to minimize total delay and energy consumption, where we need to determine:

1. The assignment between microservices and edge nodes.
2. The allocation of computing power to each microservice.

We propose a Soft Actor-Critic (SAC) based reinforcement learning algorithm to address this problem, integrating:

• A GRU unit in the policy network to capture decision correlation.
• An action selection mechanism to accelerate convergence.

Finally, we implemented a simulated scheduling system, demonstrating that our algorithm outperforms commonly used baselines by up to 65% in terms of the total objective.

Recommended citation: J. Lu, W. Li, J. Guo, X. Ding, Z. Tang, and T. Wang, 'Container Scheduling with Dynamic Computing Resource for Microservice Deployment in Edge Computing,' in MSN 2024.
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