Given the problems of resource skew, tail latency amplification and frequent migration under bursty traffic in heterogeneous nodes and cross-node network latency of Docker container cloud platforms, a predictive multi-metric load balancing strategy has been designed. This strategy jointly uses CPU, memory, network, queues and energy consumption as the state, applies exponential smoothing for short-term request intensity prediction, and conducts container placement and request distribution via feasible domain filtering, dynamic weight scoring, hysteresis migration, and abnormal node deweighting. Based on public research papers and experimental data from the last three years, this study also has some secondary analysis. According to the above results, at fewer than 16 concurrent requests, a 30ms inter-node latency can reduce throughput by 58.0% under a uniform Pod distribution; localisation and resource-aware scheduling can mitigate this drop significantly. The proposed method takes into account the constraints of load dispersion, P95 latency and migration cost simultaneously to offer an interpretable and deployable load balancing framework for Docker Swarm and other compatible orchestration platforms.

Wenhao Song, Research on Load Balancing Strategy for Cloud Platforms Based on Docker Containers. International Journal of Multimedia Computing (2026), Vol. 7, Issue 2: 28-37. https://doi.org/10.38007/IJMC.2026.070204

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