Meta Learning for Improved Policy Transfer in Changing Network Environments

Dynamic resource allocation for microservices is crucial for meeting management objectives like high throughput and low latency. Recent advancements highlight reinforcement learning as a promising technique, with policy adaptation offering potential for coping with dynamic environments. However, traditional policy adaptation methods often suffer from slow adaptation, particularly in rapidly changing scenarios, limiting their effectiveness in achieving timely responses. To address this limitation, we explore a meta-learning technique to train more generalizable and robust policies that adapt more quickly to target environments. Our findings from extensive experimentation in a real testbed reveal that the MAML meta-learning technique outperforms in scenarios with changing management objectives and achieves comparable performance to baselines in handling dynamic infrastructure loads.