An Alarm Prediction Framework Based on the Propagation Dependency and Reinforcement Learning Toward Cloud Services

In cloud platforms, the occurrence of failures in cloud services is usually accompanied by a substantial number of alarms, posing challenges to the operation, management, and maintenance. To address this issue, this paper proposes an alarm prediction framework based on propagation dependency and reinforcement learning for cloud services. First, a three-layer cloud service alarm knowledge graph is constructed, encompassing the device layer, service layer, and alarm layer, with the objective of integrating alarm data. Then, to filter out irrelevant alarms, an alarm compression schema is designed, taking into account both structural and semantic information. It achieves the preservation of propagation information by mining frequent alarm subtrees. After that, a reinforcement learning-based alarm prediction model is devised, including an alarm-driven reinforcement learning network and a global alarm discriminator. The former formulates the alarm propagation problem as a reinforcement learning inferencing task, and designs an immediate reward function to simulate the local propagation patterns of alarms. The latter guides the agent to learn alarm evolution patterns from a global perspective through an adversarial approach. Experimental results on two datasets demonstrate the effectiveness of our alarm compression and prediction models.