Resilience-Driven Task-Cluster Co-Management: Proactive Mitigation of Co-Resident Threats in AI Clusters

With the prosperity of AI-generated content (AIGC), multi-tenant training in AI task clusters has become prevalent. To improve resource utilization, multiple tenants will coexist on the same server, while malicious tenants may exploit side-channel to pose significant co-resident eavesdropping risks. Due to the extensive attack surface, distributed training tasks are particularly vulnerable to model parameters and data leakage when subjected to the same level of security protection as inference tasks. Moreover, traditional security mechanisms, reliant on static encryption or isolation, suffer from high overhead, passive defense and poor scalability, failing to address the dynamic resilience requirements of AI clusters. However, the research on proactive resilience enhancement in AI clusters is almost blank. To fill this gap, we devise a grouping task migration mechanism (GTMM), which jointly considers adaptive server grouping and proactive task migration. Specifically, we first employ an adaptive server grouping algorithm to classify servers, offering customized protection based on tenants’ security requirements. Then, we formulate the task scheduling process as a multi-objective optimization problem for making a trade-off between security, power consumption, and load balance. Next, we propose a DRL-based task migration algorithm to separate tenants that have completed co-residency for mitigating co-resident threats. Lastly, the simulation experiments demonstrate that GTMM’s security outperforms the baselines with only an affordable performance degradation.