Proactive Task Migration with Server Grouping for Co-Resident Mitigation in AI Computing Clusters

With the rise of AI-Generated Content (AIGC), multi-tenant training in large computing clusters has become a prominent trend. However, when multiple tasks from different tenants of varying scales coexist in a cluster, they pose significant co-resident eavesdropping risks. Unfortunately, distributed training tasks are particularly prone to leaks of model parameters and data when they receive uniform security protection. Research on co-resident eavesdropping and hierarchical security defenses for distributed training remains sparse. To address this, we propose a novel Grouping Task Migration Mechanism (GTMM) that integrates server grouping with task migration. First, servers are clustered according to their security levels, enabling tailored protection. We then cast task migration as a multi-objective optimization problem to mitigate co-resident threats. Next, a DDQN-based task-migration algorithm derives optimal migration decisions. Lastly, our experiments demonstrate that GTMM consistently outperforms baseline methods.