In the past decade, serverless computing has emerged as a widely used paradigm due to its simplicity and cost-effectiveness. Under this architecture, cloud service providers receive requests from tenants and schedule them to the clusters. Existing scheduling solutions, driven by economic considerations, often aim for load balancing or maximizing resource utilization. However, resource sharing among tenants in serverless computing may lead to anomalies of one tenant affecting others, thereby reducing quality of service for tenants. To bridge this gap, we explore scheduling strategies that can achieve anomaly isolation between all tenants and formalize it as a nonlinear mixed-integer programming problem. We introduce an approximation algorithm based on submodular function theory, capable of running in polynomial time with a guaranteed approximation ratio of 1-1/e. Simulations using real-world production data demonstrate the effectiveness of our approach. The method successfully prevents risks caused by thousands of function isolation conflicts, while maintaining load balancing factor degradation within \(5\%\) compared to non-isolation-enforced schemes.