In modern cloud architectures, multiple applications often run within the same clustered environment, sharing underlying resources. This resource sharing can cause interference among applications, leading to degraded latency and reduced system stability. As containerized microservices become increasingly central to cloud-native applications, their performance can suffer from complex interference scenarios related to resource competition. Meanwhile, most existing microservice approaches address interference either by detecting and localizing performance issues or by optimizing latency alone, without explaining why specific co-locations cause cross-application interference, and how this can inform service placement optimization. This work closes that gap by building a spatio-temporal data structure that captures the causal effects of cross-application interference. These causal effects are mathematically formalized as necessary and sufficient conditional probabilities that inform a multi-objective optimizer (Optuna). Cross-application profiling is used to simulate traces and estimate interference probabilities, while per-service latency baselines are provided by performance data, such as 95th-percentile response times (p95). Our approach supports network penalties, application isolation requirements, and adjustable weighting of necessary and sufficient causal metrics. Experimental results on real multi-application workloads show that interference-aware placements significantly reduce cross-application interference and improve response performance. Ultimately, the causality-driven multi-objective formulation gives cloud operators explicit control over interference, latency, and communication overhead when configuring service placements.

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