  Container orchestration technologies are widely employed in cloud computing,
facilitating the co-location of online and offline services on the same
infrastructure. Online services demand rapid responsiveness and high
availability, whereas offline services require extensive computational
resources. However, this mixed deployment can lead to resource contention,
adversely affecting the performance of online services, yet the metrics used by
existing methods cannot accurately reflect the extent of interference.
  In this paper, we introduce scheduling latency as a novel metric for
quantifying interference and compare it with existing metrics. Empirical
evidence demonstrates that scheduling latency more accurately reflects the
performance degradation of online services. We also utilize various machine
learning techniques to predict potential interference on specific hosts for
online services, providing reference information for subsequent scheduling
decisions. Simultaneously, we propose a method for quantifying node
interference based on scheduling latency. To enhance resource utilization, we
train a model for online services that predicts CPU and MEM (memory) resource
allocation based on workload type and QPS. Finally, we present a scheduling
algorithm based on predictive modeling, aiming to reduce interference in online
services while balancing node resource utilization. Through experiments and
comparisons with three other baseline methods, we demonstrate the effectiveness
of our approach. Compared with three baselines, our approach can reduce the
average response time, 90th percentile response time, and 99th percentile
response time of online services by 29.4%, 31.4%, and 14.5%, respectively.
