The state-of-the-art scheduler of containerized cloud services considers load-balance as the only criterion and neglects many others such as application performance. In the era of Big Data, however, applications have evolved to be highly data-intensive thus perform poorly in existing systems. This particularly holds for Platform-as-a-Service environments that encourage an application model of stateless application instances in containers reading and writing data to services storing states, e.g., key-value stores. To this end, this work strives to improve today's cloud services by incorporating sensitivity to both load-balance and application performance. We built and analyzed theoretical models that respect both dimensions, and unlike prior studies, our model abstracts the dilemma between load-balance and application performance into an optimization problem and employs a statistical method to meet the discrepant requirements. Using heuristic algorithms and approaches we try to solve the abstracted problems. We implemented the proposed approach in Diego (an open-source cloud service scheduler) and demonstrate that it can significantly boost the performance of containerized applications while preserving a relatively high load-balance.