Detection of Service Provider Hardware Over-commitment in Container Orchestration Environments

Abstract

The deployment of container-based services continues to increase as time passes, mainly due to its fast provision time and lower allocation overheads. Yet, the literature still neglects the performance degradation in containers due to multi-tenancy and service provider hardware over-commitment. This paper proposes a new hardware over-commitment detection for container orchestration environments, implemented twofold. First, the containerized hardware usage of deployed containers is continuously monitored in a non-intrusive manner, leveraging the container engine resource management interface. Second, collected features are used by a recurrent neural network model for detecting both container and service level hardware over-commitment, following a time-series rationale. Experiments run on a containerized Apache Spark distribution have shown that multi-tenancy and hardware over-commitment significantly affect its performance. In addition, our proposed model is able to detect hardware over-commitment with up to 91% of true-positive at the container level, and up to 93% true-positive at the service level.