# A Case Study on Virtual HPC Container Clusters and Machine Learning Applications
## 2 Related Work
### ML for Anomaly Detection  
There is a notable gap in studies on implementing ML algorithms in dockerized HPC environments. Such studies could provide valuable insights into the efficiency and scalability benefits of Docker in HPC. [PERSON]'s study in [26] delved into the deployment of applications using Docker containers and Kubernetes clusters, highlighting the efficiency and scalability offered by these technologies in managing ML workloads. This approach mirrors the themes discussed in this study on virtual HPC clusters, where containerization plays a crucial role in enhancing the performance and manageability of ML applications. In this research, the emphasis is on leveraging the power of virtualization and containerization, as seen with technologies like Docker, to streamline the development and deployment of ML models. This methodology is crucial in handling the large-scale computational demands of ML, particularly in the training and inference phases. [PERSON]'s exploration of Kubernetes clusters for orchestrating containerized applications further complements this study's focus on virtual HPC clusters. Kubernetes, being a powerful tool for managing containerized applications at scale, aligns well with the needs of ML workflows, which often require complex coordination of resources and tasks. The aspect of system log analysis in [PERSON]'s article is particularly relevant in the context of monitoring and optimizing ML applications running on virtual HPC clusters. Effective log analysis can lead to insights into performance, resource utilization, and potential bottlenecks in these complex systems, thus enabling more informed decisions about optimizing computational resources and model parameters. [PERSON] et al. [27], [PERSON] [28], and [PERSON] [29] investigated anomaly detection techniques, which were closely related to this study's application of ML for anomaly detection in system logs. These works provided insights into the state-of-the-art methods in anomaly detection, an area explored within the Docker-based virtual HPC cluster. [PERSON] et al. [30] and [PERSON], [PERSON], and [PERSON] [31] focused on self-supervised learning approaches for anomaly detection and localization. Their findings are particularly relevant for understanding the application of advanced ML techniques in anomaly detection, a key component of this study. The works of [PERSON], [PERSON], and [PERSON] [32] on the Fashion-MNIST dataset, and [PERSON], [PERSON], and [PERSON] [33] on Bayesian optimization of ML algorithms provide foundational knowledge for ML applications, which informs this study's approach to utilizing ML in Docker environments. [PERSON] et al. [25] and [PERSON] et al. [34] discussed TensorFlow, a framework for large-scale ML on heterogeneous distributed systems. This is particularly relevant to this study's focus on ML in HPC environments, providing a potential tool for implementing ML algorithms. Container technology now underpins end-to-end AI/ML pipelines. Scientists are now packaging entire AI/ML workflows in containers. [PERSON] et al. [35] built a Docker-enabled federated-learning platform where TensorFlow workers and an MQTT broker ran in rootless containers. Experiments on an edge-cloud/HPC test-bed showed near-linear scaling on multimodal physiological streams. [PERSON] et al. [36] paired _udocker_ with Horovod to create a cluster-agnostic GPU-training pipeline. Because _udocker_ uses pure user-space Python, it works on several supercomputers without admin help and with parity to native Conda. For production pipelines that frequently rebuild images, [PERSON] [37] formalized best practices--layer caching, automated smoke tests, and post-run clean-up--that cut cloud costs, while keeping MPI/Slurm integration points intact. Despite the widespread interest in the subject of ML, there have been no studies describing the use of a containerized cluster to perform these types of calculations.