# Performance Analysis of Container Effect in Deep Learning Workloads and Implications
## 2 Related Work  
In this section, we briefly summarize previous research on deep learning workload characterization and container-based systems. We also review techniques proposed to improve the performance of such systems. Recently, research has been conducted to characterize deep learning workloads in terms of reference patterns and resource usage. [PERSON] et al. comprehensively analyze memory reference patterns for neural network workloads and observe that they are significantly different from traditional workloads [19]. In particular, their analysis shows that the heap and data regions account for most memory references in deep learning workloads, but the memory reference bias is weaker than in traditional workloads, especially for write operations. [PERSON] et al. explored resource usage characteristics of containers used to train deep learning models [12]. They identify recurring patterns and similarities across containersand suggest the potential to optimize resource allocation in a dedicated deep-learning cluster. Specifically, they utilize clustering techniques and conditional restricted Boltzmann machines to discover action steps during deep learning training. They also optimize resource allocation by dynamically adjusting container resources based on phase-specific statistical information. However, their approach only addresses resource allocation and overlooks communication considerations. Also, their methods are only effective for deep learning workloads with repetitive resource usage patterns and require sufficient historical data and clustering training. [PERSON] et al. investigated the effectiveness of Docker containers as a means to simplify the deployment and management of deep learning workloads [20]. In particular, they evaluate the impact of Docker containers on the performance of deep learning workloads. Their findings indicate that both CPU- and GPU-intensive tasks exhibit minimal overhead when running inside Docker containers. This means that Docker containers can be used for deep learning workloads without significant performance degradation. However, it is important to note that their study only evaluates the performance of Docker containers on specific hardware types, so different hardware configurations are likely to produce different performance results. As a result, further research is needed to evaluate Docker container performance across different hardware types and deep learning frameworks [20]. [PERSON] et al. investigated the performance of Intel-Caffe, a distributed deep learning framework, on the Nurion supercomputers. Specifically, they focus on identifying the file I/O factors that affect the performance of Intel-Caffe in a container-based environment [21]. They observed that although the training phase of deep learning in a container-based environment has minimal overhead, page cache has a significant impact on the performance of deep learning frameworks. [PERSON] et al. analyzed container workload characteristics by collecting system trace data from host systems [22]. Specifically, they classified containers based on their resource usage and behavior and identified idle containers in order to efficiently manage container clusters. However, their experiments use benchmarking tools to generate test data, which may not be representative of real AI workloads. [PERSON] et al. explored resource consumption of containerized workloads on edge servers, with a particular focus on CPU resources, which are heavily consumed by container management and inter-container communication by daemon processes [23]. They present custom containers to improve CPU efficiency and evaluate the effectiveness of the algorithm based on specific metrics such as inter-container transfers, number of container starts, and application execution duration. Since their research focuses on CPU resources for network communication, additional considerations will be needed for deep learning workloads, where memory is another important bottleneck. [PERSON] et al.