Job placement using reinforcement learning in GPU virtualization environment

Abstract

Graphics Processing Units (GPU) are widely used for high-speed processes in the computational science areas of biology, chemistry, meteorology, etc. and the machine learning areas of image and video analysis. Recently, data centers and cloud companies have adopted GPUs to provide them as computing resources. Because the majority of cloud providers allocate the GPU resource to users in an exclusive access method, the allocated GPU resource may not be all used. Although the method of allocating a GPU resource to multiple users for sharing can increase the resource utilization, performance degradation may occur in individual jobs because of interference between different jobs. It is difficult for a cloud provider to predict or control the performance of various applications executed on various cloud resources by considering their characteristics heuristically. Therefore, an intelligent job placement technique is required to minimize the interference between different jobs and increase resource utilization. This study defines the resource utilization history of applications and proposes a reinforcement learning-based job placement technique, which uses it as an input. For resource utilization history learning, a deep reinforcement learning model (DQN) is used. As a result of learning, the current resource’s state is not exceeded, and the resource is still provided by predicting which commonly placed jobs will have less impact on the total performance when executed simultaneously. This approach prevents the performance degradation of applications with diverse execution characteristics and increases the resource utilization by executing the applications while sharing the resources. The superiority of this study is demonstrated by using the proposed learning method and other methods to analyze workloads with various resource utilization characteristics. Through the experiments, it is proven that the proposed method facilitates a reduction of the total execution time and the effective use of resources, while the maintaining performance.