An Adaptive Cloud Resource Quota Scheme Based on Dynamic Portraits and Task-Resource Matching

Due to the unrestricted location of cloud resources, an increasing number of users are opting to apply for them. However, determining the appropriate resource quota has always been a challenge for applicants. Excessive quotas can result in resource wastage, while insufficient quotas can pose stability risks. Therefore, it's necessary to propose an adaptive quota scheme for cloud resource. Most existing researches have designed fixed quota schemes for all users, without considering the differences among users. To solve this, we propose an adaptive cloud quota scheme through dynamic portraits and task-resource optimal matching. Specifically, we first aggregate information from text, statistical, and fractal three dimensions to establish dynamic portraits. On this basis, the bidirectional mixture of experts (Bi-MoE) model is designed to match the most suitable resource combinations for tasks. Moreover, we define the time-varying rewards and utilize portrait-based reinforcement learning (PRL) to obtain the optimal quotas, which ensures stability and reduces waste. Extensive simulation results demonstrate that the proposed scheme achieves a memory utilization rate of around 70%. Additionally, it shows improvements in task execution stability, throughput, and the percentage of effective execution time.