With the rapid advancement of information technology, traditional centralized cloud computing systems face challenges in meeting the stringent low-latency demands of emerging applications. To tackle this issue, this paper proposes a delay-aware cloud-edge architecture that incorporates the distributed characteristics of edge infrastructure, enabling low latency collaboration among edge nodes within the same geographic region. Furthermore, based on this architecture, a dynamic data replica management scheme is introduced, involving synergistic mechanisms between edge nodes and cloud centers to optimally place data replicas on the most suitable edge nodes. The scheme adopts a hierarchical strategy: edge nodes perform short-term localized management of data replicas, while the cloud executes long-term holistic oversight. Experimental results demonstrate that the dynamic approach effectively reduces user access latency, minimizes replica migration frequency, and decreases network bandwidth consumption.

With the wide application of deep learning, the amount of data required to train deep learning models is becoming increasingly larger, resulting in an increased training time and higher requirements for computing resources. To improve the throughput of a distributed learning system, task scheduling and resource scheduling are required. This paper proposes to combine ARIMA and GRU models to predict the future task volume. In terms of task scheduling, multi-priority task queues are used to divide tasks into different queues according to their priorities to ensure that high-priority tasks can be completed in advance. In terms of resource scheduling, the reinforcement learning method is adopted to manage limited computing resources. The reward function of reinforcement learning is constructed based on the resources occupied by the task, the training time, the accuracy of the model. When a distributed learning model tends to converge, the computing resources of the task are gradually reduced so that they can be allocated to other learning tasks. The results of experiments demonstrate that RLPTO tends to use more compu-ting nodes when facing tasks with large data scale and has good scalability. The distributed learning system reward experiment shows that RLPTO can make the computing cluster get the largest reward.