The rapid growth of data across fields of science and industry has increased the need to improve the performance of end-to-end data transfers while using the resources more efficiently. In this paper, we present a dynamic, multiparameter deep reinforcement learning (DRL) framework that adjusts application-layer transfer settings during data transfers on shared networks. Our method strikes a balance between high throughput and low energy utilization by employing reward signals that focus on both energy efficiency and fairness. The DRL agents can pause and resume transfer threads as needed, pausing during heavy network use and resuming when resources are available, to prevent overload and save energy. We evaluate several DRL techniques and compare our solution with state-of-the-art methods by measuring computational overhead, adaptability, throughput, and energy consumption. Our experiments show up to 25% increase in throughput and up to 40% reduction in energy usage at the end systems compared to baseline methods, highlighting a fair and energy-efficient way to optimize data transfers in shared network environments.

翻译：科学和工业领域数据的快速增长，要求在更高效利用资源的同时提升端到端数据传输性能。本文提出一种动态多参数深度强化学习框架，可在共享网络上调整数据传输过程中的应用层传输设置。该方法通过采用同时关注能效和公平性的奖励信号，在高吞吐量与低能耗之间取得平衡。深度强化学习代理能够根据需要暂停和恢复传输线程——在网络负载高时暂停，在资源可用时恢复——从而防止过载并节约能源。我们评估了多种深度强化学习技术，并通过计算开销、适应性、吞吐量和能耗指标，将我们的解决方案与现有先进方法进行了比较。实验表明，与基线方法相比，终端系统的吞吐量提升高达25%，能耗降低高达40%，这为在共享网络环境中优化数据传输提供了一种公平且节能的方案。