Abstract
The enormous amount of network equipment and users implies a tremendous growth of Internet traffic for multimedia services. To mitigate the traffic pressure, architectures with in-network storage are proposed to cache popular content at nodes in close proximity to users to shorten the backhaul links. Meanwhile, the reduction of transmission distance also contributes to the energy saving. However, due to limited storage, only a fraction of the content can be cached, while caching the most popular content is cost-effective. Correspondingly, it becomes essential to devise an effective popularity prediction method. In this regard, existing efforts adopt dynamic graph neural network (DGNN) models, but it remains challenging to tackle sparse datasets. In this paper, we first propose a reformative temporal graph network, which is named STGN, that utilizes extra semantic messages to enhance the temporal and structural learning of a DGNN model, since the consideration of semantics can help establish implicit paths within the sparse interaction graph and hence improve the prediction performance. Furthermore, we propose a user-specific attention mechanism to fine-grainedly aggregate various semantics. Finally, extensive simulations verify the superiority of our STGN models and demonstrate their high potential in energy-saving.
Abstract (translated)
大量的网络设备和用户意味着互联网对于多媒体服务的流量有着巨大的增长。为了缓解流量压力,我们建议将内置存储的网络架构在用户附近的关键节点缓存 popular 的内容,以缩短反向传输链路。同时,缩短传输距离也有助于提高能源节约。然而,由于存储有限,只能缓存一小部分内容,而缓存最受欢迎的内容则是成本效益最高的。因此,开发有效的流行度预测方法变得至关重要。在这方面,现有的努力采用了动态图神经网络模型(DGNN),但处理稀疏数据集仍然具有挑战性。本文首先提出了一种革新的时间图网络,名为 STGN,该网络利用额外的语义信息来提高 DGNN 模型的时间和结构学习能力,因为考虑语义可以帮助我们在稀疏交互图中建立隐含路径,从而提高预测性能。此外,我们提出了一种用户特定的注意力机制,以精细地聚合各种语义信息。最后,广泛仿真验证了我们 STGN 模型的优越性,并展示了其在能源节约方面的高潜力。
URL
https://arxiv.org/abs/2301.12355
