RDMA has become one of the most prominent networking technologies in DCNs by providing high bandwidth and ultra-low latency, especially for data-intensive applications. An important challenge with RDMA is to exploit multi-path for high throughput and reliability. Several studies have been proposed to utilize multi-path in RDMA networks, but they commonly require modification of RDMA NICs, which makes it hard to deploy them in practice. In this paper, we propose a user-level multi-path RDMA (UL-MPRDMA) scheme, in which a flow is partitioned into sub-flows, and transferred via multiple connections to make full use of multiple paths in DCNs. UL-MPRDMA quickly responds to sudden network trouble and congestion by performing dynamic sub-flow scheduling, and also effectively avoids the performance degradation problem due to the limited memory of RDMA NICs without the intervention of CPU. We implement UL-MPRDMA on a real test-bed with commercial RDMA NICs, and show that UL-MPRDMA can achieve 30% higher link utilization than an existing RDMA transport technique.
Backscatter communication is a promising technology in the hyper-connected era. Because of its ultra-low energy consumption, it can be used in various applications, but there are performance issues due to high uncertainty. We propose a signal-to-data translation model that can transform an entire backscatter signal into the original data. To train the translation model, we developed an automation framework that can efficiently collect datasets. We also proposed a data augmentation technique suitable for backscatter signals.In extensive experiments, our model significantly outperformed a simple rule-based decoding method and a commercial RFID reader. The proposed model showed consistent performance gains across different locations, obstacles, and mobility scenarios indicating a good generalization of learning.
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