论文标题
通过草图分解和矩阵优化检测流量级数据包丢失检测
Flow-Level Packet Loss Detection via Sketch Decomposition and Matrix Optimization
论文作者
论文摘要
对于云服务提供商而言，跨数据中心的细粒数据包丢失检测对于提高其服务水平和增加业务收入至关重要。但是，由于基本的限制，负责通信的广阔网络链接不在其管理中，因此无法获得足够的测量结果。此外，WAN中的毫秒级延迟抖动和时钟同步错误禁用了许多在数据中心网络上在此问题上运行良好的工具。因此，迫切需要开发一种新的工具或方法。在这项工作中，我们提出了从未从未考虑过的数学角度来提出一种新颖的损失检测方法。它的关键是将草图在上游分解为几个子饼干，并构建一个低级矩阵优化模型来解决它们。测试床上的广泛实验证明了其优越性。
For cloud service providers, fine-grained packet loss detection across data centers is crucial in improving their service level and increasing business income. However, the inability to obtain sufficient measurements makes it difficult owing to the fundamental limit that the wide-area network links responsible for communication are not under their management. Moreover, millisecond-level delay jitter and clock synchronization errors in the WAN disable many tools that perform well in data center networks on this issue. Therefore, there is an urgent need to develop a new tool or method. In this work, we propose SketchDecomp, a novel loss detection method, from a mathematical perspective that has never been considered before. Its key is to decompose sketches upstream and downstream into several sub-sketches and builds a low-rank matrix optimization model to solve them. Extensive experiments on the test bed demonstrate its superiority.
