一种用于流量计数统计的改进线性可提取草图数据结构
An Improved Linear Extractable Sketch Data Structure for Flow Count Statistics
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中文总结 AI 辅助
研究流量计数统计的草图数据结构，改进FermatSketch，通过为桶存储计数器线性组合，利用额外计算资源放宽列出键-计数器对的要求，提高空间效率，初步实验显示数据结构所需内存显著减少。
中文摘要 AI 辅助
草图数据结构在计算流数据统计方面非常有用，如网络流量、服务器请求和金融交易等。近期工作中引入了FermatSketch作为监测网络状态变化的基础数据结构，它是线性数据结构，维护计数器关联数组，支持列出所有键-计数器对且使用近线性空间。本文展示了通过在列出键-计数器对时使用额外计算资源来放宽要求，从而提高空间效率且统计时开销小。通过为每个桶存储计数器的多个线性组合来实现，系数由键生成，可解特定线性系统获键-计数器对。初步实验表明该数据结构所需内存显著减少，此工作是空间与时间的权衡。
英文摘要
Sketch data structures are very useful for computing statistics on streaming data, including network traffic, server requests, and financial transactions. In recent work, FermatSketch was introduced as an underlying data structure used to monitor changes in network states. It is a linear data structure that maintains an associated array of counters and supports listing all key-counter pairs while using almost linear space. Because it is linear, it can be used to monitor changes between two streams with space proportional to the number of items that change. The data structure is based on a hash table, and all key-counter pairs can be successfully listed when there are slots in the table with exactly one key hashed to them. We show how to relax this requirement by using additional computational resources when listing the key-counter pairs, thereby improving space efficiency with only a small overhead when collecting statistics. We achieve this by storing, for each bucket, multiple linear combinations of the counters whose coefficients are generated from the keys. With this information, certain linear systems can be solved to obtain the key-counter pairs. A preliminary experiment shows a significant reduction of memory needed for the data structure. Our work can be viewed as a trade-off between space and time.
