多级树型网络中并行签名搜索策略的概率性能分析
Probabilistic Performance Analysis of Parallel Signature Search Strategies in Multi-Level Tree Networks
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中文总结 AI 辅助
该研究针对多级树型网络的并行签名搜索策略，开发了概率框架预测完成时间，经多核原型验证，可提供高效先验预测以支持资源成本优化。
中文摘要 AI 辅助
分层分布式搜索是指在树型文件集合中定位数据模式（即签名），它是分布式索引遍历、深度包检测和序列比对的基础。从业者必须决定采用多少并行度：是按顺序扫描每一层、在子树内扇出，还是一次性启动整棵树。现有分析仅部分回答了该问题：它们通过含签名文件的统计量来表征每个节点，而对于多签名文件，则需要仅在运行时才能获取的量。我们开发了一种概率框架，可在读取任何文件前预测五种搜索策略的完成时间，这些策略涵盖从顺序到整树并行的范围。节点扫描时间被建模为签名存在性的混合，层时间为顺序统计量，并行子树扫描通过极值理论推导；当签名数量已知时，容量约束下的占用情况通过生成函数处理。每个性能公式都带有精确性标签：精确（或区域内精确）、插件式、渐近式或边界式，每种近似都针对蒙特卡洛模拟进行了量化，并确定了其适用区域。一个多核原型复现了整树、层和子树级并行之间的粗略分离，但表明同步开销会消除相近策略之间的预测分离。该框架提供了具有明确精度区域和可忽略计算成本的先验完成时间预测，所评估的设计示例在不到一毫秒内完成；这些时序模型可支持后续的资源成本优化。
英文摘要
Hierarchical distributed search, locating a data pattern, or signature, across a tree-structured collection of files, underlies distributed index traversal, deep packet inspection and sequence alignment. A practitioner must decide how much parallelism to employ: scan each layer sequentially, fan out within subtrees, or launch the whole tree at once. Existing analyses answer this only partially: they characterize every node by the statistics of a signature-holding file and, for multi-signature files, need quantities revealed only at run time. We develop a probabilistic framework predicting the completion time of five search strategies, spanning sequential to full-tree parallelism, before any file is read. Node scan times are modeled as a mixture over signature presence, layer times as order statistics, and parallel subtree scans by extreme-value arguments; when signature counts are known, occupancy under capacity constraints is treated by generating functions. Each performance formula carries an exactness label: exact (or exact-in-regime), plug-in, asymptotic or bound, with each approximation quantified against Monte Carlo simulation and its regime identified. A multicore prototype reproduces the coarse separation between full-tree, layer- and subtree-level parallelism, but shows that synchronization overhead can erase the predicted separation between close strategies. The framework delivers a priori completion-time predictions with explicit accuracy regimes and negligible computational cost, the design example evaluated in under a millisecond; these timing models can support subsequent resource-cost optimization.
