Top-k elephant flow prediction method and system based on discrete tensor filling

Abstract

The invention discloses a top-k elephant flow prediction method and system based on discretization tensor filling. The method comprises: acquiring tensors containing missing flow data from known flowdata; decomposing the tensor into three discrete binary factor matrixes to form a real value factor matrix; expressing real value tensor data by using binary factor vectors in three dimension directions of a tensor source node, time and a target node respectively, wherein the elements of the three factor matrixes are used as the binary factor vectors; representing the missing flow data at each moment by using the inner product of the binary factor vectors in the three-dimensional direction, and calculating a Hamming distance through a high-efficiency data prediction method based on bit operation to replace the inner product; calculating a Hamming distance by using a top-k prediction acceleration method based on binary code segmentation, and determining whether corresponding real value tensor data is a top-k elephant flow or not according to the Hamming distance; and retrieving all the real value tensor data, and returning the first k pieces of maximum real value tensor data to obtain atop-k elephant flow. The problem of calculation complexity in the prior art is solved, and time and space complexity are reduced.

Description

technical field [0001] The invention relates to the technical field of data interaction, in particular to a top-k elephant flow prediction method and system based on discrete tensor filling. Background technique [0002] Elephant flow occupies a very important position in network traffic, and is especially important for network status analysis. Prediction of the top-k largest flows, also known as top-k elephant flow prediction, is a fundamental network management function. Many management applications can benefit from efficient identification of top-k elephant flows, including dynamic scheduling of elephant flows through congestion control, network capacity planning, anomaly detection, and caching of forwarding table entries, etc. [0003] At present, there are many researches on top-k elephant flow prediction at home and abroad, which can be roughly divided into two categories: [0004] First, some literature studies try to use a small amount of memory to measure the card...

AI Technical Summary

Problems solved by technology

[0015] The present invention provides a top-k elephant flow prediction method and system based on discretized tensor filling, which is used to overcome the defects in the prior art such as too long time-consuming due to high space complexity, and greatly reduce time and effort. reduce space complexity

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