7 results about "Traffic sampling" patented technology

The invention discloses a network digital twinning-oriented dual-view flow data sampling method and system, and mainly solves the problems of difficult key path capture and low flow reconstruction precision caused by lack of frequency domain perception of a flow sampling method in the prior art. According to the implementation scheme, historical flow interaction data in a network node sliding window are collected to form a third-order flow tensor and normalized, and node frequency domain features are extracted and then smoothly updated to obtain a smooth feature vector; mapping the vector to determine a node category, and dividing source-destination OD pair traffic categories of the whole network; calculating statistics lever scores of all OD pairs, dynamically allocating sampling budget of each category according to the statistics lever scores, and generating a sampling set and a binary sampling mask by adopting a determinacy and random combination strategy; and collecting the flow data of the corresponding position at the current moment based on the mask, constructing a tensor completion optimization model for solving completion, and outputting the recovered whole network flow data. According to the method, through double-view cooperation of the frequency domain features and the statistical lever fraction, precise coverage of a high-value traffic path is realized, and the traffic reconstruction precision of the digital twin network is effectively improved while the sampling overhead is reduced. The method can be applied to measurement and reconstruction of large-scale network traffic.

This invention discloses a device type identification method, terminal device, and storage medium based on traffic sampling. It eliminates the need to collect complete device traffic data; device identification is achieved simply by sampling traffic at the gateway. To address the issue of missing traffic features, traditional tensor imputation algorithms learn the embeddings corresponding to tensor rows, columns, and depths, but cannot generalize to unknown rows, columns, or depths. This results in repeated retraining, time-consuming and costly feature imputation. This invention proposes an inductive tensor imputation method that utilizes historical information to learn and generate embedding functions, enabling fast and effective device traffic feature imputation.

A network perception method, distributed device, equipment and computer readable storage medium. In the device forwarding layer, the message is not encapsulated, the device bandwidth during forwarding is guaranteed, and the device bandwidth utilization is improved; real-time detection and flow sampling reporting are performed to guarantee data accuracy and low overhead of the control layer; the control layer sends information of the control layer state table according to a sending weight, so that the management layer can receive important network state information more preferentially, and the overall overhead of the management layer is reduced; compared with the existing network perception technology based on in-band measurement, by maintaining the forwarding layer state table, the perception level is adjusted from a data packet to a data flow level, the consumption of network bandwidth resources is reduced, and the processing complexity and the consumption of computing resources of the management layer are also reduced.

The application discloses a service rate evaluation method, device, equipment, storage medium and program product, the method comprises the following steps: collecting service rate data of user objects set in different analysis scenes; the service rate data comprises at least one group of service peak rate of different sampling periods; according to the service rate data corresponding to each analysis scene, the service peak rate ratio distribution matrix and the service peak average rate ratio matrix in the corresponding analysis scene are constructed; according to the service peak rate ratio distribution matrix and the service peak average rate ratio matrix in each analysis scene, the service rate of the corresponding analysis scene is estimated; the application can realize the estimation of the service peak rate of short sampling period and the bandwidth overrun probability by using the existing traffic sampling function of the existing network, and does not need to upgrade the network and construct an analysis platform, so that the accurate bandwidth use performance analysis demand in the packet network can be met, and the cost is effectively reduced.

Presented herein are systems and methods for reactive hard zoning. In one or more embodiments, a monitoring and visibility services (MVS) controller receives zoning information from a centralized discovery controller and also receives data traffic flow information from traffic samplers that continuously monitor network traffic flows (e.g., using SFLOW) in the network. Responsive to the MVS detecting a violating data traffic flow (i.e., zoning configuration does not allow a host to access a storage port), an enforcement engine of the MVS may cause one or more actions to be taken relative to the detected violation. An action may comprise adding a networking configuration (e.g., ACL or disable (a.k.a., fence) the interface on the switch that the host is attached to) to deny that communication, flagging the communication, etc. The network configuration may be reset to a default policy either automatically (e.g., timeout) or manually via administrative configuration.

This invention relates to the field of graphics card chip technology and discloses a method and system for dynamic scheduling of computing power in a graphics card cluster. The method includes acquiring graphics card utilization and network traffic sampling sequences, obtaining computationally and communication-intensive stage label sequences through timestamp alignment and stage analysis; calculating the peak computing power and bandwidth requirements of the two types of stages and prioritizing tasks; matching target graphics card groups, generating reserved bandwidth channels by pre-allocating network paths in conjunction with stage switching windows; and synchronizing execution parameters and updating the running status sequence in a closed loop. This method can achieve precise dynamic scheduling of graphics card cluster computing power, meeting the dual requirements of high-performance computing efficiency and resource utilization.

An embodiment trains, using a first plurality of real data packets transmitted over a communications network, a generative adversarial network (GAN) to generate a first plurality of generated data packets corresponding to the plurality of real data packets. An embodiment generates, using the trained GAN, a second plurality of generated data packets from a second plurality of real data packets transmitted over the communications network. An embodiment generates, using a probability distribution of the second plurality of real data packets and a probability distribution of the second plurality of generated data packets, a plurality of sampling indices, each sampling index in the plurality of sampling indices comprising a packet number to be sampled for inspection. An embodiment inspects, using a packet inspector, a third plurality of data packets transmitted over the communications network, each inspected data packet having an index in the plurality of sampling indices.