11 results about "Elephant flow" patented technology

The invention relates to an elephant stream transmission method and device, network equipment, a storage medium and a program product. The method comprises the following steps: identifying quintuple information and queue pair information of an elephant stream to be processed; dividing the elephant flow to be processed into a plurality of sub-workflows according to the current rate of the elephant flow to be processed, the maximum bearing capacity of each network path and the queue pair information; each network path is determined according to the quintuple information; and according to the priority information of each sub-workflow and the delay information of each network path, allocating a corresponding network path to each sub-workflow so as to transmit each sub-workflow. By adopting the method, the problem of elephant flow congestion can be effectively solved.

Disclosed in the present invention is an SDN-architecture-based routing method for guaranteeing network QoS. The method comprises: on the basis of network topology information, performing abstraction to obtain a directed graph; then, on the basis of an improved K-means algorithm, dividing data into mouse flows, elephant flows and mammoth flows; defining a traffic transmission constraint condition; constructing a DQN neural network; training the DQN neural network by using the maximum Q value, which corresponds to an action a having the maximum reward function R, as an objective, so as to obtain a QoS guarantee routing model; obtaining an optimal path weight value; determining a data packet sending path on the basis a Dijkstra algorithm; and finally issuing path information to a switch. The present invention avoids the situations in which data processing performed by using a conventional K-means algorithm is not stable enough and a classification result is not accurate enough. During the training of a DQN neural network, data is extracted from an experience pool according to the priority of data, such that the neural network pays more attention to important data, thereby improving the learning efficiency of the neural network and the performance of the algorithm.

The invention discloses a congestion large flow rerouting method and system based on network state perception, and the method specifically comprises the steps: carrying out the statistical calculation of the contribution degree of each flow to a queue in a congestion state, dynamically determining a large flow judgment threshold value through combining with the number of active flows, and recognizing the flow with the contribution degree exceeding the large flow judgment threshold value as a responsibility elephant flow; based on the liability elephant flow list and the network state information, modeling the rerouting problem into a mixed integer programming model with the goal of minimizing the maximum link utilization rate, and solving to obtain a global optimal path allocation scheme; and according to the global optimal path distribution scheme, a new path forwarding rule is installed on each switch and an old path rule is deleted by adopting a first-building and second-dismounting strategy. According to the invention, through cross-layer cooperation of the data plane and the control plane, sub-second congestion response and global optimal flow scheduling are realized, and the problem of congestion migration caused by traditional reactive rerouting is effectively avoided.

The invention relates to a comprehensive path optimization method for a wide-area cloud architecture network in the field of network cloud fusion. The method is particularly applicable to load balancing scheduling of a large number of continuous service flows (elephant flows) in a cloud architecture network. Comprising the following steps: monitoring a network state and identifying elephant flow, collecting information of each link, and detecting and identifying the elephant flow; topological path pre-screening and candidate path library construction improve the calculation and optimization efficiency. Initializing ant colony parameters, and formulating a multi-dimensional heuristic function; performing path exploration and probability selection, and generating an ant colony exploration calculation formula according to a target; dynamically updating pheromones, and searching an optimal path according to local pheromone updating and global pheromone updating; issuing an optimal path and updating a flow table; and continuous monitoring and dynamic adjustment are carried out. According to the invention, a dual control plane combining a routing protocol and an SDN protocol is designed, the improved ant colony algorithm based on network topology is adopted to optimize elephant flow path selection under a cloud network architecture, and the network global service transmission efficiency is improved.

The invention belongs to the field of information communication, and discloses a data center network flow resource allocation method and a related device, and the method comprises the steps: obtaining the state information of each current network flow of a data center; and calling a preset data center resource allocation model according to the state information of each current network flow of the data center to obtain a path of each network flow type of each current communication host pair of the data center and the bandwidth of each hop of the path. The preset data center resource allocation model can dynamically allocate corresponding paths and bandwidths to different network traffic types of each communication host pair of the data center, so that differentiated resource scheduling for dynamically adjusting differentiated demands of different network traffic types, such as mouse flow and elephant flow, is realized. Network congestion caused by disordered resource occupation of elephant flow in a traditional method is avoided, meanwhile, the low-delay requirement of mouse flow is guaranteed, and the transmission efficiency, the network throughput, the link utilization rate and the overall service quality of data center network flow are improved on the whole.

The embodiment of the invention provides a traffic scheduling method and device, and relates to the technical field of communication, the method is applied to a management center, and the method comprises the steps: receiving an alarm message sent by a firewall device, the alarm message being that the occupancy rate of the firewall device in a first CPU core reaches a preset alarm threshold in a continuous preset period, the firewall device is sent under the condition that the occupancy rate of the second CPU core with the lowest occupancy rate in the firewall device is lower than a preset alarm threshold; acquiring flow monitoring information of each data flow received by the first CPU core and reported by the firewall equipment and an overload reason of the first CPU core, and identifying elephant flows in each data flow based on the flow monitoring information of each data flow; determining an adjustment strategy for the elephant flow based on the overload reason; and issuing the adjustment strategy to the firewall device, so that the firewall device schedules the elephant flow based on the adjustment strategy. According to the scheme, on the premise that the performance pressure of firewall equipment is not increased, the elephant flow is scheduled, and overload of the CPU core is avoided.

The application discloses a kind of elephant flow identification and processing method in distributed energy security data acquisition device, it is related to data stream transmission technical field, and the network traffic data is obtained by flow acquisition equipment, and the characteristic value of data packet in unit time is counted by sliding window, data stream coefficient is calculated according to data packet characteristic value to judge to generate first elephant flow processing signal, simultaneously, CPU usage rate prediction model is constructed based on convolutional neural network, and real-time CPU usage rate is substituted into CPU usage rate prediction model to output expected CPU usage rate, to judge to generate second elephant flow processing signal, and the hierarchical result is obtained by hierarchical response mechanism to elephant flow, and differential processing strategy is executed according to hierarchical result, wherein, multi-channel shunt transmission is used to primary elephant flow, dynamic encryption transmission is started to secondary elephant flow, local cache is triggered to tertiary elephant flow, and security early warning signal is generated to execute security early warning mechanism.

A semiconductor chip for implementing aggregated flow detection and management includes a number of pipes, where each pipe is coupled to a portion of ports on the semiconductor chip that are to receive data packets. A logic is coupled to the pipes and is used to detect and manage an elephant flow. The elephant flow-detection and management logic includes a flow table and a byte counter.

This application provides a scheduling method, apparatus, electronic device, and medium, relating to the field of transmission and bearer network technology. The method includes: upon determining that a detected new connection event is an elephant flow, obtaining the source node, destination node, and bandwidth requirement of the elephant flow; pruning the path from the source node to the destination node according to the bandwidth requirement, and determining a candidate path set based on a four-factor weighted cost function of link occupancy, remaining bandwidth, physical hop count, and safety margin; calculating the incremental index of the first path in the candidate path set through sandbox simulation, and determining the first path as the target path if the incremental index passes security verification; and scheduling the elephant flow based on the target path. This reduces congestion caused by the default shortest path from the source, ensures low-latency transmission of small flows, and thus improves overall service performance.

The application discloses a flow label-based traffic scheduling method, device and equipment. The method comprises the following steps: a source computing node divides the service flow in a single RDMA queue pair into multiple independent sub-flows based on a preset semantic boundary; the source computing node generates an IPv6 flow label for each sub-flow, and fills the IPv6 flow label into the corresponding field of an IPv6 header when encapsulating a data packet; and the source computing node and / or a network node schedules the data packets of different sub-flows to different network paths for transmission based on the IPv6 flow label in a path selection process. The application can greatly improve the traffic entropy of the network by performing semantic subdivision on the service flow in the queue pair, dividing the service flow into multiple independent sub-flows, and allocating different IPv6 flow labels to each sub-flow, so that the elephant flow can be transmitted through multiple paths, and the out-of-order problem caused by the multi-path transmission can be avoided by using the intermittent characteristics of the traffic.

This invention discloses a network state-aware congestion-prone flow rerouting method and system. The method specifically includes: under congestion conditions, statistically calculating the contribution of each flow to the queue, dynamically determining a large flow judgment threshold based on the number of active flows, and identifying flows with contributions exceeding the large flow judgment threshold as responsible elephant flows; based on the responsible elephant flow list and network state information, modeling the rerouting problem as a mixed-integer programming model with the objective of minimizing maximum link utilization, and solving for the globally optimal path allocation scheme; according to the globally optimal path allocation scheme, installing new path forwarding rules and deleting old path rules on each switch using a build-then-tear strategy. This invention achieves sub-second congestion response and globally optimal traffic scheduling through cross-layer collaboration between the data plane and control plane, effectively avoiding the congestion migration problem caused by traditional reactive rerouting.