Lightweight Cooperative Network Traffic Measurement Method and System for Data Center Networks

Abstract

The present invention relates to the field of network measurement technologies, and specifically provides a lightweight collaborative network traffic measurement method and system for a data center network. The method includes: determining that a network flow to be measured is a first network flow or a second network flow, where the first network flow is larger than the second network flow; in the case where the network flow to be measured is the first network flow, evenly distributing its measurement tasks to each switch in a first path, where the first path is the transmission path of the network flow to be measured; in the case where the network flow to be measured is the second network flow, distributing its measurement tasks to a preset switch, where the preset switch is any switch in a second path, and the second path is the transmission path of the network flow to be measured; controlling the switches to measure and record the traffic information of the network flow to be measured according to the allocated measurement tasks; in response to a query instruction, obtaining the traffic information and calculating the number of data packets in the network flow to be measured. The data packet processing efficiency and overall measurement accuracy of the present invention are both relatively high.

Description

Technical Field

[0001] The present invention relates to the field of network measurement technologies, and particularly to a lightweight collaborative network traffic measurement method and system for a data center network. Background Art

[0002] Fine-grained network traffic monitoring can monitor and manage the network traffic in a server cluster in real time, optimize network performance, and improve user experience; it can also effectively prevent common network attacks and ensure the data security of users. In addition, efficient fine-grained network traffic monitoring can optimize the resource allocation in the entire network and reduce the network operation cost. Therefore, fine-grained network traffic monitoring has become an essential management means for network enterprises.

[0003] As the core hub of network services, a data center undertakes the important responsibility of connecting users and services, and is constantly carrying "massive" network traffic. With the continuous increase in the number of Internet users and the popularization of various online services, the user traffic faced by data centers has increased sharply. Large network companies such as Google, Facebook, and Amazon have hundreds of millions of users accessing network services through their data centers every day, which poses great challenges to the stable operation and efficient management of data centers. Therefore, monitoring and managing the network traffic of the entire network is not an easy task, especially for the data centers of large network companies. In addition, due to the need to provide real-time network services to a large number of users simultaneously, the traffic in a data center often has the important characteristic of "high speed". The data transmission speed in a data center is very fast. For example, hundreds of Gb of transmission need to be completed within 1 second. Facing the high-speed characteristic of network traffic, traditional measurement methods are difficult to capture and record the traffic passing through each switch in a timely manner.

[0004] There are a large number of existing Sketch-based measurement methods that can efficiently record traffic information, but these methods are limited to single-point measurement and are difficult to be extended to the measurement of the entire network traffic. Especially for a large data center network, the huge routing information, the bandwidth limitation of links, and the resource limitation of switches themselves will greatly affect the strategy formulation and actual deployment of the entire network traffic measurement. Since the centralized controller under the Software Defined Network (SDN) architecture has a global view of the entire network, the SDN architecture can be used to uniformly manage and control network traffic. However, in the actual measurement process, since the SDN architecture needs to monitor the status of switches in the network in real time and also needs to perform frequent collaborative communication between the controller and the switches, this architecture itself will bring huge bandwidth consumption to the entire link and is not an efficient solution.

[0005] In the case of measuring fine-grained network traffic in a large data center for the network traffic measurement solution in the related art, it is difficult to balance the packet processing efficiency and the overall measurement accuracy, and no effective solution has been proposed yet. Summary of the Invention

[0006] A lightweight collaborative network traffic measurement method and system for a data center network provided by an embodiment of the present invention can at least solve the problem that in the case of measuring fine-grained network traffic in a large data center for the network traffic measurement solution in the related art, it is difficult to balance the packet processing efficiency and the overall measurement accuracy.

[0007] The lightweight collaborative network traffic measurement method for a data center network provided by an embodiment of the present invention includes:

[0008] Determine that the network flow to be measured is a first network flow or a second network flow, where the first network flow is greater than the second network flow;

[0009] In the case that the network flow to be measured is the first network flow, evenly distribute the measurement task of the network flow to be measured to each switch in the first path, where the first path is the transmission path of the network flow to be measured;

[0010] In the case that the network flow to be measured is the second network flow, distribute the measurement task of the network flow to be measured to a preset switch, where the preset switch is any switch in the second path, and the second path is the transmission path of the network flow to be measured;

[0011] Control the switch to measure and record the traffic information of the network flow to be measured according to the assigned measurement task;

[0012] In response to a query instruction, obtain the traffic information and calculate the number of packets in the network flow to be measured.

[0013] The lightweight collaborative network traffic measurement method for a data center network provided by an embodiment of the present invention to determine that the network flow to be measured is a first network flow or a second network flow includes:

[0014] Calculate the hash value of the packets in the network flow to be measured based on the detector of the ingress switch, and store the hash value in the corresponding bucket;

[0015] In the case that the same packet appears in the network flow to be measured, control the counter in the bucket corresponding to the first hash value to increment by 1, where the first hash value is the same hash value of the same packet, and the identification fields of the same packets are the same;

[0016] When the hash values of different data packets in the network flow to be measured are the same, a hash collision occurs, and the counter in the bucket corresponding to the second hash value is decremented by 1. The second hash value is the same hash value of the different data packets, where the identification fields of the different data packets are different;

[0017] Among them, when the value of any of the counters is 0, it is determined that the network flow to be measured is a first network flow; when there is no counter with a value of 0, it is determined that the network flow to be measured is a second network flow.

[0018] For the lightweight collaborative network traffic measurement method for a data center network provided by an embodiment of the present invention, after determining that the network flow to be measured is a first network flow, the method further includes:

[0019] Storing the hash value of the network flow to be measured into a target bucket, where the target bucket is the bucket with a counter value of 0;

[0020] Resetting the value of the counter of the target bucket to 1.

[0021] For the lightweight collaborative network traffic measurement method for a data center network provided by an embodiment of the present invention, after determining that the network flow to be measured is a first network flow or a second network flow, the method further includes:

[0022] Before calculating the hash value of the data packets in the network flow to be measured based on the detector of the ingress switch, sampling the data packets in the network flow to be measured according to a preset probability;

[0023] Calculating the hash value of the sampled data packets based on the detector of the ingress switch, and storing the hash value of the sampled data packets into the corresponding bucket.

[0024] For the lightweight collaborative network traffic measurement method for a data center network provided by an embodiment of the present invention, the method further includes:

[0025] When the network flow to be measured is the second network flow, determining the hash interval matching requirement based on a preset network topology structure;

[0026] Among them, the different-length paths in the network topology structure are denoted as , the path is one or more, the length of the path is set to , h is the total number of layers of the switch, the hash interval to be maintained on the path is denoted as , and the matching requirement of the hash interval is:

[0027] ;

[0028] wherein, , j represents the j-th layer switch in the h-layer switch; , k represents the k-th layer switch in the h-layer switch;

[0029] According to the hash interval matching requirement and the network topology, allocate the measurement task of the network flow to be measured to a preset switch.

[0030] The lightweight collaborative network traffic measurement method for a data center network provided by an embodiment of the present invention creates a hash interval matching requirement based on a preset network topology, including:

[0031] Determine the hash interval matching requirement based on the network flow quantity threshold C and the preset network topology, and the network flow quantity threshold C needs to satisfy:

[0032] ;

[0033] wherein, is the number of the layer switches passing through the path , is the length of the hash interval ;

[0034] According to the network flow quantity threshold C, the hash interval matching requirement and the network topology, allocate the measurement task of the network flow to be measured to the preset switch.

[0035] The lightweight collaborative network traffic measurement method for a data center network provided by an embodiment of the present invention creates a control switch to record the traffic information of the allocated data packets, including:

[0036] Separate records of the traffic information are made based on a two-layer collaborative sketch, wherein the two-layer collaborative sketch is configured on each switch, and the two-layer collaborative sketch includes two layers of buckets. The first layer of buckets B L includes buckets, and the second layer of buckets B S includes buckets, is less than ;

[0037] When the network flow to be measured is the first network flow, record the traffic information of the data packets in the network flow to be measured based on the first layer of buckets B L ; when the network flow to be measured is the second network flow, record the traffic information of the data packets in the network flow to be measured based on the second layer of buckets B SRecord the traffic information of the data packets in the network flow to be measured.

[0038] The lightweight collaborative network traffic measurement method for a data center network provided by an embodiment of the present invention, where the traffic information is a flow label or an element label.

[0039] The lightweight collaborative network traffic measurement system for a data center network provided by an embodiment of the present invention includes:

[0040] A server that sends or receives the network flow to be measured;

[0041] A switch that forwards the network flow to be measured and processes the network flow to be measured;

[0042] A controller that determines whether the network flow to be measured is a first network flow or a second network flow, where the first network flow is greater than the second network flow;

[0043] The controller also, when the network flow to be measured is the first network flow, evenly distributes the measurement task of the network flow to be measured to each switch in the first path, where the first path is the transmission path of the network flow to be measured; when the network flow to be measured is the second network flow, distributes the measurement task of the network flow to be measured to a preset switch, where the preset switch is any switch in the second path, and the second path is the transmission path of the network flow to be measured;

[0044] The controller also controls the switch to measure and record the traffic information of the network flow to be measured according to the assigned measurement task;

[0045] The controller also responds to a query instruction, obtains the traffic information, and calculates the number of data packets in the network flow to be measured.

[0046] The electronic device provided by an embodiment of the present invention includes: a processor and a memory storing a program, where the program includes instructions that, when executed by the processor, cause the processor to execute the lightweight collaborative network traffic measurement method for a data center network.

[0047] A lightweight collaborative network traffic measurement method and system for a data center network provided by an embodiment of the present invention solve the problem that in the case of measuring fine-grained network traffic in a large data center, it is difficult to balance the packet processing efficiency and the overall measurement accuracy. By determining that the network flow to be measured is a first network flow or a second network flow, when the network flow to be measured is the first network flow, the measurement tasks of the network flow to be measured are evenly distributed to each switch on its transmission path to achieve packet-level measurement load balancing; when the network flow to be measured is the second network flow, the measurement tasks of the network flow to be measured are assigned to any switch on its transmission path to achieve flow-level measurement load balancing; thereby avoiding the packets of each network flow to be measured from being repeatedly measured by different switches, keeping the number of packets that each switch needs to process similar, and the number of network flows to be measured that each switch needs to process also similar, so as to ensure that the actual measurement loads of each switch are less different within a predetermined time, the packet processing efficiency is higher, and the overall measurement accuracy is higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other embodiments according to these drawings without creative efforts.

[0049] Figure 1 is a flowchart of the steps of a lightweight collaborative network traffic measurement method for a data center network in an embodiment of the present invention.

[0050] Figure 2 is a schematic structural diagram of TC Sketch in an embodiment of the present invention.

[0051] Figure 3 is a schematic diagram of a lightweight collaborative network traffic measurement system for a data center network in an embodiment of the present invention.

[0052] Figure 4 is a schematic diagram of the results of the maximum flow level measurement load ratio of 10 algorithms in the Fat Tree + CAIDA experimental environment in an embodiment of the present invention.

[0053] Figure 5 is a schematic diagram of the results of the maximum flow level measurement load ratio of 10 algorithms in the Fat Tree + IMC experimental environment in an embodiment of the present invention.

[0054] Figure 6It is a schematic diagram of the results of the measurement load ratio at the maximum flow level of 10 algorithms in the Spine Leaf + CAIDA experimental environment in the embodiments of the present invention.

[0055] Figure 7 It is a schematic diagram of the results of the measurement load ratio at the maximum flow level of 10 algorithms in the Spine Leaf + IMC experimental environment in the embodiments of the present invention.

[0056] Figure 8 It is a schematic diagram of the results of the measurement load ratio at the maximum packet level of 10 algorithms in the Fat Tree + CAIDA experimental environment in the embodiments of the present invention.

[0057] Figure 9 It is a schematic diagram of the results of the measurement load ratio at the maximum packet level of 10 algorithms in the Fat Tree + IMC experimental environment in the embodiments of the present invention.

[0058] Figure 10 It is a schematic diagram of the results of the measurement load ratio at the maximum packet level of 10 algorithms in the Spine Leaf + CAIDA experimental environment in the embodiments of the present invention.

[0059] Figure 11 It is a schematic diagram of the results of the measurement load ratio at the maximum packet level of 10 algorithms in the Spine Leaf + IMC experimental environment in the embodiments of the present invention.

[0060] Figure 12 It is a schematic diagram of the results of the average throughput of switches of 10 algorithms in the Fat Tree + IMC experimental environment in the embodiments of the present invention.

[0061] Figure 13 It is a schematic diagram of the results of the average throughput of switches of 10 algorithms in the Spine Leaf + IMC experimental environment in the embodiments of the present invention.

[0062] Figure 14 It is a schematic diagram of the results of the average absolute error of per-flow size measurement of 10 algorithms in the Fat Tree + CAIDA experimental environment, varying with the memory size, in the embodiments of the present invention.

[0063] Figure 15 It is a schematic diagram of the results of the average absolute error of per-flow size measurement of 10 algorithms in the Fat Tree + IMC experimental environment, varying with the memory size, in the embodiments of the present invention.

[0064] Figure 16It is a schematic diagram of the results of the F1 score of the first network flow detection of 10 algorithms in the Fat Tree + CAIDA experimental environment in the embodiments of the present invention, varying with the memory size.

[0065] Figure 17 It is a schematic diagram of the results of the F1 score of the first network flow detection of 10 algorithms in the Fat Tree + IMC experimental environment in the embodiments of the present invention, varying with the memory size.

[0066] Figure 18 It is a schematic diagram of the results of the mean absolute error of the first network flow detection of 10 algorithms in the Fat Tree + CAIDA experimental environment in the embodiments of the present invention, varying with the memory size.

[0067] Figure 19 It is a schematic diagram of the results of the mean absolute error of the first network flow detection of 10 algorithms in the Fat Tree + IMC experimental environment in the embodiments of the present invention, varying with the memory size.

[0068] Figure 20 It is a schematic diagram of the results of the F1 score of the first network flow detection of 10 algorithms in the Fat Tree + CAIDA experimental environment in the embodiments of the present invention, varying with the first network flow detection threshold.

[0069] Figure 21 It is a schematic diagram of the results of the F1 score of the first network flow detection of 10 algorithms in the Fat Tree + IMC experimental environment in the embodiments of the present invention, varying with the first network flow detection threshold.

[0070] Figure 22 It is a schematic diagram of the results of the mean absolute error of the first network flow detection of 10 algorithms in the Fat Tree + CAIDA experimental environment in the embodiments of the present invention, varying with the first network flow detection threshold.

[0071] Figure 23 It is a schematic diagram of the results of the mean absolute error of the first network flow detection of 10 algorithms in the Fat Tree + IMC experimental environment in the embodiments of the present invention, varying with the first network flow detection threshold.

[0072] Figure 24 It is a schematic diagram of the structure of the electronic device in the embodiments of the present invention. Detailed implementation manners

[0073] Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not used to limit the protection scope of the present invention.

[0074] As the core hub of network services, the data center undertakes the important responsibility of connecting users and services, and is constantly carrying "massive" network traffic at all times. Due to the need to provide real-time network services to a large number of users simultaneously, the traffic in the data center often has the important characteristic of "high speed".

[0075] There are a large number of existing measurement methods based on the Sketch data structure that can efficiently record traffic information, but these methods are limited to single-point measurement and are difficult to scale to the measurement of the entire network traffic. For large data center networks, the huge routing information, link bandwidth limitations, and resource limitations of the switches themselves will greatly affect the strategy formulation and actual deployment of the entire network traffic measurement. In addition, the centralized controller under the Software Defined Network (SDN) architecture has a global view of the entire network, and the SDN architecture can be used to uniformly manage and control network traffic. However, in the actual measurement process, since the SDN architecture needs to monitor the status of switches in the network in real time and also needs to perform frequent collaborative communication between the controller and the switches, this architecture itself will bring huge bandwidth consumption to the entire link and is not an efficient solution.

[0076] Considering that relevant strategies are formulated based on auxiliary information such as the topology structure, historical traffic data, and routing table of the entire network, the purpose is to ensure that any flow entering the network is measured by only a fixed one or more measurement nodes. However, existing collaborative measurement schemes do not efficiently solve the problem of duplicate packet recording, and do not take into account that the real-world traffic distribution is extremely uneven, that is, most of the traffic is small flows and only a very small number are large flows.

[0077] Taking the real network traffic dataset CAIDA19 as an example, when sorting the network flows in descending order, it will be found that the top 2% of the "large flows" account for 80% of the total network traffic, while the bottom 85% of the "small flows" only account for about 10% of the total network traffic. This shows that compared with "small flows", the number of "large flows" is smaller, but the number of packets in each "large flow" is larger. Therefore, although the existing collaborative measurement scheme can make the number of flows measured by each switch approximately the same, the actual measurement loads of each switch may vary greatly in a short period of time.

[0078] To this end, the embodiments of the present invention provide a lightweight collaborative network traffic measurement method and system for a data center network.

[0079] Please refer to Figure 1 the steps shown below. The lightweight collaborative network traffic measurement method for a data center network provided by the embodiments of the present invention includes:

[0080] Step S101: Determine whether the network flow to be measured is a first network flow or a second network flow, where the first network flow is larger than the second network flow.

[0081] Step S102: When the network flow to be measured is the first network flow, evenly distribute the measurement task of the network flow to be measured to each switch in the first path, where the first path is the transmission path of the network flow to be measured.

[0082] When the network flow to be measured is the second network flow, distribute the measurement task of the network flow to be measured to a preset switch, where the preset switch is any switch in the second path, and the second path is the transmission path of the network flow to be measured.

[0083] Step S103: Control the switch to measure and record the traffic information of the network flow to be measured according to the assigned measurement task.

[0084] Step S104: Respond to the query instruction, obtain the traffic information, and calculate the number of data packets in the network flow to be measured.

[0085] Those skilled in the art should understand that a network flow refers to a set of a series of data packets from a source point to one or more destination points in a network, and the size of a network flow refers to the number of data packets in the network flow; network traffic refers to the sum of all data packets transmitted in the network, regardless of which network flow these data packets belong to.

[0086] In the embodiments of the present invention, the "network flow to be measured" can be to calculate the network traffic carried by the data center within a predetermined measurement period and measure the number of data packets in each network flow in this network traffic, that is, each network flow carried by the data center within the predetermined measurement period is a network flow to be measured; it can also be to measure the size of a certain or multiple specified network flows during the measurement period to be measured and measure the number of data packets in the above-mentioned certain or multiple specified network flows, that is, the network flow to be measured refers to the above-mentioned certain or multiple specified network flows.

[0087] In other words, based on the lightweight collaborative network traffic measurement method for a data center network provided by the embodiments of the present invention, it can be used to measure the overall network traffic of the data center network or the local network traffic of the data center network.

[0088] When the network flow to be measured is the first network flow, evenly distributing the measurement task of the network flow to be measured to each switch in the first path means evenly distributing the data packets in the network flow to be measured to each switch in the first path. Because the number of data packets in the network flow to be measured in this case is large, if only one switch is used for measurement, the processing efficiency will be reduced and the load on this switch will also be too high. At the same time, the network flow to be measured in this case is completely transmitted to each switch in the first path, but each switch only measures the corresponding part of the network flow to be measured. That is, "evenly distributing" does not mean splitting the network flow to be measured into parts and then transmitting them to the corresponding switches separately, nor is it limited to equal distribution.

[0089] When the network flow to be measured is the second network flow, distributing the measurement task of the network flow to be measured to a preset switch, where the preset switch is any switch in the second path, means that the network flow to be measured in this case is only measured by one switch. Because the number of data packets in the network flow to be measured in this case is small, the measurement task can be distributed in units of flows.

[0090] In response to the query instruction, it can be that the controller responds to the manual query instruction and then queries the traffic information recorded in each switch; or it can be that the controller automatically queries the traffic information recorded in each switch according to the built-in program after the preset time arrives.

[0091] The lightweight collaborative network traffic measurement method for a data center network provided by the embodiment of the present invention creates a measurement task of the network flow to be measured to each switch on its transmission path to achieve packet-level measurement load balancing; when the network flow to be measured is the second network flow, distributing the measurement task of the network flow to be measured to any switch on its transmission path to achieve flow-level measurement load balancing; thereby avoiding the data packets of each network flow to be measured from being repeatedly measured by different switches, keeping the number of data packets that each switch needs to process similar, and the number of network flows to be measured that each switch needs to process is also similar, so as to ensure that the actual measurement loads of each switch are less different within a predetermined time, improving the data packet processing efficiency and the overall measurement accuracy.

[0092] Specifically, step S101, determining that the network flow to be measured is the first network flow or the second network flow includes:

[0093] Calculating the hash value of the data packets in the network flow to be measured based on the detector of the ingress switch, and storing the hash value in the corresponding bucket;

[0094] In the case where the same data packet appears in the network flow to be measured, control the counter in the bucket corresponding to the first hash value to increment by 1, where the first hash value is the same hash value of the same data packet, and the identification fields of the same data packets are the same;

[0095] In the case where the hash values of different data packets in the network flow to be measured are the same, a hash collision occurs, and control the counter in the bucket corresponding to the second hash value to decrement by 1, where the second hash value is the same hash value of the different data packets, and the identification fields of the different data packets are different;

[0096] Wherein, in the case where the value of any counter is 0, determine that the network flow to be measured is the first network flow; in the case where no counter value is 0, determine that the network flow to be measured is the second network flow.

[0097] Exemplarily, the above identification field is a flow label.

[0098] Preferably, before the detector based on the ingress switch calculates the hash value of the data packets in the network flow to be measured, sample the data packets in the network flow to be measured according to a preset probability.

[0099] Exemplarily, the above preset probability is less than 0.01. For example, sample the data packets in the network flow to be measured with a preset probability of 0.001 or 0.005.

[0100] The detector based on the ingress switch calculates the hash value of the sampled data packets, stores the hash value of the sampled data packets into the corresponding bucket, and then determines whether the network flow to be measured is the first network flow or the second network flow according to the above method.

[0101] Wherein, use the sampled data packets to replace all the data packets in the network flow to be measured. For example, only detect 0.1% or 0.5% of the data packets in the network flow to be measured, which can significantly shorten the time to determine whether the network flow to be measured is the first network flow or the second network flow, and the identification and determination of most of the network flows to be measured belonging to the second network flow can be ignored in terms of time.

[0102] Further, after determining that the network flow to be measured is the first network flow, the above network flow measurement method further includes:

[0103] Store the hash value of the network flow to be measured into the target bucket, where the target bucket is the bucket with a counter value of 0;

[0104] Reset the value of the counter of the target bucket to 1.

[0105] Exemplarily, the following Algorithm 1 is the content of the algorithm module in the detector:

[0106] Insertion() of LFD

[0107] Input: Packet

[0108] if then / / With a preset probability sample the packet set sample

[0109] Extract from / / For the sampled packets perform flow label extraction

[0110] Calculate the fingerprint and locate the bucket / / Calculate the flow label hash value and locate the hash value corresponding bucket

[0111] if then / / If the bucket is empty

[0112] / / Insert the hash value into the bucket

[0113] / / Increment the counter in the bucket by 1

[0114] else / / If the bucket already has a hash value inserted

[0115] if then / / If the bucket already has a hash value inserted

[0116] / / Increment the counter in the bucket by 1

[0117] else / / If the bucket already has another hash value inserted

[0118] / / Decrement the counter in the bucket by 1

[0119] if then / / If in the bucket When the counter in it is 0

[0120] / / Set the counter in the bucket to 1

[0121] / / Set the hash value inserted in the bucket to

[0122] where LFD is the abbreviation of Large Flow Detector, which is used to detect the first network flow; each LFD has buckets , , is a hash function indicating the hash value in bucket , indicating the counter counter in bucket .

[0123] Specifically, in step S102, when the network flow to be measured is the second network flow, determine the hash interval matching requirement based on the preset network topology structure, and according to the hash interval matching requirement and the network topology structure, allocate the measurement task of the network flow to be measured to the preset switch.

[0124] Exemplarily, adopt -tier Clos series of network topology structures as the preset network topology structure, such as Fat Tree and Spine Leaf.

[0125] Specifically, record the paths with the th different lengths in the network topology structure as , the path is one or more, the length of the path is set to , h is the total number of layers of switches, and the hash interval to be maintained on the path is denoted as , and the matching requirement of the hash interval is:[[]]

[0126] ;

[0127] where , j represents the jth layer switch in the h-layer switch; , k represents the kth layer switch in the h-layer switch.

[0128] Preferably, determine the hash interval matching requirements based on the network flow quantity threshold C and the preset network topology structure. The network flow quantity threshold C needs to satisfy:

[0129] ;

[0130] wherein, is the number of paths passing through the layer switch, and is the length of the hash interval . It can be understood that in actual operations, it is not necessary to calculate the specific value of the network quantity threshold C, but to use the network quantity threshold C as a medium to finally determine the length of the hash interval , so as to balance the network flow quantity to be measured on each switch and achieve load balancing at the flow level.

[0131] Specifically, in step S103, control the switch to record the traffic information of the allocated data packets, including:

[0132] Please refer to Figure 2 shown, and separately record the traffic information based on the two-layer collaborative sketch (TC Sketch). Among them, the two-layer collaborative sketch is configured on each switch including the ingress switch. The two-layer collaborative sketch includes two layers of buckets. The first layer of buckets B L includes to buckets, and the second layer of buckets B S includes to buckets, is less than ; each bucket in the first layer of buckets B L has d L cells, and each bucket in the second layer of buckets B S has d S cells. An example is given on the right side of Figure 2 with buckets and buckets . Among them, each cell has a fingerprint and a counter. The fingerprint is used to store the hash value, and the counter is used to count.

[0133] In the case where the network flow to be measured is the first network flow, record the traffic information of the data packets in the network flow to be measured based on the first layer of buckets B L ; in the case where the network flow to be measured is the second network flow, based on the second layer of buckets BS Record the traffic information of the data packets in the network flow to be measured.

[0134] Exemplarily, the following Algorithm 2 shows the algorithm content of TC Sketch in each switch:

[0135] Insertion() of TC Sketch

[0136] Input: Packet

[0137] Extract and from / / Extract the flow label and the element label and the flag bit from the data packet

[0138] Get the length of the path / / Get the length of the path of the path

[0139] Get the flow-level interval / / Get the flow-level matching interval

[0140] Get the packet-level interval / / Get the packet-level matching interval

[0141] if then / / Use when the data packet belongs to the second network flow

[0142] Calculate the value

[0143] if then

[0144] if

[0145] then

[0146] Insert into

[0147] else

[0148] Insert into

[0149] else / / Use when the data packet belongs to the first network flow

[0150] Calculate the value

[0151] if then

[0152] Insert into

[0153] Exemplarily, in step S104, the method for calculating the number of data packets in the network flow to be measured is as shown in Algorithm 3 below:

[0154] Query() of TC Sketch

[0155] Input: Flow

[0156] Calculate the value

[0157] / / The flow size is initially 0

[0158] for do / / Traverse the paths of the switch

[0159] Get the flow-level interval

[0160] if then / / Add the interval The recorded value

[0161] is the size queried in

[0162]

[0163] is the size queried in / / Add the interval The recorded value

[0164]

[0165] Return / / Return the final flow size

[0166] Specifically, before the measurement starts, measurement nodes are determined according to the network topology, including switches for measuring network flows, and corresponding algorithm modules are deployed on these switches.

[0167] Before the start of each measurement period, the network administrator needs to set the basic parameters of the measurement, such as the flow label or element label of the measurement object, or other traffic information that can characterize the characteristics of the network flow to be measured; since this solution uses a hash function, it is also necessary to calculate the intervals that need to be maintained on the switches according to the current network topology.

[0168] During each measurement period, when the ingress switch detects the arrival of a data packet, Algorithm 1 is used to determine whether the data packet belongs to the first network flow or the second network flow.

[0169] In the case where the network flow to which the data packet belongs is the first network flow, the data packet is marked, and each switch including the ingress switch in the first path measures and records the received data packet according to Algorithm 2.

[0170] In the case where the network flow to which the data packet belongs is the second network flow, the preset switches in the second path measure and record the received data packet according to Algorithm 2.

[0171] After the end of each measurement period, the administrator can collect traffic information from the TC Sketch in each of the above switches and calculate the size of any network flow according to Algorithm 3.

[0172] In the case where it is necessary to measure the network traffic carried by the data center over a period of time, the sizes of each network flow during this period are calculated separately, and the total of the sizes of each network flow can be used to obtain the network traffic carried by the data center during this period.

[0173] Please refer to Figure 3 As shown, based on the above lightweight collaborative network traffic measurement method for a data center network provided by the embodiments of the present invention, the embodiments of the present invention also provide a lightweight collaborative network traffic measurement system for a data center network, including:

[0174] A server that sends or receives the network flow to be measured;

[0175] A switch that forwards the network flow to be measured and processes the network flow to be measured;

[0176] A controller that determines whether the network flow to be measured is the first network flow or the second network flow, where the first network flow is larger than the second network flow;

[0177] The controller also evenly distributes the measurement task of the network flow to be measured to each switch in the first path when the network flow to be measured is the first network flow, where the first path is the transmission path of the network flow to be measured; when the network flow to be measured is the second network flow, the controller distributes the measurement task of the network flow to be measured to a preset switch, where the preset switch is any switch in the second path, and the second path is the transmission path of the network flow to be measured;

[0178] The controller also controls the switch to measure and record the traffic information of the network flow to be measured according to the assigned measurement task;

[0179] The controller also responds to the query instruction, obtains the traffic information, and calculates the number of data packets in the network flow to be measured.

[0180] The lightweight collaborative network traffic measurement system for a data center network provided by the embodiment of the present invention has the same beneficial effects as the above-mentioned lightweight collaborative network traffic measurement method for a data center network, which will not be elaborated here.

[0181] Exemplarily, the embodiment of the present invention also provides the experimental data of the above solution to verify its technical effects, which are as follows:

[0182] The real-world traffic data sets for experiments are respectively from CAIDA and IMC Data Center, using the source IP address in the data set as the flow label and the destination IP address as the element label; at the same time, two network topologies, Fat Tree and Spine Leaf, are respectively adopted.

[0183] Therefore, there are four experimental environments in the way of "network topology + traffic data set":

[0184] Fat Tree + CAIDA, Fat Tree + IMC, Spine Leaf + CAIDA, Spine Leaf + IMC.

[0185] In the above experimental environments, the embodiment of the present invention conducts a comparative experiment among 10 algorithms. The hash functions involved in the 10 algorithms all originate from MURMUR3, and the 10 algorithms are respectively:

[0186] LTCM, CMAX, UWRA, DS, WS, WS-NSPA, WS-FCM, CS, CS-NSPA, CS-FCM.

[0187] Among them, LTCM adopts the measurement method provided by the embodiments of the present invention. LTCM is the abbreviation of lightweight two-level collaborative measurement, indicating that the measurement method provided by the embodiments of the present invention is a lightweight two-level collaborative network traffic measurement method.

[0188] Specifically, FCM is the abbreviation of flow-level collaborative measurement framework and belongs to a general flow-level collaborative measurement framework; NSPA is the abbreviation of Network-based Sketching and Probing for Accurate flow measurement and also belongs to a measurement framework.

[0189] Integrate the two existing single-point measurement algorithms of CS (Chain Sketch) and WS (Waving Sketch) into FCM and NSPA respectively to obtain four measurement algorithms: WS-NSPA, WS-FCM, CS-NSPA, and CS-FCM.

[0190] Specifically, CMAX, UWRA, and DS are the abbreviations of CountMax (a lightweight collaborative sketch algorithm), Unit Weight Reduction Algorithm, and Distributed Sketch respectively, all of which are existing measurement algorithms and will not be elaborated in the embodiments of the present invention.

[0191] Please refer to Figures 4 to 7 As shown, the embodiments of the present invention have successively conducted comparative experiments on the maximum flow-level measurement load ratios of 10 algorithms in four experimental environments: Fat Tree + CAIDA, Fat Tree + IMC, Spine Leaf + CAIDA, and Spine Leaf + IMC.

[0192] Specifically, the maximum flow-level measurement load ratio represents the ratio of the maximum number of network flows measured by each switch of each of the 10 algorithms to the maximum number of network flows measured by each switch in the LTCM algorithm; in Figures 4 to 7Among them, the results of the maximum flow level measurement load ratio of the LTCM algorithm are all 1. The results of the maximum flow level measurement load ratio of the four algorithms of WS-NSPA, WS-FCM, CS-NSPA, and CS-FCM are relatively close to that of LTCM, but none of them is lower than 1. Therefore, the maximum flow level measurement load of the LTCM algorithm is the smallest, indicating that the measurement method provided by the embodiments of the present invention has high measurement accuracy.

[0193] Please refer to Figures 8 to 11 As shown, the embodiments of the present invention conducted comparative experiments on the standard deviation of the maximum packet level measurement load of each of the 10 algorithms in four experimental environments of Fat Tree + CAIDA, Fat Tree + IMC, Spine Leaf + CAIDA, and Spine Leaf + IMC in turn.

[0194] Specifically, the order of magnitude of the standard deviation of the maximum packet level measurement load is 10 6 , the smaller the standard deviation of the maximum packet level measurement load, the better the measurement load balancing effect of the algorithm. The standard deviation of the maximum packet level measurement load of the LTCM algorithm is the lowest in Figures 8 to 11 , indicating that the measurement load balancing effect of the LTCM algorithm among the 10 algorithms is the best, indicating that the measurement method provided by the embodiments of the present invention can achieve effective measurement load balancing.

[0195] Please refer to Figure 12 and Figure 13 As shown, the embodiments of the present invention conducted comparative experiments on the average throughput of each switch of the 10 algorithms in two experimental environments of Fat Tree + IMC and Spine Leaf + IMC in turn.

[0196] Specifically, the unit of the average throughput of the switch is Mpps, which reflects the number of millions of data packets that each switch of the 10 algorithms can process per second on average. In Figure 12 and Figure 13 , the average throughput of the switch of the LTCM algorithm is relatively high, indicating that the measurement method provided by the embodiments of the present invention has a high data packet processing speed.

[0197] Please refer to Figure 14 and Figure 15 As shown, the embodiments of the present invention conducted comparative experiments on the change of the average absolute error of each flow size measurement of the 10 algorithms with the memory size in two experimental environments of Fat Tree + CAIDA and Fat Tree + IMC in turn.

[0198] Specifically, the unit of memory is KB. Each flow size measurement refers to measuring the size of each network flow. The smaller the mean absolute error of each flow size measurement, the more accurate the measured network flow size. In Figure 14 and Figure 15 the mean absolute error of the LTCM algorithm in each flow size measurement with different memories is the smallest, indicating that the measurement method provided by the embodiments of the present invention has high measurement accuracy.

[0199] Please refer to Figure 16 and Figure 17 As shown, the embodiments of the present invention have successively conducted comparative experiments on the change of the F1 score of the first network flow detection of each of the 10 algorithms with the memory size in two experimental environments of Fat Tree + CAIDA and Fat Tree + IMC.

[0200] Specifically, the higher the F1 score of the first network flow detection, the more accurately the corresponding algorithm can detect the first network flow. In Figure 16 and Figure 17 the F1 score of the LTCM algorithm in the first network flow detection with different memories is the highest, indicating that the measurement method provided by the embodiments of the present invention can accurately detect the first network flow.

[0201] Please refer to Figure 18 and Figure 19 As shown, the embodiments of the present invention have successively conducted comparative experiments on the change of the mean absolute error of the first network flow detection of each of the 10 algorithms with the memory size in two experimental environments of Fat Tree + CAIDA and Fat Tree + IMC.

[0202] Specifically, the smaller the mean absolute error of the first network flow detection, the more accurately the corresponding algorithm can detect the first network flow. In Figure 18 and Figure 19 the mean absolute error of the LTCM algorithm in the first network flow detection with different memories is the smallest, indicating that the measurement method provided by the embodiments of the present invention can accurately detect the first network flow.

[0203] Please refer to Figure 20 and Figure 21 As shown, the embodiments of the present invention have successively conducted comparative experiments on the change of the F1 score of the first network flow detection of each of the 10 algorithms with the first network flow detection threshold in two experimental environments of Fat Tree + CAIDA and Fat Tree + IMC.

[0204] Specifically, in Figure 20 and Figure 21In the results, the F1 scores of the first network flow detection of the LTCM algorithm under different first network flow detection thresholds are the highest, indicating that the measurement method provided by the embodiment of the present invention can accurately detect the first network flow.

[0205] Please refer to Figure 22 and Figure 23 As shown, the present invention creates an embodiment of the invention and conducts comparative experiments in two experimental environments, Fat Tree + CAIDA and Fat Tree + IMC, on the change of the average absolute error of the first network flow detection of 10 algorithms with the first network flow detection threshold.

[0206] Specifically, in Figure 22 and Figure 23 In the figure, the average absolute error of the first network flow detection by the LTCM algorithm under different first network flow detection thresholds is the smallest, indicating that the measurement method provided by the embodiment of the present invention can accurately detect the first network flow.

[0207] In summary, the lightweight collaborative network traffic measurement method for data center networks provided by the embodiments of the present invention can achieve both high data packet processing efficiency and high overall measurement accuracy while measuring fine-grained network traffic in large data centers.

[0208] The present invention also provides a non-transitory machine-readable medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to execute the method of the present invention.

[0209] The present invention also provides a computer program product, including a computer program, wherein the computer program, when executed by a processor of a computer, is used to enable the computer to execute the above method provided by the present invention.

[0210] The present invention also provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor. The memory stores a computer program executable by the at least one processor, and the computer program is used to enable the electronic device to perform the method provided by the present invention.

[0211] refer to Figure 24, a block diagram of an electronic device of a server or a client that can be an embodiment of the present invention will now be described. It is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0212] As Figure 24 shown, the electronic device includes a computing unit 2401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 2402 or a computer program loaded from a storage unit 2408 into a random access memory (RAM) 2403. In the RAM 2403, various programs and data required for the operation of the electronic device can also be stored. The computing unit 2401, the ROM 2402, and the RAM 2403 are connected to each other through a bus 2404. An input / output (I / O) interface 2405 is also connected to the bus 2404.

[0213] Multiple components in the electronic device are connected to the I / O interface 2405, including: an input unit 2406, an output unit 2407, a storage unit 2408, and a communication unit 2409. The input unit 2406 can be any type of device that can input information into the electronic device. The input unit 2406 can receive input digital or character information, and generate key signal inputs related to the user settings and / or function controls of the electronic device. The output unit 2407 can be any type of device that can present information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 2408 can include but is not limited to a magnetic disk, an optical disk. The communication unit 2409 allows the electronic device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include but is not limited to a modem, a network card, an infrared communication device, and / or a wireless communication transceiver, such as a Bluetooth device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0214] The computing unit 2401 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 2401 include, but are not limited to, a CPU, a graphics processing unit (GPU), various special artificial intelligence (AI) computing units, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 2401 executes the various methods and processes described above. For example, in some embodiments, the method embodiments of the present invention can be implemented as a computer program tangibly embodied in a machine-readable medium, such as the storage unit 2408. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device via the ROM 2402 and / or the communication unit 2409. In some embodiments, the computing unit 2401 can be configured to execute the above-described method in any other suitable manner (e.g., by means of firmware).

[0215] The computer program for implementing the method of the embodiments of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to the processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor or controller, the functions / operations specified in the flowchart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0216] In the context of the embodiments of the present invention, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable signal medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, or infrared system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0217] It should be noted that the term "including" and its variants used in the embodiments of the present invention are open-ended, that is, "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "a plurality" mentioned in the embodiments of the present invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise clearly specified in the context, it should be understood as "one or more". The descriptions of terms such as "first" and "second" are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly specifying the quantity of the indicated technical features.

[0218] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the embodiments of the present invention are all information and data that have been authorized by the user or fully authorized by all parties. And the collection, use, and processing of relevant data need to comply with the relevant laws, regulations, and standards of the relevant countries and regions, and corresponding operation entrances are provided for users to choose to authorize or reject.

[0219] The steps described in the method embodiments provided by the embodiments of the present invention can be executed in different orders and / or executed in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The protection scope of the present invention is not limited in this regard.

[0220] The term "embodiment" in this specification means that the specific features, structures, or characteristics described in combination with the embodiment may be included in at least one embodiment of the present invention. The phrase appears in various positions in the specification does not necessarily mean the same embodiment, nor does it mean being independent or alternative to other embodiments and mutually exclusive. The various embodiments in this specification are all described in a related manner, and the same or similar parts between the various embodiments are referred to each other. In particular, for device, equipment, and system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts refer to the partial description of the method embodiments.

[0221] The above-described embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it cannot be understood as a limitation of the protection scope. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the appended claims.

Claims

1. A lightweight collaborative network traffic measurement method for data center networks, characterized in that: include: Determine that the network flow to be tested is a first network flow or a second network flow, wherein the first network flow is greater than the second network flow; In a case where the network flow to be measured is the first network flow, evenly distributing the measurement task of the network flow to be measured to each switch in a first path, wherein the first path is a transmission path of the network flow to be measured; In the case where the network flow to be measured is the second network flow, a hash interval matching requirement is determined based on a preset network topology structure, and a measurement task of the network flow to be measured is assigned to a preset switch according to the hash interval matching requirement and the network topology structure, wherein the preset switch is any switch in the second path, the second path is the transmission path of the network flow to be measured, and the preset switch is any switch in the second path in the network topology structure. Paths of different lengths are denoted as ,path One or more paths The length is set to , h is the total number of layers of switches, path The hash interval that needs to be maintained is recorded as , hash interval The matching requirements are: ； in, , j represents the j-th layer switch in the h-layer switches; , k represents the k-th layer switch in the h-layer switches; Controlling the switch to measure and record the flow information of the network flow to be measured according to the assigned measurement task; In response to the query instruction, the flow information is obtained, and the number of data packets in the network flow to be tested is calculated.

2. The lightweight collaborative network traffic measurement method for data center networks according to claim 1 is characterized in that: Determining whether the network flow to be tested is the first network flow or the second network flow includes: The detector based on the ingress switch calculates the hash value of the data packet in the network flow to be tested, and stores the hash value in the corresponding bucket; When the same data packet appears in the network flow to be tested, control the counter in the bucket corresponding to the first hash value to increase by 1, wherein the first hash value is the same hash value of the same data packet, wherein the identification fields of the same data packets are the same; When the hash values ​​of different data packets in the network flow to be tested are the same, a hash conflict occurs, and a counter in a bucket corresponding to a second hash value is controlled to be reduced by 1, wherein the second hash value is the same hash value of the different data packets, wherein the identification fields of the different data packets are different; Wherein, when the value of any of the counters is 0, the network flow to be tested is determined to be the first network flow; when the value of none of the counters is 0, the network flow to be tested is determined to be the second network flow.

3. The lightweight collaborative network traffic measurement method for data center networks according to claim 2 is characterized in that: After determining that the network flow to be tested is the first network flow, the method further includes: Storing the hash value of the network flow to be tested into a target bucket, wherein the target bucket is a bucket whose counter value is 0; Reset the value of the counter of the target bucket to 1.

4. The lightweight collaborative network traffic measurement method for data center networks according to claim 2 is characterized in that: Determining whether the network flow to be tested is the first network flow or the second network flow also includes: Before the detector based on the ingress switch calculates the hash value of the data packet in the network flow to be tested, the data packet in the network flow to be tested is sampled according to a preset probability; The detector based on the ingress switch calculates the hash value of the sampled data packet, and stores the hash value of the sampled data packet in a corresponding bucket.

5. The lightweight collaborative network traffic measurement method for data center networks according to claim 1 is characterized in that: Determine hash interval matching requirements based on the preset network topology, including: The hash interval matching requirement is determined based on the network flow quantity threshold C and the preset network topology structure. The network flow quantity threshold C needs to meet the following requirements: ； in, For the ( ) The path through the layer switch The number of For the hash interval Length; According to the network flow quantity threshold C, the hash interval matching requirement and the network topology structure, the measurement task of the network flow to be measured is allocated to the preset switch.

6. The lightweight collaborative network traffic measurement method for data center networks according to claim 1 is characterized in that: Controlling the switch to record the flow information of the allocated data packet, including: The flow information is recorded separately based on a two-layer collaboration sketch, wherein the two-layer collaboration sketch is configured on each of the switches, and the two-layer collaboration sketch includes two layers of buckets, a first layer bucket B L include Buckets, second layer bucket B S include Buckets, Less than ; When the network flow to be tested is the first network flow, based on the first layer bucket B L Record the flow information of the data packets in the network flow to be tested; if the network flow to be tested is the second network flow, based on the second layer bucket B S The flow information of the data packets in the network flow to be tested is recorded.

7. The lightweight collaborative network traffic measurement method for data center networks according to claim 1 is characterized in that: The traffic information is a flow label or an element label.

8. A lightweight collaborative network traffic measurement system for data center networks, characterized in that: include: Server, sends or receives the network flow to be tested; The switch forwards the network flow to be tested and processes the network flow to be tested; The controller determines that the network flow to be tested is a first network flow or a second network flow, wherein the first network flow is greater than the second network flow; The controller, further, when the network flow to be tested is the first network flow, uniformly distributes the measurement task of the network flow to be tested to each switch in the first path, wherein the first path is the transmission path of the network flow to be tested; when the network flow to be tested is the second network flow, determines the hash interval matching requirement based on a preset network topology structure, and distributes the measurement task of the network flow to be tested to the preset switches according to the hash interval matching requirement and the network topology structure, wherein the preset switch is any switch in the second path, the second path is the transmission path of the network flow to be tested, and ... Paths of different lengths are denoted as ,path One or more paths The length is set to , h is the total number of layers of switches, path The hash interval that needs to be maintained is recorded as , hash interval The matching requirements are: ； in, , j represents the j-th layer switch in the h-layer switches; , k represents the k-th layer switch in the h-layer switches; The controller further controls the switch to measure and record the flow information of the network flow to be measured according to the measurement task assigned to it; The controller also responds to the query instruction, obtains the flow information, and calculates the number of data packets in the network flow to be tested.

9. An electronic device, comprising: A processor, and a memory for storing a program, characterized in that the program includes instructions, which, when executed by the processor, cause the processor to execute a lightweight collaborative network traffic measurement method for a data center network according to any one of claims 1 to 7.

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