Network measurement method and apparatus, electronic device, storage medium, and computer program product

Abstract

The present disclosure relates to a network measurement method and device, electronic equipment, storage medium and computer program product. The network measurement method comprises: receiving viewing quality degradation information sent by a client; based on the viewing quality degradation information, obtaining a network degradation time interval of a target network; using a congestion control model, obtaining network state information of the target network with a round-trip time of a data packet as a granularity, and obtaining data packet transmission information of the target network with the round-trip time of the data packet as the granularity by monitoring the target network; determining the network state information and the data packet transmission information within the network degradation time interval as original information of a weak network part of the target network; extracting a plurality of key features with a first predetermined time length as a granularity from the original information of the weak network part and determining the plurality of key features as network measurement data of the weak network part, wherein the key feature refers to a feature representing real-time quality of the weak network part.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of communication, and in particular, to a network measurement method and device, electronic equipment, storage medium and computer program product. BACKGROUND

[0002] In recent years, with the development of large-scale media services (such as short videos, long videos and live broadcasts, etc.), smooth and clear video viewing experience is becoming increasingly important. In the process of users watching videos, stuttering, long first screen and low definition, etc. can all cause the user's viewing experience to decline, affecting the user's viewing interest, thereby reducing the user's viewing time. The degradation of the network in which the user is located is the most core factor causing the degradation of the user experience, so studying the countermeasures under weak networks is a core topic that the industry is increasingly concerned about.

[0003] Now the network infrastructure has developed a lot, and the network speed has gradually increased, but the network also has a lot of fluctuations. In order to counteract the fluctuations of the network, the industry uses adaptive multi-code rate algorithms, congestion control algorithms, preloading algorithms, etc. to counteract the fluctuations of the network. However, the fluctuations of the network are diverse, and the increase of the underlying network delay, the decrease of the bandwidth and the increase of the packet loss will all cause the user application layer available bandwidth to decrease. If the changes of the weak network can be quantitatively expressed, and a rich online real weak network database can be established locally, this will greatly improve the research and development efficiency of the countermeasures against weak network algorithms, and greatly reduce the trial and error cost.

[0004] At present, the industry and academia use network measurement and network loss instruments to record and replay weak networks. The existing network measurement methods mainly include bandwidth measurement, delay measurement, maximum transmission unit (MTU) measurement and reachability measurement, etc. However, a relatively dense network measurement will introduce a large amount of additional bandwidth cost, and at the same time, it may cause a destructive impact on the user's own experience; a relatively sparse network measurement cannot reflect the changes of the real network. SUMMARY

[0005] The present disclosure provides a network measurement method and device, electronic equipment, storage medium and computer program product to at least solve the problem of high bandwidth cost or inability to reflect the changes of the real network in related technologies.

[0006] According to a first aspect of the embodiments of the present disclosure, a network measurement method is provided, comprising: receiving viewing quality degradation information sent by a client; obtaining a network degradation time interval of a target network based on the viewing quality degradation information; obtaining network state information of the target network with a round-trip time of a data packet as a granularity by using a congestion control model, and obtaining data packet transmission information of the target network with the round-trip time of the data packet as the granularity by monitoring the target network; determining the network state information and the data packet transmission information within the network degradation time interval as original information of a weak network part of the target network; extracting a plurality of key features with a first predetermined time length as a granularity from the original information of the weak network part and determining the plurality of key features as network measurement data of the weak network part, wherein the key features refer to features representing real-time quality of the weak network part.

[0007] Optionally, the obtaining of the network degradation time interval of the target network based on the viewing quality degradation information comprises: obtaining a degradation start time at which the viewing quality degradation occurs at the client and a start download time of a video part at which the viewing quality degradation occurs at the client based on the viewing quality degradation information; determining a degradation start time at which the viewing quality degradation occurs at the server and a start download time of the video part at the server based on the first time, the second time, the degradation start time at the client and the start download time at the client, wherein the first time is a time at which a transmission layer in communication with the client receives the viewing quality degradation information, and the second time is a time at which the server receives the viewing quality degradation information; and determining the network degradation time interval of the target network with the start download time at the server and the degradation start time at the server as boundaries.

[0008] Optionally, the determining of the network state information and the data packet transmission information within the network degradation time interval as the original information of the weak network part of the target network comprises: expanding the network degradation time interval based on a second predetermined time length; and determining the network state information and the data packet transmission information within the expanded network degradation time interval as the original information of the weak network part of the target network.

[0009] Optionally, in a case where the key features include a physical propagation delay, a bottleneck routing bandwidth, a random packet loss rate and a bottleneck routing maximum queue depth, the extracting of the plurality of key features with the first predetermined time length as the granularity from the original information of the weak network part comprises: taking the physical propagation delay and the physical bandwidth in the original information as the physical propagation delay and the bottleneck routing bandwidth of the weak network part, respectively; determining a correlation degree of an observed round-trip delay and an observed packet loss rate in the original information, and determining the random packet loss rate and the bottleneck routing maximum queue depth of the weak network part based on the correlation degree; and sampling the physical propagation delay, the bottleneck routing bandwidth, the random packet loss rate and the bottleneck routing maximum queue depth with the first predetermined time length as the granularity to obtain a plurality of key features of each kind of key feature.

[0010] Optionally, the determining the random packet loss rate and the maximum queue depth of the bottleneck router of the weak network part based on the correlation degree comprises: in response to the correlation degree being less than a first threshold, taking an average value of the observed packet loss rates in all the observed round-trip time intervals as the random packet loss rate of the weak network part; in response to the correlation degree being greater than a second threshold, taking an average observed packet loss rate corresponding to a predetermined observed round-trip time interval as the random packet loss rate of the weak network part, wherein the predetermined observed round-trip time interval is an interval with the minimum average observed round-trip time; and in response to the correlation degree being greater than a third threshold, determining the maximum queue depth of the bottleneck router based on the maximum observed round-trip time, the bandwidth of the bottleneck router and the physical propagation delay.

[0011] Optionally, the determining the correlation degree between the observed round-trip time and the observed packet loss rate in the original information comprises: partitioning the observed round-trip time in the original information to obtain a plurality of observed round-trip time intervals; for each observed round-trip time interval, obtaining an average value of the observed packet loss rate and an average value of the observed round-trip time; and determining the correlation degree between the observed round-trip time and the observed packet loss rate based on a Spearman correlation coefficient of the average value of the observed packet loss rate and the average value of the observed round-trip time of each observed round-trip time interval.

[0012] According to a second aspect of the embodiments of the present disclosure, a network measurement device is provided, comprising: a receiving unit configured to receive viewing quality degradation information sent by a client; a first obtaining unit configured to obtain a network degradation time interval of a target network based on the viewing quality degradation information; a second obtaining unit configured to obtain network state information of the target network in the granularity of round-trip time of a data packet by using a congestion control model, and obtain data packet transmission information of the target network in the granularity of round-trip time of a data packet by monitoring the target network; a determining unit configured to determine the network state information and the data packet transmission information in the network degradation time interval as original information of a weak network part of the target network; and an extracting unit configured to extract a plurality of key features in the granularity of a first predetermined time length from the original information of the weak network part, and determine the plurality of key features as network measurement data of the weak network part, wherein the key features refer to features representing real-time quality of the weak network part.

[0013] Optionally, the first obtaining unit is further configured to obtain, based on the viewing quality degradation information, a degradation start time at which the viewing quality degradation occurs at the client and a start download time of the video part at which the viewing quality degradation occurs at the client; and determine, based on the first time, the second time, the degradation start time at the client and the start download time, a degradation start time at which the viewing quality degradation occurs at the server and a start download time of the video part at the server, wherein the first time is a time at which a transmission layer in communication with the client receives the viewing quality degradation information, and the second time is a time at which the server receives the viewing quality degradation information; and determine, with the start download time and the degradation start time at the server as boundaries, a network degradation time interval of the target network.

[0014] Optionally, the determining unit is further configured to expand the network degradation time interval based on a second predetermined length of time; and determine, as original information of the weak network part of the target network, network status information and data packet transmission information within the expanded network degradation time interval.

[0015] Optionally, in a case where the key features include a physical propagation delay, a bottleneck routing bandwidth, a random packet loss rate and a bottleneck routing maximum queue depth, the extracting unit is further configured to take, as the physical propagation delay and the bottleneck routing bandwidth of the weak network part, the physical propagation delay and the physical bandwidth in the original information respectively; determine a correlation between the observed round-trip delay and the observed packet loss rate in the original information, and determine, based on the correlation, the random packet loss rate and the bottleneck routing maximum queue depth of the weak network part; and sample the physical propagation delay, the bottleneck routing bandwidth, the random packet loss rate and the bottleneck routing maximum queue depth respectively with a first predetermined length of time as a granularity to obtain a plurality of key features of each kind of key feature.

[0016] Optionally, the extracting unit is further configured to, in response to the correlation being less than a first threshold value, take an average of the observed packet loss rates of all the observed round-trip delay intervals as the random packet loss rate of the weak network part; in response to the correlation being greater than a second threshold value, take an average observed packet loss rate corresponding to a predetermined observed round-trip delay interval as the random packet loss rate of the weak network part, wherein the predetermined observed round-trip delay interval is an interval with a minimum average observed round-trip delay; and in response to the correlation being greater than a third threshold value, determine the bottleneck routing maximum queue depth based on the maximum observed round-trip delay, the bottleneck routing bandwidth and the physical propagation delay.

[0017] Optionally, the extracting unit is further configured to divide the observed round-trip delays in the original information into a plurality of observed round-trip delay intervals; for each observed round-trip delay interval, obtain an observed packet loss rate average and an observed round-trip delay average; and determine, based on a Spearman correlation coefficient of the observed packet loss rate average and the observed round-trip delay average of each observed round-trip delay interval, the correlation between the observed round-trip delay and the observed packet loss rate.

[0018] According to a third aspect of the embodiments of the present disclosure, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the network measurement method of the present disclosure.

[0019] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, when the instructions in the computer-readable storage medium are executed by at least one processor, the at least one processor is caused to perform the network measurement method of the present disclosure.

[0020] According to a fifth aspect of the embodiments of the present disclosure, a computer program product is provided, comprising computer instructions, when the computer instructions are executed by a processor, the network measurement method of the present disclosure is implemented.

[0021] The embodiments of the present disclosure provide at least the following beneficial effects:

[0022] According to the network measurement method and device, electronic device, storage medium and computer program product of the present disclosure, when the client occurs viewing quality degradation, the viewing quality degradation information is sent to the server, that is, the client of the present disclosure can feed back the information of viewing quality degradation in real time, and after the server receives the information, the network degradation time interval can be known according to the viewing quality degradation information, and then the network measurement data of the weak network part of the real network is obtained by combining the monitored data packet transmission information and the network state information output by the congestion control model, which is used to accelerate the iteration of the local algorithm and improve the online user quality. Therefore, the present disclosure solves the problems of high bandwidth cost or unable to reflect the real network change in the related art.

[0023] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0024] The accompanying drawings incorporated in the specification and forming a part of it, illustrate embodiments consistent with the present disclosure, and together with the description, serve to explain the principles of the present disclosure, and do not constitute an undue limitation on the present disclosure.

[0025] Figure 1 is an implementation scenario schematic diagram of a network measurement method according to an exemplary embodiment of the present disclosure;

[0026] Figure 2 is a weak network measurement and full link optimization framework schematic diagram according to an exemplary embodiment of the present disclosure;

[0027] Figure 3 is a flowchart of a network measurement method according to an exemplary embodiment of the present disclosure;

[0028] Figure 4 is a block diagram of a network measurement device according to an example embodiment of the present disclosure;

[0029] Figure 5 is a block diagram of an electronic device 500 according to an example embodiment of the present disclosure. DETAILED DESCRIPTION

[0030] In order for those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be described clearly and completely below with reference to the drawings.

[0031] It should be noted that the terms "first", "second", and the like in the specification and claims of the present disclosure and the above-described drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following embodiments do not represent all embodiments consistent with the present disclosure. Rather, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0032] It should be noted herein that "at least one of a plurality of items" appearing in the present disclosure means that the three types of alternatives are included: "any one of the plurality of items", "a combination of any two or more of the plurality of items", and "all of the plurality of items". For example, "including at least one of A and B" includes the following three alternatives: (1) including A; (2) including B; and (3) including A and B. For another example, "performing at least one of step one and step two" means the following three alternatives: (1) performing step one; (2) performing step two; and (3) performing step one and step two.

[0033] Currently, network measurement mainly includes bandwidth measurement, delay measurement, maximum transmission unit (Maximum Transmission Unit, abbreviated as MTU) measurement, and reachability measurement, and the following will briefly introduce two network measurement methods:

[0034] 1. The network bandwidth measurement method based on PathLoad is a typical method of active network measurement, that is, a packet sequence is actively sent at a fixed rate, the arrival time and reception time of the packet are recorded, and the arrival trend of the packet interval is analyzed. If the packet arrival interval has an increasing trend, it means that the sending rate is greater than the link bandwidth, and the receiving rate at this time is calculated as the link bandwidth; if the packet arrival interval has no increasing trend, the sending rate is increased using the bisection method until the packet arrival interval is observed to have an increasing trend.

[0035] However, the network bandwidth measurement method based on PathLoad has the following three disadvantages: 1) PathLoad needs to actively send packets for network measurement, which will introduce a huge bandwidth cost in the actual deployment of online streaming applications; 2) PathLoad uses a bisection method to detect bandwidth, and the selection of the bisection boundary greatly affects the detection speed, and the bandwidth measurement efficiency is low; 3) PathLoad can only reflect the network bandwidth level when it is detected, and cannot reflect other dimensions of network characteristics, such as delay, random packet loss, queue depth, etc., and also cannot reflect the network characteristics when the quality of the streaming application is degraded.

[0036] In particular, in recent years, there have been many variant algorithms that use packet pairs and packet sequences to measure bandwidth, but they introduce more bandwidth costs and cannot observe multiple dimensions of network characteristics at the same time. Here, PathLoad is only listed as a typical algorithm.

[0037] 2) The network measurement method based on ICMP packets is a typical method for measuring reachability, packet loss and delay, that is, the client uses the ping command to periodically send ICMP packets to measure the average round-trip delay and jitter level, and to observe the packet loss rate and network reachability within a period of time.

[0038] However, the network measurement method based on ICMP packets has the following two disadvantages: 1) The delay measurement using IMCP packets is generally sparse, with a typical interval of 1000ms, and if it is more intensive, it cannot reflect the true level of physical delay; 2) The measurement using ICMP packets cannot reflect the bandwidth level of the network, and it also cannot reflect the network characteristics when the quality of the streaming application is degraded.

[0039] In order to solve the problems of high bandwidth cost, slow measurement speed and incomplete reflection of network characteristics of related network measurement methods, the present disclosure obtains network measurement data that conforms to the weak network part of the real user network by applying the real-time feedback of software (such as the streaming application on the client) and combining the congestion control model and the information required by the network monitoring real-time statistics, which is used to accelerate the iteration of the local algorithm and improve the quality of use of online users.

[0040] In the following, the network measurement method and device, electronic equipment, storage medium and computer program product according to the example embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

[0041] Figure 1 is a schematic diagram of an implementation scenario of a network measurement method according to an example embodiment of the present disclosure, as Figure 1The implementation scenario includes a server 100, a user terminal 110, and a user terminal 120. The user terminal is not limited to two and includes, but is not limited to, a mobile phone, a personal computer, and the like. The user terminal can install a streaming media application software. The server can be one server, a server cluster composed of a plurality of servers, a cloud computing platform, or a virtualization center.

[0042] After the user terminals 110 and 120 access the target network, the user can watch a video through the streaming media application software on the user terminal 110 or 120. If the watching quality deteriorates during the video watching process, the user terminal 110 or 120 sends watching quality deterioration information to the server 100. After the server 100 receives the watching quality deterioration information sent by the user terminals 110 and 120, the server 100 obtains a network deterioration time interval of the target network based on the watching quality deterioration information, obtains network state information of the target network with the round-trip time of a data packet as the granularity by using a congestion control model, and obtains data packet transmission information of the target network with the round-trip time of a data packet as the granularity by monitoring the target network. The network state information and the data packet transmission information in the network deterioration time interval are determined as original information of a weak network part of the target network. Then, a plurality of key features with a first predetermined time length as the granularity are extracted from the original information of the weak network part, and the plurality of key features are determined as network measurement data of the weak network part. The key feature refers to a feature representing the real-time quality of the weak network part.

[0043] After obtaining the network measurement data of the weak network part, a large-scale online real user weak network database can be established by using the network measurement data, and accelerated simulation playback can be performed locally, thereby greatly improving the research and development efficiency of weak network countermeasures. Figure 2 The weak network measurement and full-link optimization framework in the disclosure is shown. When the user watches a video by using a streaming media application software and the watching quality deteriorates, a network measurement unit records network conditions in real time, that is, network measurement data, and then performs weak network playback and evaluation offline to form a weak network database and provide support for offline strategy optimization.

[0044] Figure 3 FIG. 1 is a flowchart of a network measurement method according to an example embodiment of the disclosure, as shown in FIG. 1. Figure 3 The network measurement method includes the following steps:

[0045] In step S301, watching quality deterioration information sent by a client is received.

[0046] As an example, the client runs an application software (such as a streaming media application software) by using a target network. When it is monitored that the application software has watching quality deterioration, the client can send watching quality deterioration information to a server.

[0047] As an example, taking the watching quality degradation as the watching video block as an example, at this time, the watching quality degradation information (QoS info ) can include but is not limited to: the watching video block duration (block dur ), the watching video block times (block count ), the watching video block time (block ts ), the application layer video bitrate, the start download time (download start ) of the video part where the block is located.

[0048] It should be noted that the watching quality degradation is not limited to the watching video block, but can also be various types of streaming watching quality degradation, such as watching video resolution degradation, watching video first screen duration, etc. The present disclosure only exemplarily illustrates the watching video block.

[0049] In step S302, based on the watching quality degradation information, the network degradation time interval of the target network is obtained.

[0050] As an example, based on the time information corresponding to the block in the watching quality degradation information sent by the client, the corresponding time information of the server can be obtained, and then based on the corresponding time information of the server, the network degradation time interval can be determined.

[0051] According to the exemplary embodiments of the present disclosure, the network degradation time interval of the target network can be determined by the following specific steps: based on the watching quality degradation information, the degradation start time when the watching quality degradation occurs at the client and the start download time of the video part where the watching quality degradation occurs at the client are obtained; based on the first time, the second time, the degradation start time and the start download time of the client, the degradation start time when the watching quality degradation occurs at the server and the start download time of the video part at the server are determined, wherein the first time is the time when the transmission layer communicating with the client receives the watching quality degradation information, and the second time is the time when the server receives the watching quality degradation information; taking the start download time and the degradation start time of the server as boundaries, the network degradation time interval of the target network is determined.

[0052] Through the present embodiment, based on the time when the transmission layer communicating with the client receives the watching quality degradation information and the time when the server receives the watching quality degradation information, the time of the server and the client is aligned, so that based on the aligned time, the server can determine a relatively accurate network degradation time interval.

[0053] As an example, the present disclosure connects the transport layer and the application software of the client, when the application software has viewing quality degradation, the application software transmits the viewing quality degradation information to the connected transport layer, and the transport layer transmits the viewing quality degradation information to the server through the streaming media information frame. The server receives the viewing quality degradation information, starts the network measurement process, and records the measured information to the server locally, and transmits the network measurement record information to the weak network database for storage in the non-network peak period.

[0054] As an example, when the application software has viewing quality degradation, the viewing quality degradation information QoS info is transmitted to the transport layer, when the client transport layer receives the viewing quality degradation information QoS info , records the time t c , and the transport layer combines t c and QoS info into a streaming media information frame and sends it to the server. When the server receives the viewing quality degradation information QoS info , records the time t s , then analyzes the timestamp block ts and download start of the client in QoS info , and further converts them into the corresponding timestamps of the server according to the following formula:

[0055] block tsserver = block ts -t c +t s -srtt

[0056] download start = download c -t s +t s -srtt

[0057] Where srtt is the smooth rtt calculated by the server, which is not discussed in the present disclosure.

[0058] After obtaining the corresponding timestamps block ts_server and download start_server of the server, the network degradation time interval [download start_server , block ts_server ] can be obtained.

[0059] In step S303, the network state information of the target network is obtained by using the congestion control model, and the data packet transmission information of the target network is obtained by monitoring the target network.

[0060] As an example, an acknowledgement message of a data packet transmitted through the target network can be obtained, the acknowledgement message is input into the congestion control model, and then network state information corresponding to a round-trip time of the data packet of the target network, such as a physical propagation delay and a physical bandwidth, can be obtained, and the present disclosure is not limited thereto.

[0061] As an example, during operation of the target network, the target network can be monitored, and corresponding data packet transmission information, such as an observed round-trip delay and an observed packet loss rate, can be observed, and the present disclosure is not limited thereto.

[0062] As an example, the server can record network state information and data packet transmission information with a granularity of a round-trip delay (RTT) for at most T seconds (such as 600 seconds), wherein the network state information can include a physical bandwidth bw i output by the congestion control model, and a physical propagation delay d i output by the congestion control model; and the data packet transmission information can include an observed packet loss rate l i , and a current observed round-trip delay rtt i , wherein i represents the i-th RTT.

[0063] In step S304, the network state information and the data packet transmission information in the network degradation time interval are determined as original information of the weak network part of the target network.

[0064] As an example, the network state information and the data packet transmission information in the network degradation time interval can be obtained, and these network state information and data packet transmission information can be stored in a separate local file on the server, a corresponding file name can use an md5 code of the information, and the file can be used as original information of the weak network part, which is equivalent to the original information of the weak network part.

[0065] According to an example embodiment of the present disclosure, the network state information and the data packet transmission information in the network degradation time interval can be determined as original information of the weak network part of the target network by the following specific steps: expanding the network degradation time interval based on a second predetermined length; and determining the network state information and the data packet transmission information in the expanded network degradation time interval as original information of the weak network part of the target network. Through this embodiment, the network degradation time interval is expanded to avoid not completely covering the time of network degradation.

[0066] As an example, after obtaining the network degradation time interval [download start_server , block ts_server ], the network degradation time interval can be expanded by a second predetermined length to obtain an expanded network degradation time interval t i :

[0067] t i ∈[download start_server -α×srtt,block ts_server +α×srtt]

[0068] Wherein, α×srtt is the second predetermined time length, which can be between 30 seconds to 40 seconds, and the present disclosure does not limit it.

[0069] Then, the network state information and packet transmission information within the above t i Can be obtained, and these network state information and packet transmission information are stored in a separate local file on the server, and the corresponding file name can use the md5 code of the information, and the file is used as the original file of the weak network part, which is equivalent to the original information of the weak network part.

[0070] In step S305, a plurality of key features with a first predetermined time length as a granularity are extracted from the original information of the weak network part, and the plurality of key features are determined as network measurement data of the weak network part, wherein the key feature refers to a feature representing the real-time quality of the weak network part.

[0071] As an example, the present embodiment can extract network measurement data that can be used for local weak network restoration from the above original information with a first predetermined time length as a granularity, to improve the iteration efficiency of the local algorithm strategy. The above first predetermined time length is set according to the need, which can be set to 1 millisecond.

[0072] As an example, in order to obtain relatively accurate network measurement data of the weak network part, the above key features include but are not limited to: random packet loss rate l r , physical propagation delay d p , bottleneck routing bandwidth b, bottleneck routing maximum queue depth d q . The present embodiment extracts bottleneck routing bandwidth b and bottleneck routing maximum queue depth d q with 1 millisecond as a granularity, while random packet loss rate l r , physical propagation delay d p can still be in the granularity of RTT, and the network measurement data within one RTT is recorded in the following form as the corresponding weak network trace for subsequent simulation and restoration of local weak network:

[0073] <l r ,d p ,<d q1 ,b1>,<d q2 ,b2>…<d qn ,b n >>

[0074] It should be noted that, assuming there are multiple network degradation time intervals, if the interval between two network degradation time intervals does not exceed a predetermined time (such as 30 seconds), the network measurement data of the two network degradation time intervals can be merged together as a weak network trajectory.

[0075] According to an exemplary embodiment of this disclosure, when the key features include physical propagation delay, bottleneck routing bandwidth, random packet loss rate, and maximum bottleneck routing queue depth, extracting multiple key features with a first predetermined duration as the granularity from the original information of the weak network portion may include: using the physical propagation delay and physical bandwidth in the original information as the physical propagation delay and bottleneck routing bandwidth of the weak network portion, respectively; determining the correlation between the observed round-trip delay and the observed packet loss rate in the original information, and determining the random packet loss rate and maximum bottleneck routing queue depth of the weak network portion based on the correlation; and sampling the physical propagation delay, bottleneck routing bandwidth, random packet loss rate, and maximum bottleneck routing queue depth with a first predetermined duration as the granularity to obtain multiple key features for each key feature.

[0076] This embodiment utilizes the four key features described above to effectively describe the actual quality of weak network segments, enabling the subsequent use of network measurement data from these weak network segments to improve the iterative efficiency of local algorithm strategies.

[0077] As an example, this embodiment directly uses the physical propagation delay d output in real time by the congestion control model as the physical propagation delay d. p And directly use the physical bandwidth bw output in real time from the congestion control model. i The bottleneck routing bandwidth is b. The random packet loss rate and the maximum queue depth of the bottleneck route can be determined based on the correlation between the observed round-trip delay and the observed packet loss rate in the original information. After obtaining these four key features, sampling can be performed for a first predetermined duration as needed to obtain multiple key features that meet the requirements.

[0078] As an example, the congestion control model mentioned above can adopt BBR (Bottleneck Bandwidth and Round-trip Propagation Time) V3, or other congestion control models. For example, the bandwidth detection speed can be improved by improving the bandwidth detection mechanism of the congestion control model. Even when the network bandwidth changes significantly and the application layer data is limited, the bandwidth can still be measured relatively accurately, and more accurate raw network measurement data will be obtained. This disclosure does not limit the scope of the congestion control model.

[0079] It should be noted that the key features are not limited to physical propagation delay, bottleneck route bandwidth, random packet loss rate, and maximum bottleneck route queue depth; they can also be other features, which are not limited in this disclosure.

[0080] According to the example embodiment of the present disclosure, based on the correlation, determining the random packet loss rate of the weak network part and the maximum queue depth of the bottleneck route can comprise: in response to the correlation being less than a first threshold, taking the average of all observed packet loss rates in the observed round-trip time interval as the random packet loss rate of the weak network part; in response to the correlation being greater than a second threshold, taking the average observed packet loss rate corresponding to a predetermined observed round-trip time interval as the random packet loss rate of the weak network part, wherein the predetermined observed round-trip time interval is the interval with the minimum average observed round-trip time; and in response to the correlation being greater than a third threshold, determining the maximum queue depth of the bottleneck route based on the maximum observed round-trip time, the bandwidth of the bottleneck route and the physical propagation delay. Through this embodiment, based on the similarity information, the relatively accurate random packet loss rate and the maximum queue depth of the bottleneck route can be determined.

[0081] As an example, the first threshold, the second threshold and the third threshold can be set as needed, and the present disclosure is not limited in this regard.

[0082] As an example, taking the first threshold as 0.25 and the second threshold as 0.75, if the correlation is less than 0.25, the average of all observed packet loss rates is calculated as the random packet loss rate l r of the weak network part; and if the correlation is greater than 0.75, the interval with the minimum average of the observed round-trip time is determined, and the average of the observed packet loss rate in this interval is taken as the random packet loss rate l r of the weak network part.

[0083] As an example, taking the third threshold as 0.75, if the correlation is greater than 0.75, the maximum round-trip time rtt max can be determined as follows to obtain the maximum queue depth d q of the bottleneck route:

[0084] d q = (rtt max -d p ) x b

[0085] According to the example embodiment of the present disclosure, determining the correlation between the observed round-trip time and the observed packet loss rate in the original information can comprise: partitioning the observed round-trip time in the original information to obtain a plurality of observed round-trip time intervals; for each observed round-trip time interval, obtaining the average of the observed packet loss rate and the average of the observed round-trip time; and based on the Spearman correlation coefficient of the average of the observed packet loss rate and the average of the observed round-trip time in each observed round-trip time interval, determining the correlation between the observed round-trip time and the observed packet loss rate. Through this embodiment, by partitioning and the average of the observed packet loss rate and the average of the observed round-trip time in each interval, the calculation of the similarity can reduce the calculation complexity and also ensure the accuracy.

[0086] As an example, taking the network state information including physical propagation delay and physical bandwidth, and the packet transmission information including observed round-trip delay and observed packet loss rate as an example, for each piece of original information, the observed packet loss rate l i and the observed round-trip delay rtt i form an observation pair <l i ,rtt i >, and the maximum rtt i and the minimum rtt max of the observed round-trip delay are calculated. min The observed round-trip delay is evenly divided into n intervals (k ∈ [1, n]) according to the following rules.

[0087] [rtt min +(k-1)*(rtt max -rtt min ) / n,rtt min +k*(rtt max -rtt min ) / n]

[0088] For each observed round-trip delay interval, the average of the internal observed packet loss rate and the average of the observed round-trip delay are calculated to obtain <l k ,rtt k >, where k represents the kth observed round-trip delay interval. Based on the average of the observed packet loss rate and the average of the observed round-trip delay of each interval, the Spearman correlation coefficient is calculated to determine the correlation degree of the observed round-trip delay and the observed packet loss rate.

[0089] In summary, the present disclosure obtains the original information of the weak network part in the network by applying the real-time feedback of the software (i.e., the client), combining the congestion control model and the network monitoring real-time statistics required information, and effectively converts the original data into the weak network trace while removing the noise data, so as to obtain the weak network large database conforming to the real user network, so that it is possible to measure the multi-dimensional network characteristics without introducing additional bandwidth, thereby making it possible to be landed in large-scale industry.

[0090] The present disclosure obtains about 5 million weak network traces by performing network measurement in live streaming services of some occasions of the application software, and reduces the live streaming hundred-second stall time length of these occasions by about 10% by using the weak network traces for targeted optimization locally.

[0091] Figure 4 is a block diagram of a network measurement device according to an exemplary embodiment of the present disclosure. Referring to Figure 4 , the device includes a receiving unit 40, a first obtaining unit 42, a second obtaining unit 44, a determining unit 46, and an extracting unit 48.

[0092] The receiving unit 40 is configured to receive the viewing quality degradation information sent by the client; the first obtaining unit 42 is configured to obtain a network degradation time interval of the target network based on the viewing quality degradation information; the second obtaining unit 44 is configured to obtain network state information of the target network with a round-trip time of a data packet as a granularity by using a congestion control model, and obtain data packet transmission information of the target network with a round-trip time of a data packet as a granularity by monitoring the target network; the determining unit 46 is configured to determine the network state information and the data packet transmission information within the network degradation time interval as original information of a weak network part of the target network; and the extracting unit 48 is configured to extract a plurality of key features with a first predetermined time length as a granularity from the original information of the weak network part and determine the plurality of key features as network measurement data of the weak network part, wherein the key features refer to features representing real-time quality of the weak network part.

[0093] According to an example embodiment of the present disclosure, the first obtaining unit 42 is further configured to obtain a degradation start time at which viewing quality degradation occurs at the client and a start download time of a video part at which viewing quality degradation occurs at the client based on the viewing quality degradation information; and determine a degradation start time at which viewing quality degradation occurs at the server and a start download time of the video part at the server based on a first time, a second time, the degradation start time at the client and the start download time at the client, wherein the first time is a time at which a transmission layer in communication with the client receives the viewing quality degradation information, and the second time is a time at which the server receives the viewing quality degradation information; and determine the network degradation time interval of the target network with the start download time at the server and the degradation start time as boundaries.

[0094] According to an example embodiment of the present disclosure, the determining unit 46 is further configured to expand the network degradation time interval based on a second predetermined time length; and determine the network state information and the data packet transmission information within the expanded network degradation time interval as the original information of the weak network part of the target network.

[0095] According to an example embodiment of the present disclosure, in a case where the key features include a physical propagation delay, a bottleneck routing bandwidth, a random packet loss rate and a bottleneck routing maximum queue depth, the extracting unit 48 is further configured to take the physical propagation delay and the physical bandwidth in the original information as the physical propagation delay and the bottleneck routing bandwidth of the weak network part, respectively; determine a correlation degree between an observed round-trip delay and an observed packet loss rate in the original information, and determine the random packet loss rate and the bottleneck routing maximum queue depth of the weak network part based on the correlation degree; and sample the physical propagation delay, the bottleneck routing bandwidth, the random packet loss rate and the bottleneck routing maximum queue depth with the first predetermined time length as a granularity, respectively, to obtain a plurality of key features of each kind of key feature.

[0096] According to an example embodiment of the present disclosure, the extraction unit 48 is further configured to, in response to the correlation degree being less than a first threshold, take an average of the observed packet loss rates of all the observed round-trip time intervals as the random packet loss rate of the weak network part; in response to the correlation degree being greater than a second threshold, take an average of the observed packet loss rates corresponding to a predetermined observed round-trip time interval as the random packet loss rate of the weak network part, wherein the predetermined observed round-trip time interval is the interval with the minimum average observed round-trip time; and in response to the correlation degree being greater than a third threshold, determine the maximum queue depth of the bottleneck route based on the maximum observed round-trip time, the bottleneck route bandwidth, and the physical propagation delay.

[0097] According to an example embodiment of the present disclosure, the extraction unit 48 is further configured to partition the observed round-trip times in the original information to obtain a plurality of observed round-trip time intervals; for each observed round-trip time interval, obtain an average of the observed packet loss rates and an average of the observed round-trip times; and determine a correlation degree of the observed round-trip times and the observed packet loss rates based on a Spearman correlation coefficient of the average of the observed packet loss rates and the average of the observed round-trip times of each observed round-trip time interval.

[0098] According to an example embodiment of the present disclosure, an electronic device can be provided. Figure 5 FIG. 5 is a block diagram of an electronic device 500 according to an example embodiment of the present disclosure. The electronic device 500 includes at least one memory 501 and at least one processor 502, and the at least one memory stores a set of computer executable instructions. When the set of computer executable instructions is executed by the at least one processor, a network measurement method according to an example embodiment of the present disclosure is performed.

[0099] As an example, the electronic device 500 can be a PC computer, a tablet device, a personal digital assistant, a smart phone, or other devices capable of executing the above-mentioned set of instructions. Here, the electronic device 1000 does not necessarily have to be a single electronic device, but can also be a collection of any devices or circuits capable of executing the above-mentioned instructions (or set of instructions) individually or jointly. The electronic device 500 can also be part of an integrated control system or a system manager, or can be configured as a portable electronic device that interfaces with a local or remote device (e.g., via wireless transmission).

[0100] In the electronic device 500, the processor 502 can include a central processor (CPU), a graphics processor (GPU), a programmable logic device, a dedicated processor system, a microcontroller, or a microprocessor. As an example but not limitation, the processor 502 can also include an analog processor, a digital processor, a microprocessor, a multi-core processor, a processor array, a network processor, etc.

[0101] The processor 502 can execute instructions or code stored in the memory 501, which can also store data. The instructions and data can also be transmitted and received via a network using a network interface device, which can employ any known transmission protocol.

[0102] The memory 501 can be integrated with the processor 502, for example, by arranging RAM or flash memory within an integrated circuit microprocessor or the like. Alternatively, the memory 501 can comprise a separate device, such as an external disk drive, memory array, or other storage device usable by any database system. The memory 501 and the processor 502 can be operatively coupled, or can communicate with each other, for example, through I / O ports, network connections, or the like, so that the processor 502 can read files stored in the memory 501.

[0103] In addition, the electronic device 500 can also include a video display, such as a liquid crystal display, and a user interface, such as a keyboard, mouse, touch input device, or the like. All components of the electronic device can be connected to each other via a bus and / or network.

[0104] According to an embodiment of the present disclosure, a computer readable storage medium is also provided, wherein when instructions in the computer readable storage medium are run by at least one processor, the at least one processor is caused to perform the network measurement method of the embodiments of the present disclosure. Examples of the computer readable storage medium herein include read-only memory (ROM), programmable read-only memory (PROM), electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, nonvolatile memory, CD-ROM, CD-R, CD+R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD+R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-R LTH, BD-RE, Blu-ray or optical disk memory, hard disk drive (HDD), solid state disk (SSD), card type memory such as a multimedia card, secure digital (SD) card or extreme digital (XD) card, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk, and any other device configured to store a computer program in a non-transitory manner and provide the computer program to a processor or computer so that the processor or computer can execute the computer program, as well as any associated data, data files and data structures. The computer program in the computer readable storage medium described above can be run in an environment deployed in a computer device such as a client, host, proxy device, server, etc., and in addition, in one example, the computer program and any associated data, data files and data structures are distributed on a networked computer system so that the computer program and any associated data, data files and data structures are stored, accessed and executed by one or more processors or computers in a distributed manner.

[0105] According to an embodiment of the present disclosure, a computer program product is provided, comprising computer instructions, which, when executed by a processor, implement the network measurement method of the embodiments of the present disclosure.

[0106] Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the aspects disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure following, in general, the principles of the present disclosure and including such features that are evident to those skilled in the art or are known in the art and can be used in combination with the present disclosure. The specification and examples are to be considered exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.

[0107] It should be understood that the present disclosure is not limited to the precise construction that has been described above and shown in the accompanying drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope of the present disclosure. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A network measurement method, characterized in that, include: Receive viewing quality degradation information sent by the client; Based on the viewing quality degradation information, the network degradation time interval of the target network is obtained; Using a congestion control model, network status information of the target network is obtained at the granularity of round-trip time of data packets, and data packet transmission information of the target network at the granularity of round-trip time of data packets is obtained by monitoring the target network. The network status information and the data packet transmission information within the network degradation time interval are determined as the original information of the weak network portion of the target network; From the raw information of the weak network portion, multiple key features are extracted with a first predetermined duration as the granularity, and the multiple key features are determined as the network measurement data of the weak network portion, wherein the key features refer to features that characterize the real-time quality of the weak network portion.

2. The network measurement method as described in claim 1, characterized in that, The step of obtaining the network degradation time interval of the target network based on the viewing quality degradation information includes: Based on the viewing quality degradation information, the degradation start time and the start download time of the video portion with degraded viewing quality on the client are obtained. Based on the first time, the second time, the degradation start time and the start download time of the client, the degradation start time and the start download time of the video portion on the server are determined. The first time is the time when the transport layer connected to the client receives the viewing quality degradation information, and the second time is the time when the server receives the viewing quality degradation information. The network degradation time interval of the target network is determined by using the server's start download time and degradation start time as boundaries.

3. The network measurement method as described in claim 1, characterized in that, The network status information and data packet transmission information within the network degradation time interval are determined as the original information of the weak network portion of the target network, including: The network degradation time interval is extended based on a second predetermined duration; The network status information and the data packet transmission information within the expanded network degradation time interval are determined as the original information of the weak network portion of the target network.

4. The network measurement method as described in claim 1, characterized in that, When the key features include physical propagation delay, bottleneck route bandwidth, random packet loss rate, and maximum bottleneck route queue depth, etc. The step of extracting multiple key features from the original information of the weak network portion, with a granularity of a first predetermined duration, includes: The physical propagation delay and physical bandwidth in the original information are respectively used as the physical propagation delay and bottleneck routing bandwidth of the weak network part; Determine the correlation between observed round-trip delay and observed packet loss rate in the original information, and based on the correlation, determine the random packet loss rate and maximum queue depth of the bottleneck route in the weak network section; Using the first predetermined duration as the granularity, the physical propagation delay, the bottleneck routing bandwidth, the random packet loss rate, and the maximum queue depth of the bottleneck routing are sampled to obtain multiple key features for each key feature.

5. The network measurement method as described in claim 4, characterized in that, The step of determining the random packet loss rate and the maximum queue depth of the bottleneck route in the weak network section based on the relevance includes: In response to the correlation being less than a first threshold, the average of the observed packet loss rates of all observation round-trip delay intervals is taken as the random packet loss rate of the weak network portion; in response to the correlation being greater than a second threshold, the average observed packet loss rate corresponding to a predetermined observation round-trip delay interval is taken as the random packet loss rate of the weak network portion, wherein the predetermined observation round-trip delay interval is the interval with the minimum average observation round-trip delay. In response to the correlation being greater than a third threshold, the maximum queue depth of the bottleneck route is determined based on the maximum observation round-trip delay, the bottleneck route bandwidth, and the physical propagation delay.

6. The network measurement method as described in claim 4, characterized in that, Determining the correlation between the observation round-trip delay and the observation packet loss rate in the original information includes: The observation round-trip delay in the original information is partitioned to obtain multiple observation round-trip delay intervals; For each observation round-trip delay interval, obtain the average observation packet loss rate and the average observation round-trip delay. The correlation between observation round-trip delay and observation packet loss rate is determined based on the Spearman correlation coefficient of the average observation packet loss rate and the average observation round-trip delay for each observation round-trip delay interval.

7. A network measurement device, characterized in that, include: The receiving unit is configured to receive viewing quality degradation information sent by the client; The first acquisition unit is configured to acquire the network degradation time interval of the target network based on the viewing quality degradation information. The second acquisition unit is configured to use a congestion control model to acquire network status information of the target network in terms of round-trip time of data packets, and to acquire data packet transmission information of the target network in terms of round-trip time of data packets by monitoring the target network. The determining unit is configured to determine the network status information and the data packet transmission information within the network degradation time interval as the original information of the weak network portion of the target network; The extraction unit is configured to extract multiple key features at a granularity of a first predetermined duration from the raw information of the weak network portion and determine the multiple key features as network measurement data of the weak network portion, wherein the key features refer to features characterizing the real-time quality of the weak network portion.

8. An electronic device, characterized in that, include: At least one processor; At least one memory that stores computer-executable instructions. The computer-executable instructions, when executed by the at least one processor, cause the at least one processor to perform the network measurement method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by at least one processor, the at least one processor causes the at least one processor to perform the network measurement method as described in any one of claims 1 to 6.

10. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the network measurement method as described in any one of claims 1 to 6.

Images