WO2016019523A1 - 识别网络传输拥塞的方法及装置 - Google Patents
识别网络传输拥塞的方法及装置 Download PDFInfo
- Publication number
- WO2016019523A1 WO2016019523A1 PCT/CN2014/083793 CN2014083793W WO2016019523A1 WO 2016019523 A1 WO2016019523 A1 WO 2016019523A1 CN 2014083793 W CN2014083793 W CN 2014083793W WO 2016019523 A1 WO2016019523 A1 WO 2016019523A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- sampling
- actual
- packet loss
- rate
- loss rate
- Prior art date
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
Definitions
- Embodiments of the present invention relate to communication technologies, and in particular, to a method and apparatus for identifying network transmission congestion. Background technique
- the common communication method is to transmit data through data packets, that is, the data packets are sent to the destination communication node by the source communication node correctly and without error.
- packet loss often occurs in the middle transmission process for various reasons. When a packet is lost during transmission, it often causes communication damage.
- Transmission congestion that is, the transmission requirement of the data packet exceeds the transmission capability of the transmission network. If the transmission requirement of the data packet is not lowered, the discarding cannot occur. The phenomenon of sending packets.
- Transmission error A transmission error occurs during intermediate transmission processing, resulting in failure to receive the correct data packet. For example, the intermediate transmission line is interfered, some bits of the data packet are wrong, and the receiving end finds that the data packet is incorrect, so that the data packet is discarded. Transmission errors caused by different causes will cause some or all of the bits to be wrong. These types of errors can be collectively referred to as transmission errors.
- the first type according to the number of data transmission and reception packets of the source communication node and the destination communication node, the data packet loss rate is obtained.
- the data packet loss rate is greater than the preset threshold, the reason for the data packet loss is that the transmission is congested, if the data packet is lost.
- the packet rate is less than the preset threshold, it is determined that the transmission is not congested.
- the determination method incorrectly determines that the packet loss caused by the transmission error is a transmission congestion packet, thereby erroneously determining the transmission non-congestion state as the transmission congestion state.
- the second type the delay jitter changes according to the change of the packet transmission delay between the source communication node and the communication node of the H.
- the delay jitter is greater than the preset threshold, the transmission congestion is determined, and when the delay jitter is less than the preset threshold, the judgment is The transmission is not congested.
- the judgment method has the following drawbacks: When a CAR packet loss occurs in the intermediate transmission network, the method may mistake the transmission congestion to cause the packet loss to be transmitted as a non-congested packet, thereby misjudge the transmission congestion state as the transmission non-congestion. status. Summary of the invention
- Embodiments of the present invention provide a method and apparatus for identifying network transmission congestion, so as to overcome the problem that the existing identification network transmission congestion method has incorrect identification, resulting in poor accuracy of the recognition result.
- a first aspect of the embodiments of the present invention provides a method for identifying network transmission congestion, including:
- the detected value is greater than or equal to the decision value, it is identified that the network is in a transmission congestion state.
- the actual packet loss rate, the actual transmission rate, and the actual packet loss rate at the N+1th sampling moment are obtained for the judgment according to the N sampling moments.
- congestion detection values including:
- a difference between the first variance and the second variance is used as the detected value.
- the actual packet loss rate at N+1 sampling instants including:
- the acquiring the number of sent data packets and the number of received data packets at the N sampling time and the N+1 sampling time Includes:
- the obtaining the actual sending rate of the N sampling moments includes:
- the actual transmission rate of N sampling instants is received from the source communication node.
- the acquiring the number of the sent data packets and the number of the received data packets at the N sampling time and the N+1 sampling time Includes:
- the obtaining the actual sending rate of the N sampling moments includes:
- the actual transmission rate of N sampling instants is received from the destination communication node.
- the acquiring the number of sent data packets and the number of received data packets at the N sampling time and the N+1 sampling time Includes:
- the obtaining the actual sending rate of the N sampling moments includes:
- the actual transmission rate of N sampling instants is received from the source communication node.
- the value of the N ranges from 3 to 5.
- a second aspect of the embodiments of the present invention provides an apparatus for identifying network transmission congestion, including: a sampling module, configured to obtain an actual packet loss rate and an actual transmission rate at the N sampling moments, and an actual packet loss rate at the N+1th sampling moment;
- An obtaining module configured to obtain, according to an actual packet loss rate, an actual transmission rate, and an actual packet loss rate at the N+1th sampling moment, a detection value used to determine whether the network transmits congestion; Comparing the detected value with a preset decision value;
- an identifying module configured to: if the detected value is greater than or equal to the decision value, identify that the network is in a transmission congestion state.
- the acquiring module includes: a first determining unit, configured to determine an actual packet loss rate of the first sampling moment as an error packet loss rate; An acquiring unit, configured to acquire a first variance of the actual packet loss rate and the error packet loss rate at the N sampling moments;
- a second acquiring unit configured to compare the actual transmission rate of the N sampling moments with the actual transmission rate of the first sampling moment, and the ratio corresponding to each sampling moment to the The error packet loss rate is multiplied, and the congestion loss rate corresponding to the N sampling times is obtained, and the congestion loss rate corresponding to the previous sampling time and the actual packet loss rate corresponding to the adjacent subsequent sampling time are obtained.
- Second variance Second variance
- a second determining unit configured to use a difference between the first variance and the second variance as the detected value.
- the sampling module includes:
- a number obtaining unit configured to acquire the number of sending data packets and the number of received data packets at the N sampling time and the N+1 sampling time;
- a calculating unit configured to calculate, according to the number of the sent data packets and the number of the received data packets at each sampling moment, the actual packet loss rate corresponding to each sampling moment;
- the rate obtaining unit is configured to acquire the actual sending rate of the N sampling moments.
- the number obtaining unit is specifically configured to receive, by the source communication node, N sampling moments and the (N+1)th sampling moment The number of the transmitted data packets and the number of the received data packets;
- the rate obtaining unit is specifically configured to receive the actual sending rate of the N sampling moments from the source communications node.
- the number obtaining unit is specifically configured to receive N sampling moments and the N+1 sampling moments from the destination communication node. Number of the transmitted data packets and the number of received data packets ⁇ '
- the rate obtaining unit is specifically configured to receive the actual sending rate of the N sampling moments from the destination communication node.
- the number obtaining unit is specifically configured to receive N sampling moments and the (N+1)th sampling moment from a source communication node. The number of the transmitted data packets, and the number of received data packets received from the destination communication node for the N sampling instants and the (N+1)th sampling moment.
- the rate obtaining unit is specifically configured to receive, by the source communication node, the actual sending rate of the N sampling moments.
- the second aspect or the first achievable manner of the second aspect or the second achievable manner of the second aspect or the third achievable manner of the second aspect or the fourth achievable of the second aspect ranges from 3 to 5.
- a third aspect of the embodiments of the present invention provides a device for identifying a network transmission congestion, including: a communication interface and a processor;
- the processor is configured to: obtain an actual packet loss rate and an actual transmission rate at the N sampling moments, and an actual packet loss rate at the N+1th sampling moment, according to an actual packet loss rate at the N sampling moments, The actual transmission rate and the actual packet loss rate at the N+1th sampling instant, obtaining a detection value for determining whether the network transmits congestion, and comparing the detection value with a preset determination value, if the detection value is greater than Or equal to the decision value, identifying that the network is in a transmission congestion state.
- the method further includes: a memory, configured to store a program; and the processor is specifically configured to execute a program stored in the memory.
- the technical effect of the embodiment of the present invention is: obtaining an actual packet loss rate and an actual transmission rate at a plurality of sampling moments by sampling, thereby calculating a detection value for determining whether the network is in transmission congestion, and then determining, based on the detection value, whether the network is in the network. Congestion status is transmitted to improve the accuracy of the recognition result.
- FIG. 1 is a flowchart of a method for identifying network transmission congestion according to Embodiment 1 of the present invention
- FIG. 2 is a schematic diagram of a detection node deployment solution according to Embodiment 1 of the present invention
- FIG. 3 is a schematic diagram of another detection node deployment solution according to Embodiment 1 of the present invention.
- FIG. 4 is a schematic diagram of another detection node deployment scheme according to Embodiment 1 of the present invention.
- Figure 5 shows the change between the packet loss rate under transmission congestion, the packet loss rate under transmission error, and the transmission rate.
- FIG. 6 is a schematic structural diagram of an apparatus for identifying network transmission congestion according to Embodiment 2 of the present invention
- FIG. 7 is a schematic structural diagram of an acquisition module according to Embodiment 2 of the present invention
- FIG. 8 is a schematic structural diagram of a sampling module according to Embodiment 2 of the present invention.
- FIG. 9 is a schematic structural diagram of an apparatus for identifying network transmission congestion according to Embodiment 3 of the present invention.
- the technical solutions in the embodiments of the present invention are clearly and completely described in the following with reference to the accompanying drawings in the embodiments of the present invention.
- the embodiments are a part of the embodiments of the invention, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention without creative efforts are within the scope of the present invention.
- FIG. 1 is a flowchart of a method for identifying network transmission congestion according to Embodiment 1 of the present invention. As shown in FIG. 1 , the method in this embodiment may include:
- the execution entity of this embodiment is a detection node. During the process of the source communication node transmitting a data packet to the destination communication node through the network, the detection node can perform sampling processing to obtain the actual packet loss rate and the actual transmission rate at the N sampling moments. .
- the detecting node acquires the number of the transmitted data packets and the number of the received data packets at the N sampling time and the N+1th sampling time, and calculates each according to the number of transmitted data packets and the number of received data packets at each sampling time.
- the detection node can also enter one
- the actual transmission rate of the N sampling instants is acquired.
- N is represented as N sampling moments.
- the value of N ranges from 3 to 5.
- the detecting node may be deployed on the source communication node, the destination communication node, or the network. This embodiment does not limit the location of the detection node deployment.
- FIG. 2 is a schematic diagram of a detection node deployment scheme according to Embodiment 1 of the present invention.
- a detection node is deployed at a source communication node, and a detection node may collect N sampling moments and a N+1 from a source communication node. The number of transmitted data packets at the sampling time and the number of received data packets.
- the destination communication node needs to transmit the number of received data packets at each sampling instant to the source communication node through the network.
- the detecting node can collect the actual transmission rate of the N sampling instants from the source communication node.
- FIG. 3 is a schematic diagram of another detection node deployment scheme according to the first embodiment of the present invention.
- the detection node may collect N sampling moments and the N+1 from the source communication node. The number of transmitted data packets at the sampling time, and the actual transmission rate at the N sampling times, and the number of received data packets collected from the destination communication node at the N sampling time and the N+1 sampling time.
- the source communication node sends, by using the network, the number of the sent data packets at the N sampling time and the N+1th sampling time, and the actual sending rate at each sampling time to the destination communication node, where the detecting node obtains the target communication node again.
- the destination communication node sends the number of received data packets at the N sampling time and the N+1 sampling time to the source communication node through the network, and the detecting node acquires the number of received data packets from the source communication node.
- FIG. 4 is a schematic diagram of another detection node deployment scheme according to Embodiment 1 of the present invention.
- a detection node is deployed at a destination communication node, and a detection node receives N sampling moments and an N+1 sampling from a destination communication node.
- the number of transmitted data packets at the time and the number of received data packets, and the actual transmission rate of N sampling instants obtained from the destination communication node.
- the source communication node sends the number of the sent data packets at the N sampling time and the N+1th sampling time and the actual transmission rate at each sampling time to the destination communication node through the network, and the detecting node acquires N N from the destination communication node.
- the number of congestion data loss caused by transmission congestion is sent along with the data source communication node.
- the actual number of data packets rises linearly, that is, the congestion loss rate increases linearly with the packet transmission rate; and the number of error data packet loss caused by transmission error does not change with the number of packets actually sent by the source communication node.
- the change, that is, the error packet loss rate does not change with the change of the packet rate, as shown in FIG.
- the curve 1 is the transmission rate curve
- the actual sending rate ⁇ ⁇ sampling instants 1 referred to as the actual sending rate Rat ei
- T 2 sampling instants 2 referred to as Rate 2
- the actual transmission rate T 3 sampling instants 3 referred to as Rate 3
- Curve 2 is the packet loss rate curve under transmission congestion, which is the actual packet loss rate at the sampling time
- 1 ⁇ 2 is the actual packet loss rate at the sampling time
- 1 ⁇ 3 is the actual packet loss rate at the sampling time
- Curve 3 is the packet loss rate curve under transmission error
- Lt is the actual packet loss rate at ⁇ sampling time
- Lt 2 ' is the actual packet loss rate at T 2 sampling time
- Lt 3 ' is the actual packet loss at ⁇ 3 sampling time. rate.
- the trend of curve 2 is the same as that of curve 1, and curve 3 does not change with the change of curve 1, and the same packet loss rate is maintained at different sampling moments.
- the packet loss rate is obtained at the initial sampling time T1. If the network is in a transmission congestion state at time T1, the packet loss rate is a packet loss rate under transmission congestion. If the network is in a transmission error state at time T1, the packet loss rate is The packet loss rate under transmission error.
- the detecting node determines the actual packet loss rate at the first sampling time as the error packet loss rate, and obtains the first variance of the actual packet loss rate and the error packet loss rate at the sampling time.
- the detecting node compares the actual sending rate of the sampling moments with the actual sending rate of the first sampling moment, and the actual ratio of the ratio corresponding to each sampling moment to the actual sampling time of the first sampling moment. The rate is multiplied to obtain the congestion loss rate corresponding to one sampling time.
- the detecting node acquires a second variance of the congestion loss rate corresponding to the previous sampling moment and the actual packet loss rate corresponding to the adjacent subsequent sampling moment. Further, the detecting node uses the difference between the first variance and the second variance as a detected value for determining whether the network transmits congestion.
- ⁇ is the actual packet loss rate at the sampling time
- 1 ⁇ 2 is the actual packet loss rate at the sampling time
- 1 ⁇ 3 is the actual packet loss rate at the sampling time
- ⁇ is! 1 ⁇
- the actual transmission rate of the sampling time be Rat ei
- the actual transmission rate of the sampling time of T 2 is Rate 2
- the actual transmission rate of the sampling time of T 3 is Rate 3
- the actual transmission rate of the sampling time of T N is Rate N .
- the formula for calculating the detection value for determining whether the network transmits congestion is:
- ⁇ ) ⁇ ( ⁇ - )- ⁇ ( +1 - ⁇ ⁇ )
- D is the detection value, which is the actual packet loss rate at the sampling time
- ⁇ is 1 ⁇ sampling time
- ⁇ is the actual packet loss rate at the z + 1 sampling instant, which is the actual transmission rate at the second sampling instant
- ⁇ is the actual transmission rate at the sampling instant
- ⁇ is the number of sampling instants.
- the rate is used to indicate the change rate of the transmission rate at each sampling time and the transmission rate at the time T1.
- the packet loss rate increases with the transmission rate.
- the change is the same as the trend change.
- the congestion loss rate at each sampling time can be calculated according to the actual packet loss rate of T1.
- the actual packet loss rate at the z + l sampling time is taken as the packet loss rate caused by the z sampling time in the transmission congestion scenario.
- the first party of the actual packet loss rate and the error packet loss rate is engraved And obtaining a second variance of the actual packet loss rate corresponding to the congestion loss rate corresponding to the previous sampling moment. Further, the detected value D is the difference between the first variance and the second variance.
- a decision value is set in advance, and different decision values need to be set according to different transmission networks, for example, the decision values of the microwave transmission and the optical fiber transmission setting are different.
- the detecting node compares the obtained detected value with a preset decision value.
- the detected value When the detected value is greater than or equal to the decision value, it indicates that the network is congested at this time, and the detecting node can recognize that the network is in a transmission congestion state. When the detected value is smaller than the decision value, it indicates that the network is not congested, and the detecting node can judge that the network is in a non-transmission congestion state.
- the method for identifying network transmission congestion obtains the actual packet loss rate and the actual transmission rate at the N sampling times, and the actual packet loss rate at the N+1th sampling moment, and the actual packet loss according to the N sampling moments.
- the rate, the actual transmission rate, and the actual packet loss rate at the N+1th sampling instant obtain a detection value for determining whether the network transmits congestion, and compare the detection value with a preset decision value, if the detection The value is greater than or equal to the decision value, and the network is identified as being transmitted Congestion status.
- the actual packet loss rate and the actual transmission rate at a plurality of sampling moments are obtained by sampling, and a detection value for determining whether the network is in transmission congestion is calculated, and whether the network is in a transmission congestion state is determined based on the detection value, and the identification is improved.
- the accuracy of the results are obtained by sampling, and a detection value for determining whether the network is in transmission congestion is calculated, and whether the network is in a transmission congestion state is determined based on the detection value, and the identification is improved.
- FIG. 6 is a schematic structural diagram of an apparatus for identifying network transmission congestion according to Embodiment 2 of the present invention.
- the apparatus for identifying network transmission congestion in this embodiment may include: a sampling module 61, an obtaining module 62, and a comparison module 63. And identification module 64.
- the sampling module 61 is configured to obtain an actual packet loss rate and an actual transmission rate at the N sampling moments, and an actual packet loss rate at the N+1th sampling moment.
- the obtaining module 62 is configured to obtain, according to the actual packet loss rate, the actual transmission rate, and the actual packet loss rate at the N+1th sampling moment, a detection value used to determine whether the network transmits congestion.
- the comparison module 63 is configured to compare the detected value with a preset decision value.
- the identifying module 64 is configured to identify that the network is in a transmission congestion state if the detected value is greater than or equal to the decision value.
- the value of N ranges from 3 to 5.
- FIG. 7 is a schematic structural diagram of an acquiring module according to Embodiment 2 of the present invention.
- the acquiring module 62 includes: a first determining unit 621, a first obtaining unit 622, a second acquiring unit 623, and a second Determination unit 624.
- the first determining unit 621 is configured to determine an actual packet loss rate at the first sampling moment as an error packet loss rate.
- the first obtaining unit 622 is configured to acquire a first variance of the actual packet loss rate and the error packet loss rate at the N sampling moments.
- the second obtaining unit 623 is configured to compare the actual transmission rate of the N sampling moments with the actual transmission rate of the first sampling moment, and compare the ratio corresponding to each sampling moment.
- the error packet loss rate is multiplied, and the congestion loss rate corresponding to the N sampling times is obtained, and the actual packet loss rate corresponding to the congestion loss rate corresponding to the previous sampling time is obtained.
- the second variance is configured to compare the actual transmission rate of the N sampling moments with the actual transmission rate of the first sampling moment, and compare the ratio corresponding to each sampling moment.
- the second determining unit 624 is configured to use a difference between the first variance and the second variance as the detected value.
- FIG. 8 is a schematic structural diagram of a sampling module according to Embodiment 2 of the present invention, as shown in FIG.
- the sampling module 61 includes:
- the number obtaining unit 611 is configured to acquire the number of sending data packets and the number of received data packets at the N sampling time and the N+1 sampling time.
- the calculating unit 612 is configured to calculate, according to the number of the sent data packets and the number of the received data packets at each sampling time, the actual packet loss rate corresponding to each sampling moment.
- the rate obtaining unit 613 is configured to acquire the actual sending rate of the N sampling moments.
- the number obtaining unit 611 is specifically configured to receive, by the source communication node, the number of the sent data packets and the received data packet number of the N sampling moments and the (N+1)th sampling moment.
- the rate obtaining unit 613 is specifically configured to receive the actual transmission rate of the N sampling moments from the source communication node.
- the number obtaining unit 611 is specifically configured to receive, by the destination communication node, the number of the sent data packets and the number of the received data packets of the N sampling time and the N+1 sampling time.
- the rate obtaining unit 613 is specifically configured to receive the actual sending rate of the N sampling occasions from the destination communication node.
- the number obtaining unit 611 is specifically configured to receive, by the source communication node, the number of the sent data packets of the N sampling moments and the (N+1)th sampling moment, and receive the N sampling moments from the destination communication node. And the number of the received data packets at the time of the (N+1)th sampling.
- the rate obtaining unit 613 is specifically configured to receive, by the source communication node, the actual transmission rate of the N sampling occasions.
- the network transmission congestion device of this embodiment may be used to implement the technical solution of the method embodiment shown in FIG. 1.
- the implementation principle and technical effects are similar, and details are not described herein again.
- the actual packet loss rate and the actual transmission rate at a plurality of sampling moments are obtained by sampling, and a detection value for determining whether the network is in transmission congestion is calculated, and whether the network is in a transmission congestion state is determined based on the detection value, and the identification is improved.
- the accuracy of the results are obtained by sampling, and a detection value for determining whether the network is in transmission congestion is calculated, and whether the network is in a transmission congestion state is determined based on the detection value, and the identification is improved.
- FIG. 9 is a schematic structural diagram of an apparatus for identifying network transmission congestion according to Embodiment 3 of the present invention.
- the apparatus for identifying network transmission congestion in this embodiment includes: a communication interface 91, a processor 92, and a memory 93.
- the processor 92 is configured to acquire an actual packet loss rate and an actual transmission rate of the N sampling moments.
- the actual packet loss rate at the N+1th sampling time is obtained according to the actual packet loss rate, the actual transmission rate, and the actual packet loss rate at the N+1th sampling instant.
- the detected value of the congestion is compared with a preset decision value. If the detected value is greater than or equal to the decision value, it is recognized that the network is in a transmission congestion state.
- the memory 93 is configured to store a program.
- the processor 92 is specifically configured to execute a program stored in the memory 93.
- the processor 92 performs the actual packet loss rate according to the N sampling times, the actual transmission rate, and the actual packet loss rate at the N+1th sampling time, and obtains the detection value for determining whether the network transmits congestion. Specifically, determining an actual packet loss rate at the first sampling time as an error packet loss rate, and acquiring a first variance of the actual packet loss rate and the error packet loss rate at the N sampling moments, The actual transmission rate at the sampling time is respectively compared with the actual transmission rate of the first sampling time, and the actual value corresponding to each sampling time and the actual actual time of the first sampling time The packet loss rate is multiplied, and the congestion loss rate corresponding to the N sampling times is obtained, and the second variance of the actual packet loss rate corresponding to the congestion loss rate corresponding to the previous sampling time is obtained. And using a difference between the first variance and the second variance as the detection value.
- the processor 92 performs the actual packet loss rate and the actual transmission rate at the time of acquiring the N sampling times, and the actual packet loss rate at the N+1th sampling time, specifically performing the acquisition of the N sampling times and the The number of the transmitted data packets and the number of the received data packets at the time of the N+1th sampling, and the actual packet loss corresponding to each sampling time is calculated according to the number of the transmitted data packets and the number of the received data packets at each sampling time. Rate, the actual transmission rate of N sampling instants is obtained.
- the processor 92 when the processor 92 performs the acquiring the number of the data packets and the number of the received data packets at the Nth sampling time and the N+1th sampling time, performing, by the processor, the N sampling times and the receiving The number of the transmitted data packets at the time of the (N+1)th sampling and the number of the received data packets.
- the processor 92 performs the acquisition of the actual transmission rate of the N sampling moments, the processor actually performs the actual transmission rate of receiving N sampling moments from the source communication node.
- the processor 92 when the processor 92 performs the process of acquiring the number of the sent data packets and the number of the received data packets at the Nth sampling time and the number of the received data packets, the processor 92 performs: receiving N sampling moments from the destination communication node and The number of the transmitted data packets at the time of the (N+1)th sampling and the number of the received data packets.
- the processor 92 performs the acquiring the actual transmission rate of the N sampling moments
- the specific execution is: receiving the actual transmission rate of the N sampling moments from the destination communication node.
- the processor 92 when the processor 92 performs the process of acquiring the number of the sent data packets and the number of the received data packets at the Nth sampling time and the number of the received data packets, the processor 92 performs: receiving N sampling moments from the source communication node and And the number of the received data packets at the N+1th sampling time, and the number of the received data packets received by the destination communication node from the N sampling time and the N+1th sampling time.
- the processor 92 When the processor 92 performs the acquisition of the actual transmission rate of the N sampling moments, it specifically executes: receiving the actual transmission rate of the N sampling moments from the source communication node.
- N ranges from 3 to 5.
- the network transmission congestion device of this embodiment may be used to implement the technical solution of the method embodiment shown in FIG. 1.
- the implementation principle and technical effects are similar, and details are not described herein again.
- the actual packet loss rate and the actual transmission rate at a plurality of sampling moments are obtained by sampling, and a detection value for determining whether the network is in transmission congestion is calculated, and whether the network is in a transmission congestion state is determined based on the detection value, and the identification is improved.
- the accuracy of the results are obtained by sampling, and a detection value for determining whether the network is in transmission congestion is calculated, and whether the network is in a transmission congestion state is determined based on the detection value, and the identification is improved.
- the aforementioned program can be stored in a computer readable storage medium.
- the program when executed, performs the steps including the foregoing method embodiments; and the foregoing storage medium includes: a medium that can store program codes, such as a ROM, a RAM, a magnetic disk, or an optical disk.
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
本发明实施例提供一种识别网络传输拥塞的方法及装置。该方法,包括:通过获取N个采样时刻的实际丢包率和实际发送速率,以及第N+1个采样时刻的实际丢包率,根据N个采样时刻的实际丢包率、实际发送速率以及第N+1个采样时刻的实际丢包率,获取到用于判决网络是否传输拥塞的检测值,将所述检测值与预设的判决值进行比较,如果所述检测值大于或等于所述判决值,识别出所述网络处于传输拥塞状态。本实施例中,通过采样获取多个采样时刻的实际丢包率和实际发送速率,计算得到用于判决网络是否处于传输拥塞的检测值,基于该检测值判断网络是否处于传输拥塞状态,提高识别结果的准确性。
Description
识别网络传输拥塞的方法及装置
技术领域
本发明实施例涉及通信技术, 尤其涉及一种识别网络传输拥塞的方法及 装置。 背景技术
现代通信***中, 普通的通信方式是通过数据包进行数据传输, 即将数 据包由源通信节点正确无误的送到目的通信节点。 在实际传输过程中, 由于 各种原因在中间传输的过程中往往会出现数据包丢失的现象。 当数据包在传 输过程中丢失后, 往往会带来通信损伤。
一般在中间传输过程丢失数据包的原因主要有两种: (1 )传输拥塞: 即 待数据包的发送需求超出了传输网络的传送能力, 如不降低数据包的发送需 求, 就会出现丢弃无法发送的数据包的现象。 (2 )传输错误: 即在中间传输 处理过程发生传输错误, 导致无法接收正确的数据包。 例如中间传输的线路 受到干扰, 数据包的某些比特位发生错误, 接收端发现该数据包不正确, 从 而丢弃该数据包。 不同原因引起的传输错误都会使部分或全部比特位错误, 该类错误可以统称为传输误码。
目前判断是否为传输拥塞导致数据包丢失的方法主要有两种:
第一种: 根据源通信节点与目的通信节点的的收发数据包数得出数据丢 包率, 当数据丢包率大于预设门限时判断为数据包丢失的原因为传输拥塞, 如果数据包丢包率小于预设门限时判断为传输非拥塞。 但是该判断方式会误 将传输误码引起的丢包判断为传输拥塞丢包, 从而将传输非拥塞状态误判为 传输拥塞状态。
第二种: 根据源通信节点与 H的通信节点间包传输时延的变化得出时延 抖动变化, 时延抖动大于预设门限时判断为传输拥塞, 时延抖动小于预设门 限时判断为传输非拥塞。 但是该判断方法会存在下面的缺陷: 在中间传输网 络出现 CAR丢包时,该方法会误将传输拥塞造成丢包判断为传输非拥塞的丢 包, 从而将传输拥塞状态误判为传输非拥塞状态。
发明内容
本发明实施例提供一种识别网络传输拥塞的方法及装置, 以克服现有 识别网络传输拥塞方法存在错误识别, 导致识别结果准确性较差的问题。
本发明实施例的第一方面是提供一种识别网络传输拥塞的方法, 包 括:
获取 N个采样时刻的实际丢包率和实际发送速率, 以及第 N+1个采样时 刻的实际丢包率;
根据 N个采样时刻的实际丢包率、实际发送速率以及第 N+1个采样时刻 的实际丢包率, 获取到用于判决网络是否传输拥塞的检测值;
将所述检测值与预设的判决值进行比较;
如果所述检测值大于或等于所述判决值, 识别出所述网络处于传输拥塞 状态。
结合第一方面, 在第一种可实现的方式中, 所述根据 N个采样时刻的 实际丢包率、 实际发送速率以及第 N+1个采样时刻的实际丢包率, 获取到用 于判决网络是否传输拥塞的检测值, 包括:
将第 1个采样时刻的实际丢包率确定为误码丢包率;
获取 N个采样时刻的所述实际丢包率与所述误码丢包率的第一方差; 将 N个采样时刻的所述实际发送速率分别与所述第 1个采样时刻的所述 实际发送速率作比值, 以及将每个采样时刻对应的所述比值与所述第 1个采 样时刻的所述实际丢包率作乘法, 得到 N个采样时刻对应的拥塞丢包率; 获取前一采样时刻对应的所述拥塞丢包率与相邻后一采样时刻对应的所 述实际丢包率的第二方差;
将所述第一方差与所述第二方差之间的差值作为所述检测值。
结合第一方面或者第一方面的第一种可实现的方式, 在第二种可实现 的方式中, 所述获取 N个采样时刻的实际丢包率和实际发送速率, 以及第
N+1个采样时刻的实际丢包率, 包括:
获取 N个采样时刻以及所述第 N+1采样时刻的发送数据包数目和接收数 据包数目;
根据每个采样时刻的所述发送数据包数目和所述接收数据包数目, 计算 得到每个采样时刻对应的所述实际丢包率;
获取 N个采样时刻的所述实际发送速率。
结合第一方面的第二种可实现的方式, 在第三种可实现的方式中, 所 述获取 N个采样时刻以及所述第 N+1采样时刻的发送数据包数目和接收数据 包数目, 包括:
从源通信节点接收 N个采样时刻以及所述第 N+1采样时刻的所述发送数 据包数目和所述接收数据包数目;
所述获取 N个采样时刻的所述实际发送速率, 包括:
从所述源通信节点接收 N个采样时刻的所述实际发送速率。
结合第一方面的第二种可实现的方式, 在第四种可实现的方式中, 所 述获取 N个采样时刻以及所述第 N+1采样时刻的发送数据包数目和接收数据 包数目, 包括:
从目的通信节点接收 N个采样时刻以及所述第 N+1采样时刻的所述发送 数据包数目和所述接收数据包数目;
所述获取 N个采样时刻的所述实际发送速率, 包括:
从所述目的通信节点接收 N个采样时刻的所述实际发送速率。
结合第一方面的第二种可实现的方式, 在第五种可实现的方式中, 所 述获取 N个采样时刻以及所述第 N+1采样时刻的发送数据包数目和接收数据 包数目, 包括:
从源通信节点接收 N个采样时刻以及所述第 N+1采样时刻的所述发送数 据包数目;
从目的通信节点接收 N个采样时刻以及所述第 N+1采样时刻的所述接收 数据包数目;
所述获取 N个采样时刻的所述实际发送速率, 包括:
从所述源通信节点接收 N个采样时刻的所述实际发送速率。
结合第一方面或者第一方面的第一种可实现的方式或者第
第二种可实现的方式或者第一方面的第三种可实现的方式或者
的第四种可实现的方式或者第一方面的第五种可实现的方式, 在
实现的方式中, 所述 N的取值范围为 3~5。
本发明实施例的第二方面是提供一种识别网络传输拥塞的装置, 包 括:
采样模块, 用于获取 N个采样时刻的实际丢包率和实际发送速率, 以及 第 N+1个采样时刻的实际丢包率;
获取模块, 用于根据 N个采样时刻的实际丢包率、 实际发送速率以及第 N+1个采样时刻的实际丢包率,获取到用于判决网络是否传输拥塞的检测值; 比较模块, 用于将所述检测值与预设的判决值进行比较;
识别模块, 用于如果所述检测值大于或等于所述判决值, 识别出所述网 络处于传输拥塞状态。
结合第二方面, 在第一种可实现的方式中, 所述获取模块, 包括: 第一确定单元,用于将第 1个采样时刻的实际丢包率确定为误码丢包率; 第一获取单元, 用于获取 N个采样时刻的所述实际丢包率与所述误码丢 包率的第一方差;
第二获取单元, 用于将 N个采样时刻的所述实际发送速率分别与所述第 1 个采样时刻的所述实际发送速率作比值, 以及将每个采样时刻对应的所述 比值与所述误码丢包率作乘法, 得到 N个采样时刻对应的拥塞丢包率, 以及 获取前一采样时刻对应的所述拥塞丢包率与相邻后一采样时刻对应的所述实 际丢包率的第二方差;
第二确定单元, 用于将所述第一方差与所述第二方差之间的差值作为所 述检测值。
结合第二方面或者第二方面的第一种可实现的方式, 在第二种可实现 的方式中, 所述采样模块, 包括:
数目获取单元,用于获取 N个采样时刻以及所述第 N+1采样时刻的发送 数据包数目和接收数据包数目;
计算单元, 用于根据每个采样时刻的所述发送数据包数目和所述接收数 据包数目, 计算得到每个采样时刻对应的所述实际丢包率;
速率获取单元, 用于获取 N个采样时刻的所述实际发送速率。
结合第二方面的第二种可实现的方式, 在第三种可实现的方式中, 所 述数目获取单元, 具体用于从源通信节点接收 N个采样时刻以及所述第 N+1 采样时刻的所述发送数据包数目和所述接收数据包数目;
所述速率获取单元, 具体用于从所述源通信节点接收 N个采样时刻的所 述实际发送速率。
结合第二方面的第二种可实现的方式, 在第四种可实现的方式中, 所 述数目获取单元, 具体用于从目的通信节点接收 N个采样时刻以及所述第 N+1采样时刻的所述发送数据包数目和所述接收数据包数目 ·'
所述速率获取单元, 具体用于从所述目的通信节点接收 N个采样时刻的 所述实际发送速率。
结合第二方面的第二种可实现的方式, 在第五种可实现的方式中, 所 述数目获取单元, 具体用于从源通信节点接收 N个采样时刻以及所述第 N+1 采样时刻的所述发送数据包数目, 以及从目的通信节点接收 N个采样时刻以 及所述第 N+1采样时刻的所述接收数据包数目 ·'
所述速率获取单元,具体用于 N从所述源通信节点接收 N个采样时刻的 所述实际发送速率。
结合第二方面或者第二方面的第一种可实现的方式或者第二方面的 第二种可实现的方式或者第二方面的第三种可实现的方式或者第二方面 的第四种可实现的方式或者第二方面的的第五种可实现的方式, 在第六种 可实现的方式中, 所述 N的取值范围为 3~5。
本发明实施例的第三方面是提供一种识别网络传输拥塞装置, 包括: 通信接口和处理器;
其中所述处理器, 用于执行: 获取 N个采样时刻的实际丢包率和实际 发送速率, 以及第 N+1个采样时刻的实际丢包率, 根据 N个采样时刻的实际 丢包率、 实际发送速率以及第 N+1个采样时刻的实际丢包率, 获取到用于判 决网络是否传输拥塞的检测值, 将所述检测值与预设的判决值进行比较, 如 果所述检测值大于或等于所述判决值, 识别出所述网络处于传输拥塞状态。
结合第三方面, 在第一种可实现的方式中, 还包括: 存储器, 用于存 放程序; 则所述处理器, 具体用于执行所述存储器所存放的程序。
本发明实施例的技术效果是:通过采样获取多个采样时刻的实际丢包率 和实际发送速率, 从而计算得到用于判决网络是否处于传输拥塞的检测值, 然后基于该检测值判断网络是否处于传输拥塞状态,提高识别结果的准确性。 附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案, 下面将对实
施例或现有技术描述中所需要使用的附图作一简单地介绍, 显而易见地, 下 面描述中的附图是本发明的一些实施例, 对于本领域普通技术人员来讲, 在 不付出创造性劳动性的前提下, 还可以根据这些附图获得其他的附图。
图 1为本发明实施例一提供的识别网络传输拥塞的方法的流程图; 图 2为本发明实施例一提供的一种检测节点部署方案;
图 3为本发明实施例一提供的另一种检测节点部署方案;
图 4为本发明实施例一提供的另一种检测节点部署方案;
图 5为传输拥塞下的丢包率、 传输错误下的丢包率与发送速率之间的变 化关系;
图 6为本发明实施例二提供的识别网络传输拥塞的装置的结构示意图; 图 7为本发明实施例二提供的获取模块的结构示意图;
图 8为本发明实施例二提供的采样模块的结构示意图;
图 9为本发明实施例三提供的识别网络传输拥塞的装置的结构示意图。 具体实施方式 为使本发明实施例的目的、 技术方案和优点更加清楚, 下面将结合本发 明实施例中的附图, 对本发明实施例中的技术方案进行清楚、 完整地描述, 显然, 所描述的实施例是本发明一部分实施例, 而不是全部的实施例。 基于 本发明中的实施例, 本领域普通技术人员在没有做出创造性劳动前提下所获 得的所有其他实施例, 都属于本发明保护的范围。
图 1为本发明实施例一提供的识别网络传输拥塞的方法的流程图, 如图 1所示, 本实施例的方法可以包括:
101、 获取 N个采样时刻的实际丢包率和实际发送速率, 以及第 N+1个 采样时刻的实际丢包率。
本实施例的执行主体为检测节点, 在源通信节点通过网络向目的通信节 点发送数据包的过程中, 该检测节点能够进行采样处理, 来获取 N个采样时 刻的实际丢包率以及实际发送速率。
具体地,检测节点获取 N个采样时刻以及所述第 N+1采样时刻的发送数 据包数目和接收数据包数目, 根据每个采样时刻的发送数据包数目和接收数 据包数目, 计算得到每个采样时刻对应的实际丢包率。 检测节点还可以进一
歩地采集获取到 N个采样时刻的实际发送速率。 N表示为 N个采样时刻。 优 选地, N的取值范围为 3~5。
本实施例中, 检测节点可以部署在源通信节点上、 目的通信节点或者网 络中。 本实施例对检测节点部署的位置不作限定。
图 2为本发明实施例一提供的一种检测节点部署方案, 如图 2所示, 检 测节点部署在源通信节点处, 检测节点可以从源通信节点采集到 N个采样时 刻以及第 N+1采样时刻的发送数据包数目和接收数据包数目, 首先, 目的通 信节点需要通过网络将每个采样时刻的接收数据包数目发送到源通信节点。 进一歩地,检测节点可以从源通信节点采集到 N个采样时刻的实际发送速率。
图 3为本发明实施例一提供的另一种检测节点部署方案, 如图 3所示, 检测节点部署在网络中时, 检测节点可以从源通信节点收集到 N个采样时刻 以及第 N+1采样时刻的发送数据包数目,以及 N个采样时刻的实际发送速率, 从目的通信节点收集到 N个采样时刻以及第 N+1采样时刻的接收数据包数 目。可选地, 源通信节点通过网络将 N个采样时刻以及第 N+1采样时刻的发 送数据包数目以及每个采样时刻的实际发送速率发送到目的通信节点, 检测 节点再从目的通信节点获取到 N个采样时刻以及第 N+1采样时刻的发送数据 包数目以及 N个采样时刻的实际发送速率。 可选地, 目的通信节点通过网络 将 N个采样时刻以及第 N+1采样时刻的接收数据包数目发送到源通信节点, 检测节点再从源通信节点获取到接收数据包数目。
图 4为本发明实施例一提供的另一种检测节点部署方案, 如图 4所示, 检测节点部署在目的通信节点处, 检测节点从目的通信节点接收 N个采样时 刻和第 N+1采样时刻的发送数据包数目和接收数据包数目, 以及从该目的通 信节点获取 N个采样时刻的所述实际发送速率。 其中, 源通信节点通过网络 将 N个采样时刻以及第 N+1采样时刻的发送数据包数目以及每个采样时刻的 实际发送速率发送到目的通信节点, 检测节点再从目的通信节点获取到 N个 采样时刻以及第 N+1采样时刻的发送数据包数目以及 N个采样时刻的实际发 送速率。
102、 根据 N个采样时刻的实际丢包率、 实际发送速率以及第 N+1个采 样时刻的实际丢包率, 获取到用于判决网络是否传输拥塞的检测值。
实际中, 由传输拥塞引起的拥塞数据丢包数目随着数据源通信节点发送
的实际数据包数目直线上升, 即拥塞丢包率随着发包率直线上升; 而由传输 误码引起的误码数据丢包数目, 不会随着源通信节点实际发送的数据包数目 的变化而变化, 即误码丢包率不随着发包率变化而变化, 如图 5所示。
图 5中曲线 1为发送速率曲线, Ί\采样时刻的实际发送速率 1记为 Ratei, T2采样时刻的实际发送速率 2记为 Rate2, T3采样时刻的实际发送速率 3记为 Rate3 曲线 2为传输拥塞下的丢包率曲线, 为 Ί\采样时刻的实际丢包率, 1^2为 采样时刻的实际丢包率, 1^3为 采样时刻的实际丢包率。 曲线 3为 传输错误下的丢包率曲线, Lt 为 Ί\采样时刻的实际丢包率, Lt2 '为 T2采样时 刻的实际丢包率, Lt3 '为 Τ3采样时刻的实际丢包率。
其中, 曲线 2的变化趋势与曲线 1的变化趋势相同, 而曲线 3不随着曲 线 1的变化而变化, 在不同的采样时刻保持相同的丢包率。 在初始采样时刻 T1获取丢包率, 如果 T1时刻网络处于传输拥塞状态下, 则该丢包率为传输 拥塞下的丢包率, 如果 T1时刻网络处于传输错误状态下, 则该丢包率为传输 错误下的丢包率。
具体地, 检测节点将第 1个采样时刻的实际丢包率确定为误码丢包率, 获取 Ν个采样时刻的所述实际丢包率与所述误码丢包率的第一方差。
检测节点将 Ν个采样时刻的实际发送速率分别与第 1个采样时刻的实际 发送速率作比值, 以及将每个采样时刻对应的所述比值与所述第 1个采样时 刻的所述实际丢包率作乘法, 得到 Ν个采样时刻对应的拥塞丢包率。
检测节点获取前一采样时刻对应的所述拥塞丢包率与相邻后一采样时刻 对应的所述实际丢包率的第二方差。 进一歩地, 检测节点将所述第一方差与 所述第二方差之间的差值作为用于判决网络是否传输拥塞的检测值。
本实施例,设!^为^采样时刻的实际丢包率, 1^2为 采样时刻的实际 丢包率, 1^3为 采样时刻的实际丢包率, ..., !^^为!1^^采样时刻的实际丢 包率, LtN+1采样时刻的实际丢包率。 设1\采样时刻的实际发送速率为 Ratei, T2采样时刻的实际发送速率为 Rate2,T3采样时刻的实际发送速率为 Rate3,..., TN采样时刻的实际发送速率为 RateN。
其中, 计算用于判决网络是否传输拥塞的检测值的公式为:
Ζ) =∑(^ - )-∑( +1 - χ ^) 其中, D为检测值, 为第 采样时刻的实际丢包率, ^为 1\采样时刻
的实际丢包率, ^为第 z + 1采样时刻的实际丢包率, 为第 ζ采样时刻的实 际发送速率, ^^^为^采样时刻的实际发送速率, Ν为采样时刻的数目。
Ratei
本实施例中, Rate、用于表示每个采样时刻的发送速率与 T1时刻的发送 速率的变化趋势, 根据图 5所示可知当网络处于传输拥塞状态时, 由于丢包 率随着发送速率的变化同趋势变化, 在获取到每个采样时刻的发送速率的变 化趋势后, 根据 T1的实际丢包率可以计算出每个采样时刻的拥塞丢包率。而 在实际中, 考虑到传输拥塞场景下丢包率变化滞后发送速率变化, 将 z + l采样 时刻的实际丢包率 作为传输拥塞场景下 z采样时刻 所引起的丢包率。 本实施例中, 通过 刻的所述实际丢包率与所 述误码丢包率的第一方
通过 获取前一采样时刻对应的 所述拥塞丢包率与相邻后一采样时刻对应的所述实际丢包率的第二方差。 进 一歩地, 检测值 D为第一方差和第二方差的差值。
103、 将所述检测值与预设的判决值进行比较。
本实施例中, 预先设置一个判决值, 需要根据不同传输网络来设置不同 的判决值, 如, 微波传输与光纤传输设置的判决值不同。 检测节点将得到的 检测值与预设的判决值进行比较。
104、如果所述检测值大于或等于所述判决值, 识别出所述网络处于传输 拥塞状态。
在比较出检测值大于或者等于判决值时, 说明网络此时发生拥塞, 检测 节点可以识别出网络处于传输拥塞状态。 当比较出检测值小于判决值时, 说 明网络未发生拥塞, 检测节点可以判断网络处于非传输拥塞状态。
本实施例提供的识别网络传输拥塞方法, 通过获取 N个采样时刻的实际 丢包率和实际发送速率, 以及第 N+1个采样时刻的实际丢包率, 根据 N个采 样时刻的实际丢包率、 实际发送速率以及第 N+1个采样时刻的实际丢包率, 获取到用于判决网络是否传输拥塞的检测值, 将所述检测值与预设的判决值 进行比较, 如果所述检测值大于或等于所述判决值, 识别出所述网络处于传
输拥塞状态。 本实施例中, 通过采样获取多个采样时刻的实际丢包率和实际 发送速率, 计算得到用于判决网络是否处于传输拥塞的检测值, 基于该检测 值判断网络是否处于传输拥塞状态, 提高识别结果的准确性。
图 6为本发明实施例二提供的识别网络传输拥塞的装置的结构示意图, 如图 6所示, 本实施例的识别网络传输拥塞的装置可以包括: 采样模块 61、 获取模块 62、 比较模块 63和识别模块 64。
其中, 采样模块 61, 用于获取 N个采样时刻的实际丢包率和实际发送速 率, 以及第 N+1个采样时刻的实际丢包率。
获取模块 62, 用于根据 N个采样时刻的实际丢包率、 实际发送速率以及 第 N+1个采样时刻的实际丢包率, 获取到用于判决网络是否传输拥塞的检测 值。
比较模块 63, 用于将所述检测值与预设的判决值进行比较。
识别模块 64, 用于如果所述检测值大于或等于所述判决值, 识别出所述 网络处于传输拥塞状态。
本实施例中, 所述 N的取值范围为 3~5。
图 7为本发明实施例二提供的获取模块的结构示意图, 如图 7所示, 所 述获取模块 62, 包括: 第一确定单元 621、 第一获取单元 622、 第二获取单 元 623和第二确定单元 624。
其中, 第一确定单元 621, 用于将第 1个采样时刻的实际丢包率确定为 误码丢包率。
第一获取单元 622, 用于获取 N个采样时刻的所述实际丢包率与所述误 码丢包率的第一方差。
第二获取单元 623, 用于将 N个采样时刻的所述实际发送速率分别与所 述第 1个采样时刻的所述实际发送速率作比值, 以及将每个采样时刻对应的 所述比值与所述误码丢包率作乘法, 得到 N个采样时刻对应的拥塞丢包率, 以及获取前一采样时刻对应的所述拥塞丢包率与相邻后一采样时刻对应的所 述实际丢包率的第二方差。
第二确定单元 624, 用于将所述第一方差与所述第二方差之间的差值作 为所述检测值。
图 8为本发明实施例二提供的采样模块的结构示意图, 如图 8所示, 所
述采样模块 61, 包括:
数目获取单元 611, 用于获取 N个采样时刻以及所述第 N+1采样时刻的 发送数据包数目和接收数据包数目。
计算单元 612, 用于根据每个采样时刻的所述发送数据包数目和所述接 收数据包数目, 计算得到每个采样时刻对应的所述实际丢包率。
速率获取单元 613, 用于获取 N个采样时刻的所述实际发送速率。
可选地, 所述数目获取单元 611, 具体用于从源通信节点接收 N个采样 时刻以及所述第 N+1 采样时刻的所述发送数据包数目和所述接收数据包数 圈。
所述速率获取单元 613, 具体用于从所述源通信节点接收 N个采样时刻 的所述实际发送速率。
可选地, 所述数目获取单元 611, 具体用于从目的通信节点接收 N个采 样时刻以及所述第 N+1采样时刻的所述发送数据包数目和所述接收数据包数 圈。
所述速率获取单元 613, 具体用于从所述目的通信节点接收 N个采样时 刻的所述实际发送速率。
可选地, 所述数目获取单元 611, 具体用于从源通信节点接收 N个采样 时刻以及所述第 N+1采样时刻的所述发送数据包数目, 以及从目的通信节点 接收 N个采样时刻以及所述第 N+1采样时刻的所述接收数据包数目。
所述速率获取单元 613, 具体用于 N从所述源通信节点接收 N个采样时 刻的所述实际发送速率。
本实施例的识别网络传输拥塞装置, 可以用于执行图 1所示方法实施例 的技术方案, 其实现原理和技术效果类似, 此处不再赘述。
本实施例中,通过采样获取多个采样时刻的实际丢包率和实际发送速率, 计算得到用于判决网络是否处于传输拥塞的检测值, 基于该检测值判断网络 是否处于传输拥塞状态, 提高识别结果的准确性。
图 9为本发明实施例三提供的识别网络传输拥塞的装置的结构示意图, 如图 9所示, 本实施例的识别网络传输拥塞的装置包括: 通信接口 91、 处理 器 92和存储器 93。
其中,处理器 92,用于获取 N个采样时刻的实际丢包率和实际发送速率,
以及第 N+1个采样时刻的实际丢包率, 根据 N个采样时刻的实际丢包率、 实 际发送速率以及第 N+1个采样时刻的实际丢包率, 获取到用于判决网络是否 传输拥塞的检测值, 将所述检测值与预设的判决值进行比较, 如果所述检测 值大于或等于所述判决值, 识别出所述网络处于传输拥塞状态。
所述存储器 93, 用于存放程序; 则所述处理器 92, 具体用于执行所述存 储器 93所存放的程序。
进一歩地, 在处理器 92执行根据 N个采样时刻的实际丢包率、 实际发 送速率以及第 N+1个采样时刻的实际丢包率, 获取到用于判决网络是否传输 拥塞的检测值时, 具体执行将第 1个采样时刻的实际丢包率确定为误码丢包 率, 获取 N个采样时刻的所述实际丢包率与所述误码丢包率的第一方差, 将 N个采样时刻的所述实际发送速率分别与所述第 1个采样时刻的所述实际发 送速率作比值, 以及将每个采样时刻对应的所述比值与所述第 1个采样时刻 的所述实际丢包率作乘法, 得到 N个采样时刻对应的拥塞丢包率, 获取前一 采样时刻对应的所述拥塞丢包率与相邻后一采样时刻对应的所述实际丢包率 的第二方差, 将所述第一方差与所述第二方差之间的差值作为所述检测值。
进一歩地, 所述处理器 92执行获取 N个采样时刻的实际丢包率和实际 发送速率, 以及第 N+1个采样时刻的实际丢包率时, 具体执行获取 N个采样 时刻以及所述第 N+1采样时刻的发送数据包数目和接收数据包数目, 根据每 个采样时刻的所述发送数据包数目和所述接收数据包数目, 计算得到每个采 样时刻对应的所述实际丢包率, 获取 N个采样时刻的所述实际发送速率。
可选地, 所述处理器 92执行所述获取 N个采样时刻以及所述第 N+1采 样时刻的发送数据包数目和接收数据包数目时, 具体执行从源通信节点接收 N个采样时刻以及所述第 N+1采样时刻的所述发送数据包数目和所述接收数 据包数目。 所述处理器 92执行获取 N个采样时刻的所述实际发送速率时, 具体执行从所述源通信节点接收 N个采样时刻的所述实际发送速率。
可选地, 所述处理器 92执行获取 N个采样时刻以及所述第 N+1采样时 刻的发送数据包数目和接收数据包数目时, 具体执行: 从目的通信节点接收 N个采样时刻以及所述第 N+1采样时刻的所述发送数据包数目和所述接收数 据包数目。 所述处理器 92执行获取 N个采样时刻的所述实际发送速率时, 具体执行: 从所述目的通信节点接收 N个采样时刻的所述实际发送速率。
可选地, 所述处理器 92执行获取 N个采样时刻以及所述第 N+1采样时 刻的发送数据包数目和接收数据包数目时, 具体执行: 从源通信节点接收 N 个采样时刻以及所述第 N+1采样时刻的所述发送数据包数目, 从目的通信节 点接收 N个采样时刻以及所述第 N+1采样时刻的所述接收数据包数目。
所述处理器 92执行获取 N个采样时刻的所述实际发送速率时, 具体执 行: 从所述源通信节点接收 N个采样时刻的所述实际发送速率。
所述 N的取值范围为 3~5。
本实施例的识别网络传输拥塞装置, 可以用于执行图 1所示方法实施例 的技术方案, 其实现原理和技术效果类似, 此处不再赘述。
本实施例中,通过采样获取多个采样时刻的实际丢包率和实际发送速率, 计算得到用于判决网络是否处于传输拥塞的检测值, 基于该检测值判断网络 是否处于传输拥塞状态, 提高识别结果的准确性。
本领域普通技术人员可以理解: 实现上述各方法实施例的全部或部分歩 骤可以通过程序指令相关的硬件来完成。 前述的程序可以存储于一计算机可 读取存储介质中。 该程序在执行时, 执行包括上述各方法实施例的歩骤; 而 前述的存储介质包括: ROM、 RAM, 磁碟或者光盘等各种可以存储程序代码 的介质。
最后应说明的是: 以上各实施例仅用以说明本发明的技术方案, 而非对 其限制; 尽管参照前述各实施例对本发明进行了详细的说明, 本领域的普通 技术人员应当理解:其依然可以对前述各实施例所记载的技术方案进行修改, 或者对其中部分或者全部技术特征进行等同替换; 而这些修改或者替换, 并 不使相应技术方案的本质脱离本发明各实施例技术方案的范围。
Claims
1、 一种识别网络传输拥塞的方法, 其特征在于, 包括:
获取 N个采样时刻的实际丢包率和实际发送速率, 以及第 N+1个采样时 刻的实际丢包率;
根据 N个采样时刻的实际丢包率、实际发送速率以及第 N+1个采样时刻 的实际丢包率, 获取到用于判决网络是否传输拥塞的检测值;
将所述检测值与预设的判决值进行比较;
如果所述检测值大于或等于所述判决值, 识别出所述网络处于传输拥塞 状态。
2、 根据权利要求 1所述的识别网络传输拥塞的方法, 其特征在于, 所述 根据 N个采样时刻的实际丢包率、实际发送速率以及第 N+1个采样时刻的实 际丢包率, 获取到用于判决网络是否传输拥塞的检测值, 包括:
将第 1个采样时刻的实际丢包率确定为误码丢包率;
获取 N个采样时刻的所述实际丢包率与所述误码丢包率的第一方差; 将 N个采样时刻的所述实际发送速率分别与所述第 1个采样时刻的所述 实际发送速率作比值, 以及将每个采样时刻对应的所述比值与所述第 1个采 样时刻的所述实际丢包率作乘法, 得到 N个采样时刻对应的拥塞丢包率; 获取前一采样时刻对应的所述拥塞丢包率与相邻后一采样时刻对应的所 述实际丢包率的第二方差;
将所述第一方差与所述第二方差之间的差值作为所述检测值。
3、 根据权利要求 1或 2所述的识别网络传输拥塞的方法, 其特征在于, 所述获取 N个采样时刻的实际丢包率和实际发送速率, 以及第 N+1个采样时 刻的实际丢包率, 包括:
获取 N个采样时刻以及所述第 N+1采样时刻的发送数据包数目和接收数 据包数目;
根据每个采样时刻的所述发送数据包数目和所述接收数据包数目, 计算 得到每个采样时刻对应的所述实际丢包率;
获取 N个采样时刻的所述实际发送速率。
4、 根据权利要求 3所述的识别网络传输拥塞的方法, 其特征在于, 所述 获取 N个采样时刻以及所述第 N+1采样时刻的发送数据包数目和接收数据包
数目, 包括:
从源通信节点接收 N个采样时刻以及所述第 N+1采样时刻的所述发送数 据包数目和所述接收数据包数目;
所述获取 N个采样时刻的所述实际发送速率, 包括:
从所述源通信节点接收 N个采样时刻的所述实际发送速率。
5、 根据权利要求 3所述的识别网络传输拥塞的方法, 其特征在于, 所述 获取 N个采样时刻以及所述第 N+1采样时刻的发送数据包数目和接收数据包 数目, 包括:
从目的通信节点接收 N个采样时刻以及所述第 N+1采样时刻的所述发送 数据包数目和所述接收数据包数目 ·'
所述获取 N个采样时刻的所述实际发送速率, 包括:
从所述目的通信节点接收 N个采样时刻的所述实际发送速率。
6、 根据权利要求 3所述的识别网络传输拥塞的方法, 其特征在于, 所述 获取 N个采样时刻以及所述第 N+1采样时刻的发送数据包数目和接收数据包 数目, 包括:
从源通信节点接收 N个采样时刻以及所述第 N+1采样时刻的所述发送数 据包数目;
从目的通信节点接收 N个采样时刻以及所述第 N+1采样时刻的所述接收 数据包数目;
所述获取 N个采样时刻的所述实际发送速率, 包括:
从所述源通信节点接收 N个采样时刻的所述实际发送速率。
7、 根据权利要求 1-6任一项所述的识别网络传输拥塞的方法, 其特征在 于, 所述 N的取值范围为 3~5。
8、 一种识别网络传输拥塞的装置, 其特征在于, 包括:
采样模块, 用于获取 N个采样时刻的实际丢包率和实际发送速率, 以及 第 N+1个采样时刻的实际丢包率;
获取模块, 用于根据 N个采样时刻的实际丢包率、 实际发送速率以及第 N+1个采样时刻的实际丢包率,获取到用于判决网络是否传输拥塞的检测值; 比较模块, 用于将所述检测值与预设的判决值进行比较;
识别模块, 用于如果所述检测值大于或等于所述判决值, 识别出所述网
络处于传输拥塞状态。
9、 根据权利要求 8所述的识别网络传输拥塞的装置, 其特征在于, 所述 获取模块, 包括:
第一确定单元,用于将第 1个采样时刻的实际丢包率确定为误码丢包率; 第一获取单元, 用于获取 N个采样时刻的所述实际丢包率与所述误码丢 包率的第一方差;
第二获取单元, 用于将 N个采样时刻的所述实际发送速率分别与所述第 1 个采样时刻的所述实际发送速率作比值, 以及将每个采样时刻对应的所述 比值与所述误码丢包率作乘法, 得到 N个采样时刻对应的拥塞丢包率, 以及 获取前一采样时刻对应的所述拥塞丢包率与相邻后一采样时刻对应的所述实 际丢包率的第二方差;
第二确定单元, 用于将所述第一方差与所述第二方差之间的差值作为所 述检测值。
10、根据权利要求 8或 9所述的识别网络传输拥塞的装置, 其特征在于, 所述采样模块, 包括:
数目获取单元,用于获取 N个采样时刻以及所述第 N+1采样时刻的发送 数据包数目和接收数据包数目;
计算单元, 用于根据每个采样时刻的所述发送数据包数目和所述接收数 据包数目, 计算得到每个采样时刻对应的所述实际丢包率;
速率获取单元, 用于获取 N个采样时刻的所述实际发送速率。
11、 根据权利要求 10所述的识别网络传输拥塞的装置, 其特征在于, 所述数目获取单元, 具体用于从源通信节点接收 N个采样时刻以及所述 第 N+1采样时刻的所述发送数据包数目和所述接收数据包数目;
所述速率获取单元, 具体用于从所述源通信节点接收 N个采样时刻的所 述实际发送速率。
12、 根据权利要求 10所述的识别网络传输拥塞的装置, 其特征在于, 所述数目获取单元, 具体用于从目的通信节点接收 N个采样时刻以及所 述第 N+1采样时刻的所述发送数据包数目和所述接收数据包数目;
所述速率获取单元, 具体用于从所述目的通信节点接收 N个采样时刻的 所述实际发送速率。
13、 根据权利要求 10所述的识别网络传输拥塞的装置, 其特征在于, 所述数目获取单元, 具体用于从源通信节点接收 N个采样时刻以及所述 第 N+1采样时刻的所述发送数据包数目, 以及从目的通信节点接收 N个采样 时刻以及所述第 N+1采样时刻的所述接收数据包数目 ·'
所述速率获取单元,具体用于 N从所述源通信节点接收 N个采样时刻的 所述实际发送速率。
14、 根据权利要求 8-13任一项所述的识别网络传输拥塞的装置, 其特征 在于, 所述 N的取值范围为 3~5。
15、 一种识别网络传输拥塞的装置, 其特征在于, 包括: 通信接口和处 理器; 其中, 所述处理器用于执行如权利要求 1-7任一项所述的识别网络传 输拥塞的方法。
16、 根据权利要求 15所述的识别网络传输拥塞的装置, 其特征在于, 还 包括: 存储器, 用于存放程序; 则所述处理器, 具体用于执行所述存储器所 存放的程序。
Priority Applications (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
PCT/CN2014/083793 WO2016019523A1 (zh) | 2014-08-06 | 2014-08-06 | 识别网络传输拥塞的方法及装置 |
CN201480013106.3A CN105517668B (zh) | 2014-08-06 | 2014-08-06 | 识别网络传输拥塞的方法及装置 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
PCT/CN2014/083793 WO2016019523A1 (zh) | 2014-08-06 | 2014-08-06 | 识别网络传输拥塞的方法及装置 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2016019523A1 true WO2016019523A1 (zh) | 2016-02-11 |
Family
ID=55263005
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CN2014/083793 WO2016019523A1 (zh) | 2014-08-06 | 2014-08-06 | 识别网络传输拥塞的方法及装置 |
Country Status (2)
Country | Link |
---|---|
CN (1) | CN105517668B (zh) |
WO (1) | WO2016019523A1 (zh) |
Cited By (33)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9935851B2 (en) | 2015-06-05 | 2018-04-03 | Cisco Technology, Inc. | Technologies for determining sensor placement and topology |
US9967158B2 (en) | 2015-06-05 | 2018-05-08 | Cisco Technology, Inc. | Interactive hierarchical network chord diagram for application dependency mapping |
US10033766B2 (en) | 2015-06-05 | 2018-07-24 | Cisco Technology, Inc. | Policy-driven compliance |
US10089099B2 (en) | 2015-06-05 | 2018-10-02 | Cisco Technology, Inc. | Automatic software upgrade |
US10116559B2 (en) | 2015-05-27 | 2018-10-30 | Cisco Technology, Inc. | Operations, administration and management (OAM) in overlay data center environments |
US10142353B2 (en) | 2015-06-05 | 2018-11-27 | Cisco Technology, Inc. | System for monitoring and managing datacenters |
US10171357B2 (en) | 2016-05-27 | 2019-01-01 | Cisco Technology, Inc. | Techniques for managing software defined networking controller in-band communications in a data center network |
US10177977B1 (en) | 2013-02-13 | 2019-01-08 | Cisco Technology, Inc. | Deployment and upgrade of network devices in a network environment |
US10250446B2 (en) | 2017-03-27 | 2019-04-02 | Cisco Technology, Inc. | Distributed policy store |
US10289438B2 (en) | 2016-06-16 | 2019-05-14 | Cisco Technology, Inc. | Techniques for coordination of application components deployed on distributed virtual machines |
US10374904B2 (en) | 2015-05-15 | 2019-08-06 | Cisco Technology, Inc. | Diagnostic network visualization |
US10523512B2 (en) | 2017-03-24 | 2019-12-31 | Cisco Technology, Inc. | Network agent for generating platform specific network policies |
US10523541B2 (en) | 2017-10-25 | 2019-12-31 | Cisco Technology, Inc. | Federated network and application data analytics platform |
US10554501B2 (en) | 2017-10-23 | 2020-02-04 | Cisco Technology, Inc. | Network migration assistant |
US10574575B2 (en) | 2018-01-25 | 2020-02-25 | Cisco Technology, Inc. | Network flow stitching using middle box flow stitching |
US10594560B2 (en) | 2017-03-27 | 2020-03-17 | Cisco Technology, Inc. | Intent driven network policy platform |
US10594542B2 (en) | 2017-10-27 | 2020-03-17 | Cisco Technology, Inc. | System and method for network root cause analysis |
US10680887B2 (en) | 2017-07-21 | 2020-06-09 | Cisco Technology, Inc. | Remote device status audit and recovery |
US10708183B2 (en) | 2016-07-21 | 2020-07-07 | Cisco Technology, Inc. | System and method of providing segment routing as a service |
US10708152B2 (en) | 2017-03-23 | 2020-07-07 | Cisco Technology, Inc. | Predicting application and network performance |
US10764141B2 (en) | 2017-03-27 | 2020-09-01 | Cisco Technology, Inc. | Network agent for reporting to a network policy system |
US10798015B2 (en) | 2018-01-25 | 2020-10-06 | Cisco Technology, Inc. | Discovery of middleboxes using traffic flow stitching |
US10826803B2 (en) | 2018-01-25 | 2020-11-03 | Cisco Technology, Inc. | Mechanism for facilitating efficient policy updates |
US10873593B2 (en) | 2018-01-25 | 2020-12-22 | Cisco Technology, Inc. | Mechanism for identifying differences between network snapshots |
US10873794B2 (en) | 2017-03-28 | 2020-12-22 | Cisco Technology, Inc. | Flowlet resolution for application performance monitoring and management |
US10917438B2 (en) | 2018-01-25 | 2021-02-09 | Cisco Technology, Inc. | Secure publishing for policy updates |
US10931629B2 (en) | 2016-05-27 | 2021-02-23 | Cisco Technology, Inc. | Techniques for managing software defined networking controller in-band communications in a data center network |
US10972388B2 (en) | 2016-11-22 | 2021-04-06 | Cisco Technology, Inc. | Federated microburst detection |
US10999149B2 (en) | 2018-01-25 | 2021-05-04 | Cisco Technology, Inc. | Automatic configuration discovery based on traffic flow data |
US11128700B2 (en) | 2018-01-26 | 2021-09-21 | Cisco Technology, Inc. | Load balancing configuration based on traffic flow telemetry |
US11233821B2 (en) | 2018-01-04 | 2022-01-25 | Cisco Technology, Inc. | Network intrusion counter-intelligence |
US11765046B1 (en) | 2018-01-11 | 2023-09-19 | Cisco Technology, Inc. | Endpoint cluster assignment and query generation |
CN118101526A (zh) * | 2024-04-24 | 2024-05-28 | 沈阳蓝巨人网络科技有限公司 | 基于信息通信技术的安全监测方法及*** |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112422356B (zh) * | 2019-08-22 | 2022-07-29 | 腾讯科技(深圳)有限公司 | 确定网络状态的方法及装置 |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101686100A (zh) * | 2008-09-25 | 2010-03-31 | 华为技术有限公司 | 处理丢包的方法、传输质量控制方法、装置及*** |
US7821937B1 (en) * | 2007-06-29 | 2010-10-26 | Symantec Corporation | Network protocol with damage loss resilient congestion control algorithm |
CN102204182A (zh) * | 2010-12-29 | 2011-09-28 | 华为技术有限公司 | 一种数据传输的拥塞控制方法及装置 |
US20130114421A1 (en) * | 2011-11-04 | 2013-05-09 | Microsoft Corporation | Adaptive bandwidth estimation |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101505268B (zh) * | 2009-03-12 | 2012-01-04 | 上海交通大学 | 基于加权一阶局域法的公共拥塞路径检测方法 |
-
2014
- 2014-08-06 CN CN201480013106.3A patent/CN105517668B/zh active Active
- 2014-08-06 WO PCT/CN2014/083793 patent/WO2016019523A1/zh active Application Filing
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7821937B1 (en) * | 2007-06-29 | 2010-10-26 | Symantec Corporation | Network protocol with damage loss resilient congestion control algorithm |
CN101686100A (zh) * | 2008-09-25 | 2010-03-31 | 华为技术有限公司 | 处理丢包的方法、传输质量控制方法、装置及*** |
CN102204182A (zh) * | 2010-12-29 | 2011-09-28 | 华为技术有限公司 | 一种数据传输的拥塞控制方法及装置 |
US20130114421A1 (en) * | 2011-11-04 | 2013-05-09 | Microsoft Corporation | Adaptive bandwidth estimation |
Cited By (112)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10177977B1 (en) | 2013-02-13 | 2019-01-08 | Cisco Technology, Inc. | Deployment and upgrade of network devices in a network environment |
US10374904B2 (en) | 2015-05-15 | 2019-08-06 | Cisco Technology, Inc. | Diagnostic network visualization |
US10116559B2 (en) | 2015-05-27 | 2018-10-30 | Cisco Technology, Inc. | Operations, administration and management (OAM) in overlay data center environments |
US11477097B2 (en) | 2015-06-05 | 2022-10-18 | Cisco Technology, Inc. | Hierarchichal sharding of flows from sensors to collectors |
US10623284B2 (en) | 2015-06-05 | 2020-04-14 | Cisco Technology, Inc. | Determining a reputation of a network entity |
US10089099B2 (en) | 2015-06-05 | 2018-10-02 | Cisco Technology, Inc. | Automatic software upgrade |
US10116530B2 (en) | 2015-06-05 | 2018-10-30 | Cisco Technology, Inc. | Technologies for determining sensor deployment characteristics |
US10009240B2 (en) | 2015-06-05 | 2018-06-26 | Cisco Technology, Inc. | System and method of recommending policies that result in particular reputation scores for hosts |
US10116531B2 (en) | 2015-06-05 | 2018-10-30 | Cisco Technology, Inc | Round trip time (RTT) measurement based upon sequence number |
US10129117B2 (en) | 2015-06-05 | 2018-11-13 | Cisco Technology, Inc. | Conditional policies |
US10142353B2 (en) | 2015-06-05 | 2018-11-27 | Cisco Technology, Inc. | System for monitoring and managing datacenters |
US11252058B2 (en) | 2015-06-05 | 2022-02-15 | Cisco Technology, Inc. | System and method for user optimized application dependency mapping |
US10171319B2 (en) | 2015-06-05 | 2019-01-01 | Cisco Technology, Inc. | Technologies for annotating process and user information for network flows |
US10177998B2 (en) | 2015-06-05 | 2019-01-08 | Cisco Technology, Inc. | Augmenting flow data for improved network monitoring and management |
US9979615B2 (en) | 2015-06-05 | 2018-05-22 | Cisco Technology, Inc. | Techniques for determining network topologies |
US10181987B2 (en) | 2015-06-05 | 2019-01-15 | Cisco Technology, Inc. | High availability of collectors of traffic reported by network sensors |
US10230597B2 (en) | 2015-06-05 | 2019-03-12 | Cisco Technology, Inc. | Optimizations for application dependency mapping |
US10243817B2 (en) | 2015-06-05 | 2019-03-26 | Cisco Technology, Inc. | System and method of assigning reputation scores to hosts |
US10904116B2 (en) | 2015-06-05 | 2021-01-26 | Cisco Technology, Inc. | Policy utilization analysis |
US11368378B2 (en) | 2015-06-05 | 2022-06-21 | Cisco Technology, Inc. | Identifying bogon address spaces |
US10305757B2 (en) | 2015-06-05 | 2019-05-28 | Cisco Technology, Inc. | Determining a reputation of a network entity |
US10320630B2 (en) | 2015-06-05 | 2019-06-11 | Cisco Technology, Inc. | Hierarchichal sharding of flows from sensors to collectors |
US10326672B2 (en) | 2015-06-05 | 2019-06-18 | Cisco Technology, Inc. | MDL-based clustering for application dependency mapping |
US10326673B2 (en) | 2015-06-05 | 2019-06-18 | Cisco Technology, Inc. | Techniques for determining network topologies |
US9967158B2 (en) | 2015-06-05 | 2018-05-08 | Cisco Technology, Inc. | Interactive hierarchical network chord diagram for application dependency mapping |
US10439904B2 (en) | 2015-06-05 | 2019-10-08 | Cisco Technology, Inc. | System and method of determining malicious processes |
US10454793B2 (en) | 2015-06-05 | 2019-10-22 | Cisco Technology, Inc. | System and method of detecting whether a source of a packet flow transmits packets which bypass an operating system stack |
US10505827B2 (en) | 2015-06-05 | 2019-12-10 | Cisco Technology, Inc. | Creating classifiers for servers and clients in a network |
US10505828B2 (en) | 2015-06-05 | 2019-12-10 | Cisco Technology, Inc. | Technologies for managing compromised sensors in virtualized environments |
US10516586B2 (en) | 2015-06-05 | 2019-12-24 | Cisco Technology, Inc. | Identifying bogon address spaces |
US10516585B2 (en) | 2015-06-05 | 2019-12-24 | Cisco Technology, Inc. | System and method for network information mapping and displaying |
US11968102B2 (en) | 2015-06-05 | 2024-04-23 | Cisco Technology, Inc. | System and method of detecting packet loss in a distributed sensor-collector architecture |
US11968103B2 (en) | 2015-06-05 | 2024-04-23 | Cisco Technology, Inc. | Policy utilization analysis |
US10536357B2 (en) | 2015-06-05 | 2020-01-14 | Cisco Technology, Inc. | Late data detection in data center |
US11936663B2 (en) | 2015-06-05 | 2024-03-19 | Cisco Technology, Inc. | System for monitoring and managing datacenters |
US10567247B2 (en) | 2015-06-05 | 2020-02-18 | Cisco Technology, Inc. | Intra-datacenter attack detection |
US11924073B2 (en) | 2015-06-05 | 2024-03-05 | Cisco Technology, Inc. | System and method of assigning reputation scores to hosts |
US11902121B2 (en) | 2015-06-05 | 2024-02-13 | Cisco Technology, Inc. | System and method of detecting whether a source of a packet flow transmits packets which bypass an operating system stack |
US11902120B2 (en) | 2015-06-05 | 2024-02-13 | Cisco Technology, Inc. | Synthetic data for determining health of a network security system |
US10623282B2 (en) | 2015-06-05 | 2020-04-14 | Cisco Technology, Inc. | System and method of detecting hidden processes by analyzing packet flows |
US11252060B2 (en) | 2015-06-05 | 2022-02-15 | Cisco Technology, Inc. | Data center traffic analytics synchronization |
US10623283B2 (en) | 2015-06-05 | 2020-04-14 | Cisco Technology, Inc. | Anomaly detection through header field entropy |
US10659324B2 (en) | 2015-06-05 | 2020-05-19 | Cisco Technology, Inc. | Application monitoring prioritization |
US11902122B2 (en) | 2015-06-05 | 2024-02-13 | Cisco Technology, Inc. | Application monitoring prioritization |
US10686804B2 (en) | 2015-06-05 | 2020-06-16 | Cisco Technology, Inc. | System for monitoring and managing datacenters |
US10693749B2 (en) | 2015-06-05 | 2020-06-23 | Cisco Technology, Inc. | Synthetic data for determining health of a network security system |
US11894996B2 (en) | 2015-06-05 | 2024-02-06 | Cisco Technology, Inc. | Technologies for annotating process and user information for network flows |
US11700190B2 (en) | 2015-06-05 | 2023-07-11 | Cisco Technology, Inc. | Technologies for annotating process and user information for network flows |
US10728119B2 (en) | 2015-06-05 | 2020-07-28 | Cisco Technology, Inc. | Cluster discovery via multi-domain fusion for application dependency mapping |
US10735283B2 (en) | 2015-06-05 | 2020-08-04 | Cisco Technology, Inc. | Unique ID generation for sensors |
US10742529B2 (en) | 2015-06-05 | 2020-08-11 | Cisco Technology, Inc. | Hierarchichal sharding of flows from sensors to collectors |
US11695659B2 (en) | 2015-06-05 | 2023-07-04 | Cisco Technology, Inc. | Unique ID generation for sensors |
US11637762B2 (en) | 2015-06-05 | 2023-04-25 | Cisco Technology, Inc. | MDL-based clustering for dependency mapping |
US10797973B2 (en) | 2015-06-05 | 2020-10-06 | Cisco Technology, Inc. | Server-client determination |
US10797970B2 (en) | 2015-06-05 | 2020-10-06 | Cisco Technology, Inc. | Interactive hierarchical network chord diagram for application dependency mapping |
US11601349B2 (en) | 2015-06-05 | 2023-03-07 | Cisco Technology, Inc. | System and method of detecting hidden processes by analyzing packet flows |
US10862776B2 (en) | 2015-06-05 | 2020-12-08 | Cisco Technology, Inc. | System and method of spoof detection |
US10033766B2 (en) | 2015-06-05 | 2018-07-24 | Cisco Technology, Inc. | Policy-driven compliance |
US11924072B2 (en) | 2015-06-05 | 2024-03-05 | Cisco Technology, Inc. | Technologies for annotating process and user information for network flows |
US11153184B2 (en) | 2015-06-05 | 2021-10-19 | Cisco Technology, Inc. | Technologies for annotating process and user information for network flows |
US11528283B2 (en) | 2015-06-05 | 2022-12-13 | Cisco Technology, Inc. | System for monitoring and managing datacenters |
US10917319B2 (en) | 2015-06-05 | 2021-02-09 | Cisco Technology, Inc. | MDL-based clustering for dependency mapping |
US11522775B2 (en) | 2015-06-05 | 2022-12-06 | Cisco Technology, Inc. | Application monitoring prioritization |
US11405291B2 (en) | 2015-06-05 | 2022-08-02 | Cisco Technology, Inc. | Generate a communication graph using an application dependency mapping (ADM) pipeline |
US11516098B2 (en) | 2015-06-05 | 2022-11-29 | Cisco Technology, Inc. | Round trip time (RTT) measurement based upon sequence number |
US10979322B2 (en) | 2015-06-05 | 2021-04-13 | Cisco Technology, Inc. | Techniques for determining network anomalies in data center networks |
US11502922B2 (en) | 2015-06-05 | 2022-11-15 | Cisco Technology, Inc. | Technologies for managing compromised sensors in virtualized environments |
US11496377B2 (en) | 2015-06-05 | 2022-11-08 | Cisco Technology, Inc. | Anomaly detection through header field entropy |
US9935851B2 (en) | 2015-06-05 | 2018-04-03 | Cisco Technology, Inc. | Technologies for determining sensor placement and topology |
US11102093B2 (en) | 2015-06-05 | 2021-08-24 | Cisco Technology, Inc. | System and method of assigning reputation scores to hosts |
US11121948B2 (en) | 2015-06-05 | 2021-09-14 | Cisco Technology, Inc. | Auto update of sensor configuration |
US11431592B2 (en) | 2015-06-05 | 2022-08-30 | Cisco Technology, Inc. | System and method of detecting whether a source of a packet flow transmits packets which bypass an operating system stack |
US11128552B2 (en) | 2015-06-05 | 2021-09-21 | Cisco Technology, Inc. | Round trip time (RTT) measurement based upon sequence number |
US10931629B2 (en) | 2016-05-27 | 2021-02-23 | Cisco Technology, Inc. | Techniques for managing software defined networking controller in-band communications in a data center network |
US11546288B2 (en) | 2016-05-27 | 2023-01-03 | Cisco Technology, Inc. | Techniques for managing software defined networking controller in-band communications in a data center network |
US12021826B2 (en) | 2016-05-27 | 2024-06-25 | Cisco Technology, Inc. | Techniques for managing software defined networking controller in-band communications in a data center network |
US10171357B2 (en) | 2016-05-27 | 2019-01-01 | Cisco Technology, Inc. | Techniques for managing software defined networking controller in-band communications in a data center network |
US10289438B2 (en) | 2016-06-16 | 2019-05-14 | Cisco Technology, Inc. | Techniques for coordination of application components deployed on distributed virtual machines |
US10708183B2 (en) | 2016-07-21 | 2020-07-07 | Cisco Technology, Inc. | System and method of providing segment routing as a service |
US11283712B2 (en) | 2016-07-21 | 2022-03-22 | Cisco Technology, Inc. | System and method of providing segment routing as a service |
US10972388B2 (en) | 2016-11-22 | 2021-04-06 | Cisco Technology, Inc. | Federated microburst detection |
US10708152B2 (en) | 2017-03-23 | 2020-07-07 | Cisco Technology, Inc. | Predicting application and network performance |
US11088929B2 (en) | 2017-03-23 | 2021-08-10 | Cisco Technology, Inc. | Predicting application and network performance |
US10523512B2 (en) | 2017-03-24 | 2019-12-31 | Cisco Technology, Inc. | Network agent for generating platform specific network policies |
US11252038B2 (en) | 2017-03-24 | 2022-02-15 | Cisco Technology, Inc. | Network agent for generating platform specific network policies |
US10764141B2 (en) | 2017-03-27 | 2020-09-01 | Cisco Technology, Inc. | Network agent for reporting to a network policy system |
US11509535B2 (en) | 2017-03-27 | 2022-11-22 | Cisco Technology, Inc. | Network agent for reporting to a network policy system |
US10250446B2 (en) | 2017-03-27 | 2019-04-02 | Cisco Technology, Inc. | Distributed policy store |
US10594560B2 (en) | 2017-03-27 | 2020-03-17 | Cisco Technology, Inc. | Intent driven network policy platform |
US11146454B2 (en) | 2017-03-27 | 2021-10-12 | Cisco Technology, Inc. | Intent driven network policy platform |
US11863921B2 (en) | 2017-03-28 | 2024-01-02 | Cisco Technology, Inc. | Application performance monitoring and management platform with anomalous flowlet resolution |
US11202132B2 (en) | 2017-03-28 | 2021-12-14 | Cisco Technology, Inc. | Application performance monitoring and management platform with anomalous flowlet resolution |
US11683618B2 (en) | 2017-03-28 | 2023-06-20 | Cisco Technology, Inc. | Application performance monitoring and management platform with anomalous flowlet resolution |
US10873794B2 (en) | 2017-03-28 | 2020-12-22 | Cisco Technology, Inc. | Flowlet resolution for application performance monitoring and management |
US10680887B2 (en) | 2017-07-21 | 2020-06-09 | Cisco Technology, Inc. | Remote device status audit and recovery |
US11044170B2 (en) | 2017-10-23 | 2021-06-22 | Cisco Technology, Inc. | Network migration assistant |
US10554501B2 (en) | 2017-10-23 | 2020-02-04 | Cisco Technology, Inc. | Network migration assistant |
US10523541B2 (en) | 2017-10-25 | 2019-12-31 | Cisco Technology, Inc. | Federated network and application data analytics platform |
US10904071B2 (en) | 2017-10-27 | 2021-01-26 | Cisco Technology, Inc. | System and method for network root cause analysis |
US10594542B2 (en) | 2017-10-27 | 2020-03-17 | Cisco Technology, Inc. | System and method for network root cause analysis |
US11750653B2 (en) | 2018-01-04 | 2023-09-05 | Cisco Technology, Inc. | Network intrusion counter-intelligence |
US11233821B2 (en) | 2018-01-04 | 2022-01-25 | Cisco Technology, Inc. | Network intrusion counter-intelligence |
US11765046B1 (en) | 2018-01-11 | 2023-09-19 | Cisco Technology, Inc. | Endpoint cluster assignment and query generation |
US10826803B2 (en) | 2018-01-25 | 2020-11-03 | Cisco Technology, Inc. | Mechanism for facilitating efficient policy updates |
US11924240B2 (en) | 2018-01-25 | 2024-03-05 | Cisco Technology, Inc. | Mechanism for identifying differences between network snapshots |
US10574575B2 (en) | 2018-01-25 | 2020-02-25 | Cisco Technology, Inc. | Network flow stitching using middle box flow stitching |
US10873593B2 (en) | 2018-01-25 | 2020-12-22 | Cisco Technology, Inc. | Mechanism for identifying differences between network snapshots |
US10798015B2 (en) | 2018-01-25 | 2020-10-06 | Cisco Technology, Inc. | Discovery of middleboxes using traffic flow stitching |
US10999149B2 (en) | 2018-01-25 | 2021-05-04 | Cisco Technology, Inc. | Automatic configuration discovery based on traffic flow data |
US10917438B2 (en) | 2018-01-25 | 2021-02-09 | Cisco Technology, Inc. | Secure publishing for policy updates |
US11128700B2 (en) | 2018-01-26 | 2021-09-21 | Cisco Technology, Inc. | Load balancing configuration based on traffic flow telemetry |
CN118101526A (zh) * | 2024-04-24 | 2024-05-28 | 沈阳蓝巨人网络科技有限公司 | 基于信息通信技术的安全监测方法及*** |
Also Published As
Publication number | Publication date |
---|---|
CN105517668A (zh) | 2016-04-20 |
CN105517668B (zh) | 2019-05-28 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
WO2016019523A1 (zh) | 识别网络传输拥塞的方法及装置 | |
US9872198B2 (en) | Systems and methods for data transmission | |
WO2019153337A1 (zh) | 网络质量评估方法、装置、网络检测设备及可读存储介质 | |
US9282017B2 (en) | Apparatus and method for analyzing a packet | |
CN106067847B (zh) | 一种语音数据传输方法及装置 | |
US9444755B2 (en) | Packet processing method and packet processing device | |
WO2017133014A1 (zh) | Tcp传输流中基于接收端的网络性能检测方法及*** | |
US10243825B2 (en) | Available bandwidth estimation system, available bandwidth estimation method, reception apparatus, and reception apparatus program storage medium | |
US20190068502A1 (en) | Information processing apparatus, method and non-transitory computer-readable storage medium | |
WO2016086890A1 (zh) | 一种检测连通性的方法和装置 | |
US20140307562A1 (en) | Packet loss counter | |
WO2016049809A1 (zh) | 一种流量控制方法及*** | |
WO2015196385A1 (zh) | 确定传输缓存量的方法和设备 | |
KR101527826B1 (ko) | 데이터 전송 방법, 장치, 및 시스템 | |
US20200136944A1 (en) | Data Transmission Performance Detection | |
TWI658715B (zh) | 通信裝置、可用頻寬計算系統、可用頻寬計算方法及程式 | |
US20150023189A1 (en) | Packet analysis device and packet analyzing method | |
WO2012071851A1 (zh) | 根据网络抖动调整bfd发送间隔的方法及装置 | |
CN113364698B (zh) | 带宽估计方法、***、电子设备与存储介质 | |
US20150271041A1 (en) | Methods and apparatus to determine network delay with location independence from retransmission delay and application response time | |
US10715406B2 (en) | Flow sample | |
CN114666001B (zh) | 一种时间同步***及其多级安全监测方法、设备及介质 | |
US20160147702A1 (en) | Communication control device, method of communicating a frame, and storage medium | |
US20170288814A1 (en) | A transmitter that does not resend a packet despite receipt of a message to resend the packet | |
KR101519952B1 (ko) | 목적지 노드 기반 네트워크 혼잡 감지 장치 및 방법 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 14899506 Country of ref document: EP Kind code of ref document: A1 |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 14899506 Country of ref document: EP Kind code of ref document: A1 |