CN107147535A - A kind of distributed network measurement data statistical analysis technique - Google Patents
A kind of distributed network measurement data statistical analysis technique Download PDFInfo
- Publication number
- CN107147535A CN107147535A CN201710409035.4A CN201710409035A CN107147535A CN 107147535 A CN107147535 A CN 107147535A CN 201710409035 A CN201710409035 A CN 201710409035A CN 107147535 A CN107147535 A CN 107147535A
- Authority
- CN
- China
- Prior art keywords
- data
- probe device
- analytics server
- distributed network
- statistical analysis
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/142—Network analysis or design using statistical or mathematical methods
-
- 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
- H04L43/04—Processing captured monitoring data, e.g. for logfile generation
-
- 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
- H04L43/06—Generation of reports
-
- 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
- H04L43/50—Testing arrangements
Landscapes
- Engineering & Computer Science (AREA)
- Signal Processing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Mathematical Physics (AREA)
- Probability & Statistics with Applications (AREA)
- Pure & Applied Mathematics (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Algebra (AREA)
- Data Mining & Analysis (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
The present invention relates to a kind of distributed network measurement data statistical analysis method, this method is pre-processed using probe device, the two-stage distributed processing mode of data analytics server post processing, and the data analysis rule that probe device is issued according to data analytics server is matched to the initial data of collection, pre-processed and mixing operation；Data after front-end processing result and partial fusion are sent to data analytics server in the form of report message by duties Ethernet after the completion of operation, data analytics server receives the data prediction result and fused data collected after fused data to each probe and post-processed and finally submit user.The present invention can give full play of the disposal ability of probe device on the premise of existing equipment and collocation method is kept, and reach reduction duties net transport overhead, reduction data analytics server computing cost and the target for improving analysis system overall performance.
Description
Technical field
The invention belongs to network communication field, and in particular to a kind of distributed network measurement data statistical analysis technique.
Background technology
The data collection and analysis of express network is analyze data transmission performance, diagnostic network failure, judges packet transmission clothes
One of important means and method of quality of being engaged in.The passive measurement of network data and analyzing and processing are as one kind with expense is small, adopt
Collection measurement is accurate and network measure method the advantages of not influenceing network existing customer service and be used widely.It is general and
Speech, Network Passive Measurement method is realized by disposing probe device and data analytics server equipment in a network.Probe device
Similar to the monitoring camera on highway, the hotspot location for needing to monitor in network and analyzing is attached to, is not influenceing normal
The equipment that the data passed through are acquired and handled in the case of communication.Data analytics server is similar to the traffic in city
The monitoring and scheduling room of administrative center, server is collected, analyzes, handled and each probe device is presented to keeper and collects in real time
Network data.Passive measurement method is operationally acquired and its is untouched not by probe device to the data passed through first
Data analytics server is sent to dynamicly；Data analytics server collects the initial data that probe device reports and to it periodically
It is compared, analyzes and handles, the result finally analyzed is presented to network management personnel.What data analytics server showed
As a result it is usually the network performance index relevant on the whole network time delay, bandwidth and hop count etc..With network size, number of probes with
And the increase of the data traffic on network, traditional passive network measure analysis method faces following Railway Project.
(1) transmission bandwidth can not meet demand
With the increase and the increase of number of probes of network service traffic, the data volume of collection is likely to be breached tens TB amount
Level.The System of such mass data brings huge challenge to network transmission bandwidth.Assuming that once arranging 50 probes
The network measure example of equipment scale, each road G bit interfaces of probe collection 1 are then responsible for the network of real-time Transmission gathered data
Should possess at least 50gbps transfer capability, this is inconceivable in real network.
(2) centralized processing ability can not meet requirement
With the increase and the increase of number of probes of network service traffic, the disposal ability to data analytics server is proposed
Higher and higher to require, the server of common configuration has been difficult the process demand for meeting magnanimity gathered data.Replace and upgrade
The cost more and more higher of data processing server, and it is extremely inconvenient.On the other hand, probe device possesses weak disposal ability and low
Data throughout (flows through the data traffic of each probe device relative to all probes for being aggregated into data processing server
Initial data for it is considerably less) feature, and data analytics server has the spy of strong disposal ability and high data throughput
Levy.
Chinese patent 201220245806.3 discloses a kind of distributed data statistical system, appoints for equalization data processing
Business and raising data-handling efficiency；The system uses standard HTTP, and and the undefined statistics related to network measure
Analysis method, is not suitable at the distributed data analyzing applied to the network measure field being had higher requirements to Data Fusion
Reason.
The content of the invention
It is an object of the invention to provide a kind of distributed network measurement data statistical analysis technique, solve in big data
The network measurement data of amount and multiprobe equipment analyzes and processes problem in real time.
The technical scheme for realizing the object of the invention is：A kind of distributed network measurement data statistical analysis method, including with
Lower step：
Step 1, data analytics server issues flow table；
Step 2, each local data of network probe device for flow through this node is pre-processed；
Step 3, data analytics server carries out fusion treatment according to the pre-processed results for each probe device collected.
Compared with prior art, remarkable advantage of the invention is：
(1) network probe equipment is also equipped with certain storage and computing capability in addition to acquisition function, passes through dispatch service
Instruction, the operations such as filtering, pretreatment, fusion, storage, statistics and readjustment is performed to gathered data, so as to pass through duties network
(network of connection server and probe device) only transmits finite data amount to data analytics server, and then can efficiently utilize
The transmission bandwidth of duties network.
(2) initial data progress of the data analysis rule that probe device is issued according to data analytics server to collection
Match somebody with somebody, pre-process and mixing operation；Data after front-end processing result and partial fusion are sent to by duties net after the completion of operation
Data analytics server, data analytics server receives the data prediction result collected after fused data to each probe and melted
Data are closed to carry out fusion treatment and finally submit user；By distributed two stages for the treatment of mode, duties net is greatly reduced
Bandwidth pressure.
(3) present invention will need the data analysis task scheduling of higher amount of calculation and larger data treating capacity to multiple networks
Performed in probe device, so as to reach computational load the target in a balanced way in the whole network calculate node；For example：User is needed to adopting
Collection data in meet particular constraints data carry out statistical analysis, then data analytics server can by data flow constraints,
The information such as real-time demand and statistical indicator is issued to the probe device specified；Network probe equipment is then according to data analysis service
Real-time streams matching, processing and statistics are carried out to gathered data for the mission requirements that device is issued and most result echo back data divides at last
Analyse server；The present invention can substantially reduce the computation burden of data analytics server, the computing cost of balanced the whole network.
Brief description of the drawings
Fig. 1 is distributed network measurement data statistical analysis method schematic diagram of the present invention.
Embodiment
With reference to Fig. 1, a kind of distributed network measurement data statistical analysis method comprises the following steps：
Step 1, data analytics server issues flow table；
Step 2, each local data of network probe device for flow through this node is pre-processed；
Step 3, data analytics server carries out fusion treatment according to the pre-processed results for each probe device collected.
Further, the pretreatment includes Data Matching filtering, data flux statistics, data delay statistics and data jump
Number statistics.
Further, handed between data analytics server and probe device by the command response mode of master-slave mode
Mutually.
Further, data analytics server issues data stream list to probe device, and data stream list includes identification field, statistics
Domain and operation domain；Identification field is used for identifying data flow interested, and statistics domain is used for record preprocessing statistical result, and operation domain is used
To define operation of the probe device performed by specific data stream.
Further, the data stream list identification field that data analytics server is issued to probe device includes original ip addresses, mesh
Ip addresses, source port number, destination slogan, protocol number and other users make by oneself message data mark.
Further, the data stream list statistics domain that data analytics server is issued to probe device includes duration, number
According to flow, packet stream amount, time delay, hop count and the self-defined statistic of other users.
Further, the method for amalgamation processing of data analytics server includes MAX-MIN operations, additivity operation and multiplying property behaviour
Make these method for amalgamation processing.
Further, the order of data analytics server and probe device interaction mainly includes the order of flow table operation class, number
Class order and collection report type order are taken according to returning, the flow table operation class order includes addition flow table, deletes flow table and change stream
Table, the data, which are returned, takes class order to include back taking statistic order and returning taking data command, and the collection report type order includes
Data acquisition reporting command and statistic reporting command.
Technical solution of the present invention is described in detail below.
The distributed network measurement data statistical analysis method of the present invention is main by interaction protocol, flow table structure and fusion
Reason method and each several part are constituted.
(1) interaction protocol
The distributed network measurement data analysing method framework is related between data analytics server and probe device
Volume of data is interacted, and these data interactions must in order be completed under a unified protocol conventions, so as to realize distribution
Data analysis function.In general, it is master slave relation between data analytics server and probe device, typically by data analysis
Server initiates order, and the mode that probe device carries out response is interacted.Can also be by probe device actively under individual cases
Initiation reports operation.Specific protocol interaction flow distinguishes data analytics server and probe device discussion.
Probe device：
(1) data analytics server order is received；
(2) whether the order can be performedIt is to continue executing with, otherwise jumps to the 4th step；
(3) perform and send confirmation and perform message to data analytics server, jump to the 5th step；
(4) refusal confirmation message is sent to data analytics server；
(5) step of rebound the 1st.
Data analytics server：
(1) user instruction is waited；
(2) user command is sent to probe device；
(3) confirmation of probe device is waited；
(4) it is whether overtimeIt is to continue, otherwise jumps to the 1st step；
(5) whether number of retransmissions is exceededIt is to continue, otherwise retransmits and jump to the 3rd step；
(6) the 1st step is jumped to.
The order that data analytics server is issued in system protocol is mainly carried in command messages, the order that system is supported
Message is as follows：
(1) flow table order is added
Probe device is received after the order, and preserving data stream list in the command messages, (data flow is i.e. for matched data stream
It is that can identify the specific feature of data in network, the attribution data with same characteristic features is in same stream).And mark it to be
The passback in real time of no needs, storage and reply confirm response.
(2) flow table order is deleted
Probe device is received after the order, is deleted corresponding chart and is replied confirmation response.
(3) flow table order is changed
Probe device is received after the order, changes corresponding data stream list, and reply confirmation response.
(4) return and take statistic order
Fetch the statistic result so far corresponding to specific data stream table.Probe device is received after the order, loopback
Statistic report message and slightly band confirmation response.
(5) return and take data command
Fetch all data storages corresponding to specific data stream table so far.Probe device is received after the order, is returned
Send and confirmation response is sent after terminating with all data storages corresponding to the flow table, whole data storage loopbacks.
Following table is command messages explanation：
Table 1
Probe device is received can report data reporting after some orders to data analytics server.Some data reporting quantity
It is huge, it is necessary to which multiple message transmissions, after last end of transmission, probe device can transmit response message to data analysis service
Device.The report message of probe device transmission is as follows：
(1) gathered data report message
Probe device sends gathered data report message finally to be terminated with response message.
(2) statistic report message
Probe device sends statistic report message, and is terminated with response message.
Following table is report message explanation：
Table 2
(2) flow table structure
Distributed network measurement data analysing method of the present invention is stream-oriented real-time data analysis.All probes
The operation such as extraction, pretreatment and statistic record of device for flow be all a series of flow tables for being provided using data analytics server as
Foundation.Each flow table describes mark and the operation of probe device convection current, specifically includes identification field, statistics domain and operation domain.
As shown in table 3, identification field is made up of following field.
Table 3
(1) matching field
Matching field determines carry out traffic identifier using which region of header.General standard area includes source IP
Address, purpose IP address, source port, destination interface and protocol fields.Can be with for some users for having a special scene demand
Some custom fields are added in flow table.Custom field allows user to be named using customized mode, but can not be with mark
Pseudo header field is repeated.
(2) largest field length
The field defines the maximum length for flowing matching field, and the maximum matching length of standard header field is fixed
's.The maximum matching length of such as source IP address is 32 bits.The maximum matching length that user makes field by oneself has user's definition, but
It is that must not exceed the effective length with collection message.
(3) logic decision field
The value of the field determines the logical relation of the matched rule and the matched rule of other records of corresponding record.Example
Such as：The total matched rule of some flow table is that source IP is A and purpose IP is B, then the source ip addresses in the field of awareness of failing to be sold at auction of the flow table
The logic decision domain of record is " and ", and the logic decision domain of purpose ip addresses record is also " and ".
(4) span field
The field determines the span of corresponding record matching field.As for some data that probe device is collected
Value with header fields falls into the span, then represents the message and belong to the stream, be otherwise not hit by.Span is with a series of
Data acquisition system is represented.Such as { (3,15), (125,1024) } etc..In addition, if this field is not provided with any matching filtering, then
It could be arranged to whole receiving (ALL).
(5) offset and deflected length field
The title of standard agreement matching area inherently represents matching field location in the header, for example：
Source ip addresses are the 4-7 byte of ip headers.But the position of User Defined matching field is typically in self-defined application layer
Head is, it is necessary to the manual specified location of user.Two fields of offset and deflected length, which coordinate, then can specify user to make matching by oneself
The position of field in the packet.Such as user is by the way that the offset and deflected length of self-defined matching field are respectively set to
100 and 4, then represent user and wish to judge collection message by the data value of 4 bytes started in the 100th byte of message
Whether the stream of the flow table assigned operation is belonged to.
In addition, when the field of awareness of failing to be sold at auction has multiple matching records, its priority is according to self-defined area behind first universal standard region
The order in domain.
Statistics domain is made up of following field：
(1) duration
The statistic represent since list item set up, the stream continue for how long, the duration be last
Reach the due in of message.Data analytics server is performed after the operation for fetching the field, and return value is [TF1…TFN] sequence
Row, the sequence represents the timestamp (sequence) of last bag of the stream of each probe device return, data analytics server
Maximum will be taken to identify duration statistics amount.
(2) data traffic
The statistic represents the flow indicator of the stream so far, and unit is bits per second.Data analytics server is performed
After the operation for fetching the field, return value is [(LP1,TP1)…(LPN,TPN)] data are to sequence, each data pair in the sequence
Youngster represents the time stamp T for same data detected in a particular probe equipmentP1With message length LPN, data analysis
Server will carry out subsequent treatment to identify the statistic to it.
(3) packet stream amount
The statistic represents the flow indicator of the stream so far, and unit is that message is per second.Data analytics server is performed
After the operation for fetching the field, the value that data analytics server is returned is [TP1…TPN] data sequence, every number in the sequence
The timestamp of the specific data detected according to representing in a particular probe equipment, data analytics server will subsequently be located to it
Reason is so as to identify the statistic.
(4) time delay
The statistic represents the time delay index of the end-to-end and point-to-point of the stream so far.Data analytics server is performed
After the operation for fetching the field, the value of return is [T1…TN] data sequence, each data in the sequence represent a specific spy
The timestamp of the special packet detected in pin equipment, data analytics server will carry out subsequent treatment to identify the statistics to it
Amount.
(5) hop count
The statistic represents the hop count index end to end of the stream so far.Data analytics server performs and fetches the word
After the operation of section, the value of return is [TTL1…TTLN] data sequence, each data in the sequence represent a particular probe and set
The ttl field value of the special packet of standby upper detection, data analytics server will carry out subsequent treatment to identify the statistics to it
Amount.
(6) other statistics
The key assignments and data value of other self-defined statistics are user-defined numerical value and processing method.
The main operation and action as probe device performed by specific identifier domain of operation domain, is held by User Defined
OK.
Table 4 is statistics domain key value and data value, and wherein key assignments is the mark of the statistic, can be that flow table is known, can also
It is some package identification (such as IPID).[(LP1,TP1)…(LPN,TPN)] it is (message length, timestamp) data pair sequence,
[TTL1…TTLN] be message in TTL sequences.
Table 4
| Statistic | Key assignments | Data value |
| Duration | Traffic identifier | [TF1…TFN] |
| Data traffic | IP ID | [(LP1,TP1)…(LPN,TPN)] |
| Packet stream amount | IP ID | [TP1…TPN] |
| Time delay | IP ID | [T1…TN] |
| Hop count | IP ID | [TTL1…TTLN] |
(3) method for amalgamation processing
After data analytics server, which performs statistic, fetches operation, probe device is by by the corresponding of generation of sorting
Statistics is to being sent to data analytics server.Now, data analytics server will start (fusion treatment) process of post processing,
The process is to, by the statistic information needed for being exported to user, specific method for amalgamation processing has following a few classes after data processing：
(1) MAX-MIN operates class
The generic operation, which is mainly, takes minimax to operate the data set for belonging to some key assignments, so as to realize the meter of statistic
Calculate.For example for stream duration manipulation, execution is calculated as below data analytics server：
Duration=Max ([TF1…TFN])
Wherein, [TF1…TFN] it is the timestamp that last message of N number of probe device so far is reached.Data point
Maximum acquired by analysis server is the maximum duration of the stream.Other are related to MAX-MIN operation statistics amounts and cover maximum
Message length and minimum hop count etc..
(2) additivity operation class
The generic operation, which is mainly, takes additivity addition and subtraction to operate the data set for belonging to some key assignments, so as to realize statistic
Calculate.For example for time delay operations, execution is calculated as below data analytics server：
Delay=Max ([T1…TN])-Min(T1…TN)
Wherein, [T1…TN] it is between the timestamp information that each probe device is reported, the maximal and minmal value of the sequence
Difference is end-to-end time delay statistic.Similar operation also includes hop count statistic etc..
(3) multiplying property operation class
The generic operation is mainly takes multiplication or divide operations to the data set for belonging to some key assignments, so as to realize statistic
Calculating.For example operated for flow, execution is calculated as below data analytics server：
Wherein, TPNFor the probe device N collection message p reported timestamp information, LPN is the report that probe device N is reported
Literary P length, the length sum of the sequence and the traffic statistics amount that the business of time difference is probe device N.Similar operation is also
Including packet stream amount etc..
In addition, above-mentioned method for amalgamation processing can use the method calculated by value (often to collect a data calculating
Once), it would however also be possible to employ bucket computational methods (take a calculating cycle such as 10 seconds, to the data in the cycle
Result of calculation is the representative statistical result of this time).Bucket method amount of calculation is small in general, counts numerical quantity
Shake is small, can embody systematic steady state characteristic.
Below with reference to the accompanying drawings the present invention is described in detail and in conjunction with the embodiments.
Embodiment
With reference to Fig. 1, a kind of distributed network measurement data statistical analysis technique comprises the following steps：
The first step, configures at least 1 data analytics server and some probe devices in a network.
Second step, data analytics server is matched somebody with somebody according to the input of user configuring by adding flow table order to probe device
Put data stream list.
3rd step, the flow table that probe device issues configuration according to data analytics server is started working, to the number collected
According to being pre-processed and stored and its corresponding statistical value and initial data are stored in probe device.
4th step, data analytics server is fetched according to life according to the operation of user by returning to take statistic order or return
Order, obtains the gathered data and statistic data of some specific or whole probe device.
5th step, probe device receives returning for data analytics server and taken after order, collect local pre-processed results or
Person's initial data.Pre-processed results are reported to data point by probe device by data report message or statistic report message
Analyse server.
6th step, data analytics server receives data-message or the statistic message that probe device is reported, and carries out
Fusion treatment, and the result after fusion treatment is presented to network management user.
Claims (8)
1. a kind of distributed network measurement data statistical analysis method, it is characterised in that comprise the following steps：
Step 1, data analytics server issues flow table；
Step 2, each local data of network probe device for flow through this node is pre-processed；
Step 3, data analytics server carries out fusion treatment according to the pre-processed results for each probe device collected.
2. distributed network measurement data statistical analysis method according to claim 1, it is characterised in that the pretreatment
Including Data Matching filtering, data flux statistics, data delay statistics and data hop count statistics.
3. distributed network measurement data statistical analysis method according to claim 1, it is characterised in that：Data analysis takes
Interacted between business device and probe device by the command response mode of master-slave mode.
4. distributed network measurement data statistical analysis method according to claim 3, it is characterised in that：Data analysis takes
Business device issues data stream list to probe device, and data stream list includes identification field, statistics domain and operation domain；Identification field is used for identifying sense
The data flow of interest, statistics domain is used for record preprocessing statistical result, and operation domain is used for defining probe device for specific data
The performed operation of stream.
5. distributed network measurement data statistical analysis method according to claim 4, it is characterised in that：Data analysis takes
The data stream list identification field that business device is issued to probe device includes original ip addresses, purpose ip addresses, source port number, destination interface
Number, protocol number and other users make by oneself message data mark.
6. distributed network measurement data statistical analysis method according to claim 4, it is characterised in that：Data analysis takes
The data stream list statistics domain that business device is issued to probe device include the duration, data traffic, packet stream amount, time delay, hop count and
The self-defined statistic of other users.
7. distributed network measurement data statistical analysis method according to claim 1, it is characterised in that：Data analysis takes
The method for amalgamation processing of business device includes MAX-MIN operations, additivity operation and multiplying property and operates these method for amalgamation processing.
8. distributed network measurement data statistical analysis method according to claim 1, it is characterised in that：Data analysis takes
The order of business device and probe device interaction mainly includes the order of flow table operation class, data time and takes class order and collection report type life
Order, the flow table operation class order includes addition flow table, deletes flow table and change flow table, and the data, which are returned, takes class order to include back
Take statistic order and return and take data command, the collection report type order includes data acquisition reporting command and statistic is reported
Order.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710409035.4A CN107147535A (en) | 2017-06-02 | 2017-06-02 | A kind of distributed network measurement data statistical analysis technique |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710409035.4A CN107147535A (en) | 2017-06-02 | 2017-06-02 | A kind of distributed network measurement data statistical analysis technique |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107147535A true CN107147535A (en) | 2017-09-08 |
Family
ID=59780448
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710409035.4A Pending CN107147535A (en) | 2017-06-02 | 2017-06-02 | A kind of distributed network measurement data statistical analysis technique |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107147535A (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110209475A (en) * | 2019-05-07 | 2019-09-06 | 北京明略软件系统有限公司 | Collecting method and device |
| CN111327471A (en) * | 2020-02-24 | 2020-06-23 | 中移（杭州）信息技术有限公司 | Network quality analysis method and device, computer equipment and storage medium |
| CN111723128A (en) * | 2020-06-17 | 2020-09-29 | 珠海格力电器股份有限公司 | Energy internet data processing system |
| CN112217819A (en) * | 2020-10-12 | 2021-01-12 | 珠海市鸿瑞信息技术股份有限公司 | Industrial control message semantic analysis auditing method based on double-factor authentication system |
| CN112306028A (en) * | 2020-11-06 | 2021-02-02 | 浙江安点科技有限责任公司 | Comprehensive data acquisition monitoring system based on industrial control network |
| CN113438124A (en) * | 2021-06-07 | 2021-09-24 | 清华大学 | Network measurement method and device based on intention driving |
| CN114039900A (en) * | 2021-11-03 | 2022-02-11 | 北京德塔精要信息技术有限公司 | Efficient network data packet protocol analysis method and system |
| CN114158080A (en) * | 2020-08-17 | 2022-03-08 | 中国电信股份有限公司 | Monitoring method, monitoring device and computer readable storage medium |
| CN114257525A (en) * | 2021-12-21 | 2022-03-29 | 武汉思普崚技术有限公司 | Centralized control platform network management method, device, equipment and storage medium |
| CN114257417A (en) * | 2021-11-26 | 2022-03-29 | 中国南方电网有限责任公司 | Network security data processing method and device, computer equipment and storage medium |
| CN114422309A (en) * | 2021-12-03 | 2022-04-29 | 中国电子科技集团公司第二十八研究所 | Method for analyzing service message transmission effect based on abstract feedback comparison mode |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1145541A1 (en) * | 1998-11-24 | 2001-10-17 | Niksun, Inc. | Apparatus and method for collecting and analyzing communications data |
| EP1751920A1 (en) * | 2004-04-16 | 2007-02-14 | Apparent Networks, Inc. | Method and apparatus for automating and scaling active probing-based ip network performance monitoring and diagnosis |
| CN102143022A (en) * | 2011-03-16 | 2011-08-03 | 北京邮电大学 | Cloud measurement device and method for IP network |
| CN102891779A (en) * | 2012-09-27 | 2013-01-23 | 北京网瑞达科技有限公司 | Large-scale network performance measuring system and method for IP network |
| CN103188112A (en) * | 2011-12-28 | 2013-07-03 | 阿里巴巴集团控股有限公司 | Network flow detection method and network flow detection device |
| CN104065538A (en) * | 2014-07-07 | 2014-09-24 | 北京瑞汛世纪科技有限公司 | Network performance measurement control method, control server and system |
| CN104753732A (en) * | 2013-12-27 | 2015-07-01 | 郭祖龙 | Distribution based network traffic analysis system and method |
| WO2015131597A1 (en) * | 2014-09-17 | 2015-09-11 | 中兴通讯股份有限公司 | Method and device for flow analysis |
-
2017
- 2017-06-02 CN CN201710409035.4A patent/CN107147535A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1145541A1 (en) * | 1998-11-24 | 2001-10-17 | Niksun, Inc. | Apparatus and method for collecting and analyzing communications data |
| EP1751920A1 (en) * | 2004-04-16 | 2007-02-14 | Apparent Networks, Inc. | Method and apparatus for automating and scaling active probing-based ip network performance monitoring and diagnosis |
| CN102143022A (en) * | 2011-03-16 | 2011-08-03 | 北京邮电大学 | Cloud measurement device and method for IP network |
| CN103188112A (en) * | 2011-12-28 | 2013-07-03 | 阿里巴巴集团控股有限公司 | Network flow detection method and network flow detection device |
| CN102891779A (en) * | 2012-09-27 | 2013-01-23 | 北京网瑞达科技有限公司 | Large-scale network performance measuring system and method for IP network |
| CN104753732A (en) * | 2013-12-27 | 2015-07-01 | 郭祖龙 | Distribution based network traffic analysis system and method |
| CN104065538A (en) * | 2014-07-07 | 2014-09-24 | 北京瑞汛世纪科技有限公司 | Network performance measurement control method, control server and system |
| WO2015131597A1 (en) * | 2014-09-17 | 2015-09-11 | 中兴通讯股份有限公司 | Method and device for flow analysis |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110209475A (en) * | 2019-05-07 | 2019-09-06 | 北京明略软件系统有限公司 | Collecting method and device |
| CN111327471A (en) * | 2020-02-24 | 2020-06-23 | 中移（杭州）信息技术有限公司 | Network quality analysis method and device, computer equipment and storage medium |
| CN111723128A (en) * | 2020-06-17 | 2020-09-29 | 珠海格力电器股份有限公司 | Energy internet data processing system |
| CN111723128B (en) * | 2020-06-17 | 2023-06-30 | 珠海格力电器股份有限公司 | Energy internet data processing system |
| CN114158080A (en) * | 2020-08-17 | 2022-03-08 | 中国电信股份有限公司 | Monitoring method, monitoring device and computer readable storage medium |
| CN114158080B (en) * | 2020-08-17 | 2024-03-01 | 中国电信股份有限公司 | Monitoring method, device and computer readable storage medium |
| CN112217819A (en) * | 2020-10-12 | 2021-01-12 | 珠海市鸿瑞信息技术股份有限公司 | Industrial control message semantic analysis auditing method based on double-factor authentication system |
| CN112217819B (en) * | 2020-10-12 | 2021-04-27 | 珠海市鸿瑞信息技术股份有限公司 | Industrial control message semantic analysis auditing method based on double-factor authentication system |
| CN112306028A (en) * | 2020-11-06 | 2021-02-02 | 浙江安点科技有限责任公司 | Comprehensive data acquisition monitoring system based on industrial control network |
| CN113438124B (en) * | 2021-06-07 | 2022-05-06 | 清华大学 | Network measurement method and device based on intention driving |
| CN113438124A (en) * | 2021-06-07 | 2021-09-24 | 清华大学 | Network measurement method and device based on intention driving |
| CN114039900A (en) * | 2021-11-03 | 2022-02-11 | 北京德塔精要信息技术有限公司 | Efficient network data packet protocol analysis method and system |
| CN114257417A (en) * | 2021-11-26 | 2022-03-29 | 中国南方电网有限责任公司 | Network security data processing method and device, computer equipment and storage medium |
| CN114422309A (en) * | 2021-12-03 | 2022-04-29 | 中国电子科技集团公司第二十八研究所 | Method for analyzing service message transmission effect based on abstract feedback comparison mode |
| CN114422309B (en) * | 2021-12-03 | 2023-08-11 | 中国电子科技集团公司第二十八研究所 | Service message transmission effect analysis method based on abstract return comparison mode |
| CN114257525A (en) * | 2021-12-21 | 2022-03-29 | 武汉思普崚技术有限公司 | Centralized control platform network management method, device, equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107147535A (en) | A kind of distributed network measurement data statistical analysis technique | |
| EP2429128B1 (en) | Flow statistics aggregation | |
| US7657624B2 (en) | Network usage management system and method | |
| US9531620B2 (en) | Control plane packet traffic statistics | |
| CN109787833B (en) | Network abnormal event sensing method and system | |
| CN101933290B (en) | Method for configuring acls on network device based on flow information | |
| US6321264B1 (en) | Network-performance statistics using end-node computer systems | |
| US20050216241A1 (en) | Method and apparatus for gathering statistical measures | |
| CN104243240B (en) | SDN (self-defending network) flow measuring method based on Open Flow | |
| CN103947156B (en) | Method, apparatus and communication network for root cause analysis | |
| CN110312279A (en) | A kind of monitoring method and device of network data | |
| CN103081403A (en) | Method and apparatus for analysis of the operation of a communication system using events | |
| CN104378264B (en) | A kind of virtual machine process flux monitoring method based on sFlow | |
| CN101933313A (en) | Method of resolving network address to host names in network flows for network device | |
| CN103414608B (en) | Rapid web flow collection statistical system and method | |
| CN102106101A (en) | System and method for monitoring and analyzing network traffic | |
| CN105608517B (en) | Business transaction performance management and visualization method and device based on flow | |
| CN108989136A (en) | Business end to end performance monitoring method and device | |
| CN109714266A (en) | A kind of data processing method and the network equipment | |
| US20100165859A1 (en) | Sorting flow records into analysis buckets | |
| CN110113205A (en) | A kind of network troubleshooting system and its working method based on software defined network technology | |
| CN110430070A (en) | A kind of service state analysis method, device, server, data analysis equipment and medium | |
| CN107820270A (en) | A kind of GPRS interface monitor systems based on GSM R networks | |
| CN106161339B (en) | Obtain the method and device of IP access relations | |
| CN103678092B (en) | log analysis method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170908 |
|
| RJ01 | Rejection of invention patent application after publication |