Router machine room level positioning method based on network topology constraint and data mining

Abstract

The invention discloses a router machine room level positioning method based on network topology constraint and data mining, relates to the technical field of IP positioning, and determines the actual position of a router in the topology by combining the structural characteristics of the network topology and the AS peer-to-peer interconnection relationship; the computer room to which the router belongs is accurately determined by utilizing global network paths, computer room facilities and IP-AS relationships through topological constraint and data mining technologies, and high-precision router computer room level positioning is realized. According to the invention, the limitation that the traditional positioning technology can only provide city-level or national-level positioning precision in a dynamic network environment is broken through, and the specific computer room position of the router can be accurately provided. And the accuracy and the reliability of router positioning in the network positioning field are obviously improved.

Description

Technical Field

[0001] The present invention relates to the field of IP positioning technology, and more specifically to a router room-level positioning method based on network topology constraints and data mining. Background Art

[0002] Routers are the hubs of the Internet, responsible for forwarding data packets efficiently and accurately to ensure unimpeded network connections. They are the core equipment for network communications and are essential for achieving global information exchange and Internet functions. At the same time, with the development of big data and artificial intelligence technologies, the accuracy and reliability of IP positioning technology have been significantly improved, and the highest positioning accuracy has even reached the street level. However, the positioning accuracy of important infrastructure routers in the Internet can often only reach the city or country level, and the positioning accuracy can no longer meet the needs of high-precision router positioning.

[0003] Therefore, it is necessary to propose a router room-level positioning method based on network topology constraints and data mining to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to solve the problem that the positioning accuracy of important infrastructure routers in the Internet can usually only reach the city level or national level, and the positioning accuracy can no longer meet the demand for high-precision router positioning.

[0005] In order to achieve the above-mentioned purpose, the present invention specifically adopts the following technical solutions:

[0006] The router room-level positioning method based on network topology constraints and data mining includes the following steps:

[0007] Step 1: Collect different types of data through multiple platforms, and construct basic network data sets including global computer room facility information data set, network path data set, IP and AS mapping relationship data set, IP and Hostname mapping relationship data set, and router alias data set;

[0008] Step 2: Combine the network path dataset collected by global IP, the global computer room facility information dataset, the IP and AS mapping relationship dataset, and the router alias dataset, and use the network topology constraint method to bind and locate the router to a unique computer room facility;

[0009] Step 3: Combine the IP and Hostname mapping relationship dataset and the global computer room facility information dataset to further explore the relationship between the router's geocoding and the computer room location, and improve the binding positioning of the router and computer room facilities.

[0010] Furthermore, the step 1 collects and filters the computer room facility data through a multi-source heterogeneous distributed data collection platform to construct a computer room facility information data set.

[0011] Furthermore, step 1 is based on a distributed detection platform, uses a scamper tool to collect network path data of global IPs, constructs a network path data set, collects and analyzes BGP path data, tracks the AS sequence that the IP prefix passes through, constructs a mapping relationship between IP and AS, and constructs an IP and AS mapping relationship data set.

[0012] Furthermore, the step 1 collects the global IP and Hostname mapping relationship based on Dig recognition and constructs an IP and Hostname mapping relationship data set.

[0013] Furthermore, in step 1, three methods, namely, alias identification based on Midar tool, alias identification based on Kapar tool and alias mining based on Hostname, are used to identify router aliases and construct a router alias dataset.

[0014] Furthermore, the network topology constraint method in step 2 includes two steps:

[0015] Step 21, using the shared machine room information between two ASs and between the AS and the exchange center IXP, constrain the location of the router to achieve preliminary constraints;

[0016] Step 22: Based on the router alias, implement the second router location constraint.

[0017] Furthermore, in step 3, the Hostname information corresponding to the router interface IP and the collected data of the computer room facilities are used to mine the potential relationship between the geographic code, the operator and the computer room facilities, so as to supplement and improve the binding positioning of the router and the computer room facilities.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] 1. The present invention proposes a router room-level positioning method based on network topology constraints and data mining, which combines the structural characteristics of the network topology and the AS peer-to-peer interconnection relationship to determine the actual position of the router in the topology.

[0020] 2. The present invention utilizes global network paths, computer room facilities and IP-AS relationships, and through topological constraints and data mining technology, accurately determines the computer room to which the router belongs, thereby achieving high-precision computer room-level positioning of the router.

[0021] 3. The present invention breaks through the limitation that traditional positioning technology can only provide city-level or national-level positioning accuracy in a dynamic network environment. It can accurately provide the specific room location of the router. It significantly improves the accuracy and reliability of router positioning in the field of network positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the process of the present invention.

[0023] Figure 2 is a flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] like Figure 1 and Figure 2 As shown, a method for locating a router room level based on network topology constraints and data mining includes the following steps:

[0026] Step 1: Collect different types of data through various means, and construct different types of basic network data such as global computer room facility information dataset, network path dataset, IP and AS mapping relationship dataset, IP and Hostname mapping relationship dataset and router alias dataset.

[0027] Through multi-source heterogeneous data collection and filtering technology, we collect information about computer room facilities from multiple public platforms, including PeeringDB, PCH, HE, IXPDB, internetexchangemap, datacentermap, idcquan, baxtel, infrapedia, and OSM platforms. We use an efficient distributed data collection platform to implement customized collection strategies for different data sources, and achieve high-concurrency collection of data from data centers, exchange centers, and telecommunications computer rooms. At the same time, we study and analyze the characteristics of each data source, build a complete computer room facility analysis and filtering model, and construct a global computer room facility information data set through normalization processing.

[0028] Collect global network path data. Based on the distributed detection platform, use the Scamper tool to collect global IP network path data to build a global network path dataset.

[0029] Construct IP and AS mapping relationship datasets and IP and Hostname mapping relationship datasets. By analyzing the BGP path attributes, we can trace the AS (autonomous system) sequence that the IP prefix passes through, thereby clarifying the association between the IP address and the AS and constructing the IP and AS mapping relationship dataset. Based on the Dig tool, we collect the mapping relationship between the global IP and Hostname and construct the IP and Hostname mapping relationship dataset.

[0030] Router alias technology is used to identify router aliases and construct a router alias dataset. Router alias identification technologies include alias identification based on the Midar tool, alias identification based on the Kapar tool, and alias mining technology based on Hostname. Among them, the Midar tool uses the incremental relationship between IP and ID to efficiently identify different interface IPs on the same router to achieve the identification of router aliases. The Kapar tool uses the network topology similarity based on the global network path data topology data to analyze and identify different interface IPs on the same router to achieve the identification of router aliases. The Hostname-based alias mining technology uses the similarity of Hostname based on the IP and Hostname mapping relationship dataset to identify different interface IPs on the same router to achieve the identification of router aliases.

[0031] Step 2: Use the network topology constraint method to bind the router to a unique computer room facility

[0032] First, identify the type of peering. Combining the global network path data set and the IP and AS mapping relationship data set, annotate the AS information to which the router IP belongs, and thus identify the two types of peering in the network path data: indirect peering (peer relationship with the exchange center IXP) and direct peering (peer relationship between two ASs directly connected), such as Figure 2 As shown in ①.

[0033] Secondly, for the two types of peer-to-peer interconnection, the shared computer room information between two ASs and between the AS and the exchange center IXP is used to achieve preliminary constraints and complete the binding and positioning of some routers and computer room facilities. Figure 2 As shown in ②, AS5's equipment room facilities are F6, F7, and F8, and AS6's equipment room facilities are F5, F7, and F9. Therefore, the equipment room facility shared by AS5 and AS6 is F7. In this case, the router interface IP5 under AS5 can be bound and located with the equipment room facility F7. However, the equipment room facilities shared by AS1 and IXP1 are F3 and F4. Therefore, the router interface IP1 under AS1 cannot be bound and located with the only equipment room facility.

[0034] Finally, in the case where the router cannot be bound to a unique computer room facility, the router alias is used for a second constraint. Figure 2 As shown in ③, when it is known that the shared equipment room facilities between AS1 and IXP1 are F3 and F4, it can be concluded through another network path that the shared equipment room facilities between AS1 and AS2 are F2 and F3; at the same time, the router interfaces IP1 and IP2 under AS1 are aliases of each other, and the router interfaces IP1 and IP2 can be constrained to the only equipment room facility F3 again, realizing the binding positioning of the router interface IP1-2 and the equipment room facility F3.

[0035] Step 3: Explore the relationship between Hostname and computer room facilities, and supplement and improve the binding positioning of routers and computer room facilities.

[0036] Combining the IP and Hostname mapping relationship dataset, IP and AS mapping relationship dataset and global computer room facility information dataset, we can explore the potential relationship between geocoding, operators and computer room facilities. For example, the Hostname value in Table 1 is "evoque-wdcl-crl-cog.evoquedcs.com", which means that the geocoding of the router interface IP is "wdcl" and the operator is "Evoque Data Center Solutions". At the same time, from the global computer room facility information dataset, we can know that the operator Evoque Data Center Solutions has a computer room facility named "Evoque_WDCl (Bldg A)-Ashburn". We can bind the router interface IP to the computer room facility "Evoque_WDCl (Bldg A)-Ashburn" to achieve the binding and positioning of the router and computer room facilities.

[0037] Table 1 Router room location table mined based on Hostname

[0038]

[0039]

[0040] against Figure 2 To provide a detailed description, VP represents a probe; IPO, IP1, IP2, IP3, IP4, IP5 and IP6 represent interface IPs of real routers, and IP1 and IP2 are considered to be a pair of router aliases through the alias identification method; AS1, AS2, AS5, AS6 and IXP1 represent operator AS numbers and exchange centers; F1, F2, F3, F4, F5, F6, F7, F8 and F9 represent computer room facilities.

[0041] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. The patent protection scope of the present invention shall be based on the claims. All equivalent structural changes made using the contents of the description and drawings of the present invention should also be included in the protection scope of the present invention.

Claims

1. A router room-level positioning method based on network topology constraints and data mining, characterized in that: The following steps are involved: Step 1: Collect different types of data through multiple platforms, and construct basic network data sets including global computer room facility information data set, network path data set, IP and AS mapping relationship data set, IP and Hostname mapping relationship data set, and router alias data set; Step 2: Combine the network path dataset collected by global IP, the global computer room facility information dataset, the IP and AS mapping relationship dataset, and the router alias dataset, and use the network topology constraint method to bind and locate the router to a unique computer room facility; Step 3: Combine the IP and Hostname mapping relationship dataset and the global computer room facility information dataset to further explore the relationship between the router's geocoding and the computer room location, and improve the binding positioning of the router and computer room facilities.

2. The method for locating router room level based on network topology constraints and data mining according to claim 1, characterized in that: The step 1 collects and filters the computer room facility data through a multi-source heterogeneous distributed data collection platform to construct a computer room facility information data set.

3. The method for locating router room level based on network topology constraints and data mining according to claim 1, characterized in that: The step 1 uses the scamper tool to collect network path data of global IP based on the distributed detection platform and construct a network path data set; By collecting and analyzing BGP path data, tracing the AS sequence that the IP prefix passes through, constructing the mapping relationship between IP and AS, and constructing the IP and AS mapping relationship data set.

4. The method for locating router room level based on network topology constraints and data mining according to claim 1, characterized in that: The step 1 collects the global IP and Hostname mapping relationship based on Dig recognition and constructs an IP and Hostname mapping relationship data set.

5. The method for locating router room level based on network topology constraints and data mining according to claim 1, characterized in that: In step 1, three methods are used: alias identification based on Midar tool, alias identification based on Kapar tool, and alias mining based on Hostname to identify router aliases and construct a router alias dataset.

6. The method for locating router room level based on network topology constraints and data mining according to claim 1, characterized in that: The network topology constraint method in step 2 includes two steps: Step 21, using the shared machine room information between two ASs and between the AS and the exchange center IXP, constrain the location of the router to achieve preliminary constraints; Step 22: Based on the router alias, implement the second router location constraint.

7. The method for locating router room level based on network topology constraints and data mining according to claim 1, characterized in that: In step 3, the Hostname information corresponding to the router interface IP and the collected data of the computer room facilities are used to mine the potential relationship between the geographic code, the operator and the computer room facilities, and improve the binding positioning of the router and the computer room facilities.

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