A Location-Based Prefix Aggregation Method

Abstract

The present invention discloses a location-based prefix aggregation method, aiming at the on-orbit deployment of terrestrial IPv4 prefixes. It converts the expression of the address set from the traditional binary mask prefix to a decimal integer interval, supports precise control of the set. This method iteratively aggregates the prefixes within the corresponding range centered on the ground station, and then corrects the aggregation errors by sorting and constructing non-overlapping intervals. By adopting the technical solution of the present invention, the scale of the location routing table can be reduced, and at the same time, the aggregation effect can be improved, which can ensure that the on-orbit IPv4 data packets are landed nearby, achieving an effect similar to that of the IPv6 location routing technology.

Description

Technical Field

[0001] The present invention relates to the technical field of routing compression, and particularly to a location-based prefix aggregation method. Background Art

[0002] Addressing and forwarding are core issues in the IP architecture. In the integrated space-ground network, due to characteristics such as high dynamics of the topology and multiple constraints of space devices, addressing and forwarding become more complex in such scenarios. The traditional approach is to find the forwarding path within the satellite network, that is, the path from the ingress satellite to the egress satellite, to complete on-board forwarding. However, due to the large number of ground prefixes (reaching 942,420 IPv4 prefixes in 2022), this poses a severe challenge to the integrated network.

[0003] To better solve the routing and addressing problems of the integrated network, some new ideas have emerged in the academic community in the past two years. Among them, the location-based routing and addressing method has remarkable effects. This scheme adopts an IPv6 addressing strategy based on geographical location, and intermediate nodes can directly obtain geographical locations from IPv6 addresses. In addition, this scheme also presents a satellite optimal interface selection algorithm based on the above geographical location information to replace the traditional routing table entries and table lookup process, greatly reducing the scale of table entries and effectively solving the routing and addressing problems. However, the idea of embedding geographical location semantics into addresses still has deficiencies: Although the IPv6 address space is sufficient, it poses higher requirements on the addressing and address allocation institutions and challenges to the standardization work, and it cannot be achieved overnight in terms of actual deployment; and due to the small IPv4 address space, this method is not applicable. Summary of the Invention

[0004] To overcome the problems existing in the related technologies, an embodiment of the present invention provides a location-based prefix aggregation method, which can reduce the scale of the location routing table, improve the aggregation effect at the same time, ensure that IPv4 data packets on the satellite "go to the ground nearby", and achieve an effect similar to that of the IPv6 location routing technology.

[0005] An embodiment of the present invention provides a location-based prefix aggregation method, including the following steps:

[0006] Step 1, input the location routing information table LRIB, the distance set DIS of all adjacent intervals in the location routing information table, the non-overlapping interval table NOIB, and the initial non-overlapping interval table INOIB, iteratively traverse DIS to obtain the minimum value min of the adjacent interval distances and the subscript q of the minimum value min, and obtain the selected interval I in the LRIB q and I q+1 , and set the aggregation threshold to 1000 kilometers;

[0007] Step 2, if the minimum value min of the adjacent interval distance is greater than twice the aggregation threshold, obtain the compressed location routing information table, and the process ends; otherwise, go to Step 3;

[0008] Step 3, aggregate each original location OL in the original locations OLs of I q and I q+1 into an aggregated location AL, and calculate the distance D i between AL and each OL i , where i is a natural number; i

[0009] Step 4, if any D i is greater than 1000 kilometers, assign the q-th node in DIS to 100 * 1000, and return to Step 1; otherwise, go to Step 5;

[0010] Step 5, aggregate I q and I q+1 into I r , delete I q , I q+1 from NOIB, and add I r , then obtain all non-overlapping intervals NOI within the AI range in NOIB, initialize ci = [], which is used to save the corrected interval CI, and initialize n ci = 0, which is used to save the number of CIs;

[0011] Step 6, loop to determine whether NOI j is equal to NULL. If NOI j is not equal to NULL, then search for NOIB and INOIB, obtain the original and aggregated positions of NOI j , calculate the distance D j , and determine whether the distance D j is greater than 1000 km. If the distance D j is not greater than 1000 km, continue to loop to determine whether NOI j is equal to NULL. If the distance D j is greater than 1000, then let n ci be equal to n ci + 1, ci be equal to NOI j , and continue to loop to determine whether NOI j is equal to NULL. If it is determined that NOI j is equal to NULL, then go to Step 7;

[0012] Step 7, determine whether n ci > 1 or n ci == 1 and (ci == I q or ci == I q+1), if the condition is satisfied, restore NOIB, assign the q-th node of DIS to 100 * 1000, go to step 1, otherwise go to step 8;

[0013] Step 8, for I in LRIB r Replace I q , delete I q+1 , update DIS;

[0014] Step 9, determine whether n ci is equal to 1. If so, insert ci into LRIB, update DIS and NOIB, go to step 1, otherwise go to step 1.

[0015] Furthermore, steps 3 and 4 include the following steps:

[0016] Iteratively select intervals at two adjacent positions, merge the included OLs into the position AL where the sum of the distances to the earth's surface of all OLs is the smallest. During each iteration, AL moves to a new center point, and it is required that the distance from AL to all OLs that have participated in aggregation during the entire iteration does not exceed the aggregation threshold.

[0017] Furthermore, the obtaining of the original and aggregated positions in step 6 j includes the following steps:

[0018] According to the longest prefix matching principle, the geographical location of CI is the position of the shortest interval among all current table entries. Finding the position of CI is transformed into finding the shortest interval that contains CI;

[0019] Construct NOIB, which contains two columns, namely the ordered integer sequence OIS and all possible intervals corresponding to it;

[0020] Take the shortest interval in the intersection of the intervals corresponding to the starting and ending OIS values.

[0021] Furthermore, the calculation of the distance D in step 6 j , includes the following steps:

[0022] Arrange the minimum and maximum values of all LRIB intervals in ascending order to form an ordered integer sequence OIS;

[0023] Each pair of consecutive values in OIS forms at least two NOI;

[0024] By verifying the geographical locations of all NOI within each AL range one by one, obtain the incorrect intervals.

[0025] Adopting the technical solution provided by the embodiment of the present invention has the following beneficial effects: By converting the expression mode of the address set from the traditional binary mask prefix to a decimal integer interval and converting the original longest prefix match to the shortest interval match, and aggregating the real IPv4 prefixes of the Internet, the compression ratio is significantly improved.

[0026] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present invention, and are used together with the specification to explain the principles of the present invention.

[0028] Figure 1 It is a flowchart of location-based prefix aggregation in an embodiment of the present invention.

[0029] Figure 2 It is an illustrative diagram of interval aggregation in an embodiment of the present invention.

[0030] Figure 3 It is an illustrative diagram of interval marking and obtaining the positions of non-overlapping intervals in an embodiment of the present invention.

[0031] Figure 4 It is a conversion diagram of non-overlapping intervals in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] Here, the exemplary embodiments will be described in detail, and the examples are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. On the contrary, they are only examples of devices and related applications and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0033] The technical solution of the present invention is to convert the expression mode of the address set from the traditional binary mask prefix to a decimal integer interval and convert the original longest prefix match to the shortest interval match, and aggregate the real IPv4 prefixes of the Internet, so that the compression ratio is significantly improved.

[0034] Figure 1 It is a flowchart of location-based prefix aggregation in an embodiment of the present invention. As Figure 1 shown, the location-based prefix aggregation process further includes the following steps:

[0035] Step 1: Initialize the Location Routing Information Base (LRIB), the Distance Set (DIS) of all adjacent intervals in the location routing information table, the Nooverlapping interval Base (NOIB), and the initial no-overlapping interval table INOIB. Iteratively traverse the DIS to obtain the minimum value min of the adjacent interval distances and the subscript q of the minimum value min. Obtain the selected interval I in the LRIB q and I q+1 , and set the aggregation threshold to 1000 kilometers;

[0036] Step 2: If the minimum value min of the adjacent interval distance is greater than twice the aggregation threshold, obtain the compressed location routing information table and end the process; otherwise, go to Step 3.

[0037] Step 3: Aggregate each original location OL in the original locations (OLs) of I q and I q+1 into an aggregate location (AL), and calculate the distance D i between the AL and each OL i , where i is a natural number. i

[0038] Step 4: If any D i is greater than 1000 kilometers, assign the q-th node in the DIS to 100 * 1000, and return to Step 1; otherwise, go to Step 5.

[0039] Iteratively select the intervals of two adjacent locations (converted from prefixes), and merge the included OLs into the location AL with the smallest sum of the distances to all OLs on the earth's surface. During each iteration, the AL will move to a new center point, and it is required that the distance between the AL and all OLs that have participated in the aggregation during the entire iteration does not exceed the threshold.

[0040] Figure 2 is the analysis diagram of the prefix aggregation problem in the embodiment of the present invention. As Figure 2 shown, the original prefixes are located at three points A, B, and C.

[0041] In the first iteration, for the prefix intervals of adjacent points A and B, take the minimum and maximum values of their intervals and merge them into [224, 231], and the location is E.

[0042] In the second iteration, E and C are used as a new pair of adjacent points, and the new aggregate location AL is B. At this time, it is found that the distance between B and the point C that has participated in the aggregation exceeds 1000 kilometers, and this aggregation is cancelled.​

[0043] Step 5. Aggregate I q and I q+1 into I r , delete I q and I q+1 from NOIB, and add I r . Then, obtain all non-overlapping intervals NOI within the AI range in NOIB, initialize ci = [], which is used to save the correction interval (CI), and initialize n ci = 0, which is used to save the number of CIs.

[0044] Step 6. Loop to determine whether NOI j is equal to NULL. If NOI j is not equal to NULL, then search for NOIB and INOIB, obtain the original and aggregated positions of NOI j , calculate the distance D j , and determine whether the distance D j is greater than 1000 km. If the distance D j is not greater than 1000 km, continue to loop to determine whether NOI j is equal to NULL. If the distance D j is greater than 1000, then set n ci equal to n ci + 1, ci equal to NOI j , and continue to loop to determine whether NOI j is equal to NULL. If it is determined that NOI j is equal to NULL, then go to Step 7.

[0045] The above-mentioned obtaining of the original and aggregated positions of NOI j includes the following steps:

[0046] According to the longest prefix matching principle, the geographical location of CI is the position of the shortest interval among all current table entries. Finding the position of CI is transformed into finding the shortest interval that contains CI.

[0047] Construct NOIB, which includes two columns, namely an ordered integer sequence (OIS) and all possible intervals corresponding to it.

[0048] Take the shortest interval in the intersection of the intervals corresponding to the starting and ending OIS values.

[0049] Figure 3 is an example diagram of interval marking and obtaining the position of non-overlapping intervals in the embodiments of the present invention. As Figure 3As shown, taking the NOI entry [180, 191] as an example, the shortest intersection of all its corresponding intervals is [128, 191, A], and the geographical location of [180, 191] is A. When the interval aggregation causes a change in the LRIB, the intervals in the second column of the NOI table are added or deleted accordingly.

[0050] The above calculation of distance D j , further includes the following steps:

[0051] Arrange the minimum and maximum values of all LRIB intervals in ascending order to form an ordered integer sequence OIS.

[0052] Each two consecutive numerical values in the OIS form no less than two NOIs.

[0053] Obtain the error intervals by verifying the geographical locations of all NOIs within each AL range one by one.

[0054] Figure 4 is the non-overlapping interval conversion graph in the embodiment of the present invention. As Figure 4 shown, if all aggregation errors within the interval [128, 191] are to be obtained, it is only necessary to verify all non-overlapping intervals within 128 - 191, that is, 5 intervals: [128, 160], [160, 167], [167, 176], [176, 180], and [180, 191].

[0055] Step 7, determine n ci > 1 or n ci == 1 and (ci == I q or ci == I q+1 ), if the above conditions are met, restore NOIB, assign the qth node of DIS to 100 * 1000, and go to step 1, otherwise go to step 8.

[0056] Step 8, replace I in LRIB with I r , delete I q , and update DIS. q+1

[0057] Step 9, determine whether n ci is equal to 1. If so, insert ci into LRIB, update DIS and NOIB, and go to step 1, otherwise go to step 1.

[0058] By adopting the embodiment of the present invention, the expression mode of the address set is converted from the traditional binary mask prefix to a decimal integer interval, and the original longest prefix matching is converted to the shortest interval matching, and the real IPv4 prefixes on the Internet are aggregated, so that the compression ratio is significantly improved.

[0059] Other embodiments of the present invention will be readily apparent to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following the general principles of the invention and including known common knowledge or conventional technical means in the technical field not disclosed herein.

[0060] It should be understood that the present invention is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. A location-based prefix aggregation method, characterized in that, it includes the following steps: Step 1, input the Location Routing Information Table (LRIB), the set DIS of distances of all adjacent intervals in the Location Routing Information Table, the Non-Overlapping Interval Table (NOIB), and the Initial Non-Overlapping Interval Table (INOIB). Iteratively traverse DIS to obtain the minimum value min of the adjacent interval distances and the subscript q of the minimum value min, and obtain the selected interval I in LRIB q and I q+1 , and set the aggregation threshold to 1000 kilometers; Step 2, if the minimum value min of the adjacent interval distance is greater than twice the aggregation threshold, obtain the compressed location routing information table, and the process ends; otherwise, go to Step 3; Step 3, take I q and I q+1 in the original positions OLs, aggregate each original position OL i into an aggregated position AL, and calculate the distance D i between AL and each OL i , where i is a natural number; Step 4, if any D i is greater than 1000 kilometers, assign the q-th node in DIS to 100 * 1000, return to Step 1, otherwise go to Step 5; Step 5, aggregate I q and I q+1 into I r , delete I q and I q+1 from NOIB, and add I r . Then, obtain all non-overlapping intervals NOI within the AI range in NOIB, initialize ci = [], which is used to save the corrected interval CI, and initialize n ci = 0, which is used to save the number of CIs; Step 6, loop to judge NOI j whether it is equal to NULL. If NOI j is not equal to NULL, then search for NOIB and INOIB, and obtain NOI j the original and aggregated positions, and calculate the distance D j , judge the distance D j whether it is greater than 1000 km. If the distance D j is not greater than 1000 km, continue to loop to judge NOI j whether it is equal to NULL. If the distance D j is greater than 1000, then let n ci be equal to n ci +1, ci be equal to NOI j , continue to loop to judge NOI j whether it is equal to NULL. If it is judged that NOI j is equal to NULL, then go to Step 7; Step 7, determine whether n ci > 1 or n ci == 1 and (ci == I q or ci == I q+1 ). If the above conditions are met, restore NOIB, assign the q-th node of DIS to 100 * 1000, and go to Step 1; otherwise, go to Step 8. Step 8, replace I in LRIB r with I q , delete I q+1 , and update DIS; Step 9, determine whether n ci is equal to 1. If so, insert ci into LRIB, update DIS and NOIB, and go to Step 1; otherwise, go to Step 1.

2. The location-based prefix aggregation method according to claim 1, characterized in that, Steps 3 and 4 further include the following steps: Iteratively select the intervals of two adjacent positions, and merge the OLs they contain into the position AL with the smallest sum of the distances on the earth's surface of all OLs. During each iteration, AL moves to the new center point, and it is required that the distance between AL and all the OLs that have participated in the aggregation during the entire iteration does not exceed the aggregation threshold.

3. The location-based prefix aggregation method according to claim 1, characterized in that, Obtaining NOI as described in Step 6 j The original and aggregated positions further include the following steps: According to the longest prefix matching principle, the geographical location of CI is the position of the shortest interval among all current table entries. Finding the position of CI is transformed into finding the shortest interval containing CI; Construct NOIB, which includes two columns, namely the ordered integer sequence OIS and all possible intervals corresponding to it; Take the shortest interval in the intersection of the intervals corresponding to the starting and ending OIS values.

4. The location-based prefix aggregation method according to claim 1, characterized in that, The distance D calculated in step 6 j , further includes the following steps: Arrange the minimum and maximum values of all LRIB intervals in ascending order to form an ordered integer sequence OIS; Each two consecutive values in OIS form at least two NOI; Obtain the error interval by verifying the geographical locations of all NOI within each AL range one by one.

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