Figure 17 shows the load of each ASN-GW for 2000 seconds. From Figure 17, we found that the load of the pure R4 mobility is the heaviest among these mobility methods. This is because the pure R4 mobility method never changes MS’s the anchor ASN-GW to release gateway’s load. On the contrary, the load of the pure R3 mobility is the lowest. Using pure R3 mobility method, MSs always change their anchored ASN-GWs. The anchor ASN-GW is also the serving ASN-GW. While ASN-GWs use non-predictive ASN-GW relocation algorithm [12], MSs will be relocated due to the heavy load. We found that using non-predictive method the load of ASN-GW is going up and down frequently, and the load is not stable. Note that load of ASN-GW using the ART-based relocation method is lower than that of non-predictive method. Besides, the load of ASN-GW using the ART-based relocation method is more stable.
Figure 17. ASN-GW loading vs. Time
Table 6 shows that the mean and the variance of each ASN-GW’s load. As previously mentioned, the mean and variance for the load of pure R4 mobility are highest, and they are lowest with pure R3 mobility. Compare to the Non-predictive method, the ART-Based method has lower mean load of ASN-GW. Without a proper strategy to perform relocations, the load of Non-predictive method is unstable.
Therefore, the ASN-GW’s load variance of Non-predictive is high. The variance of load with the ART-Based method is low and close to the variance of load with pure R3 mobility.
Table 6. Mean and Variance of each ASN-GW’s load
(Mean, Variance) ASN-GW1 ASN-GW2 ASN-GW3 ASN-GW4
Pure R3 Mobility (0.447 , 0.013 ) (0.416 , 0.013 ) (0.434 , 0.013 ) (0.425 , 0.013 ) Pure R4 Mobility (0.847 , 0.035 ) (0.836 , 0.034 ) (0.848 , 0.033 ) (0.827 , 0.031 ) Non-Predictive (0.546 , 0.019 ) (0.585 , 0.025 ) (0.538 , 0.018 ) (0.545 , 0.020 ) ART-Based (0.502 , 0.015 ) (0.504 , 0.013 ) (0.485 , 0.013 ) (0.498 , 0.014 ) Table 7 shows that the average numbers of MSs of each four ASN-GWs. The number includes both of anchored MS and serving MS. Among the four methods, the pure R4 mobility has the highest average number of MSs because the pure R4 mobility does not perform any relocation. On the contrary, the number of the pure R3 mobility is the lowest because it does not have any anchored MS. The ARTBR method has lower number than the Non-predictive method because the ARTBR select suitable mobility method according to the load of the ASN-GW but the Non-predictive method only use ASN anchored mobility.
Table 7. Average number of MSs of each ASN-GW
ASN-GW1 ASN-GW2 ASN-GW3 ASN-GW4
Pure R3 Mobility 266 238 253 243
Pure R4 Mobility 442 438 443 438
Non-Predictive 341 346 326 331
ART-Based 334 334 325 320 The relationships between the average throughput and the number of serving MSs for the four methods are shown in Figure 18. In general, more serving MSs the ASN-GW serves more throughput the ANS-GW achieves. Among the four methods, the pure R3 mobility has the highest average throughput. The ART-based relocation
method achieves better average throughput than the non-predictive method. Note that the average throughput for the pure R4 mobility drops down when the number of serving MSs is greater than 200. This is because the ANS-GW is overloaded.
Figure 18. Average throughput vs. number of serving MS
When the ping-pong phenomenon occurs, a R3 tunnel with short lifetime will be constructed. If ping-pong phenomenon happens seriously, the average R3 tunnel lifetime will be short. Thus, we use the lifetime of R3 tunnel to represent the condition of ping-pong phenomenon. Table 8 shows the average R3 tunnel lifetime of the four relocation methods. The average R3 tunnel lifetime of the pure R4 mobility is extremely high because it does not perform any relocation and MSs never change their anchored ASN-GW. Thus, no ping-pong phenomenon will happen. The R3 tunnel lifetime of the pure R3 mobility is shortest because the MS movement triggers relocation and the ping-pong phenomenon occurs seriously. The non-predictive method does not select suitable MS to perform relocation. Therefore, the ping-pong phenomenon is still happened. The R3 tunnel lifetime of the ART-based method is longer than other methods with relocation strategy because our method can choose
proper MSs to do relocations. MSs anchor at the ASN-GW where they have longer ART. The result of average R3 tunnel time shows the ART-based method can avoid the ping-pong phenomenon and every R3 tunnel will be used longer.
Table 8. Average R3 tunnel lifetime Pure R3
Mobility
Pure R4
Mobility Non-predictive ART-based Avg. R3 Tunnel
Lifetime 48.89 (sec) 1,505.45 (sec) 110.75 (sec) 188.88 (sec) Table 9 shows that the delay time of handoff and relocation during the simulation.
The pure R3 mobility has longest delay time because the relocation always trigger by the movement of MSs. The pure R4 mobility never performs relocation, and it does not take time to perform relocation. Thus, the delay of pure R4 mobility is shortest.
The ART method has shorter delay time than the non-predictive method because the ART method has proper strategy to perform relocations and it can avoid unnecessary relocations happening.
Table 9. Handoff and relocation delay ASN Anchored
Mobility
CSN Anchored
Mobility Relocation Average Pure R3 Mobility 0 119,604.58
(sec) 0 4.39
(sec) Pure R4 Mobility 56,055.23
(sec) 0 0 2.06
Table 10 shows that the numbers of two mobility management methods used by ASN-GWs. The pure R3 mobility has used CSN anchored mobility for 27,235 times without using any ASN anchored mobility. On contrary, the pure R4 mobility has used ASN anchored mobility for 27,235 times without using any CSN anchored mobility.
The Non-predictive method is always using ASN anchored mobility while MSs handoff and it may perform relocations because of the heavy load of ASN-GW. For this reason, the Non-predictive method has used ASN anchored mobility for 27,235 times and also has performed relocations for 13,216 times. The ART-based selects suitable mobility method and properly performs relocations. Therefore, the ART-based method has used ASN anchored mobility for 17,844 times, 9,391 times for using CSN anchored mobility, and 1,874 times for relocations.
Table 10. Numbers of mobility used and relocation of each method ASN Anchored
Mobility
CSN Anchored
Mobility Relocation
Pure R3 Mobility 0 27,235 0
Pure R4 Mobility 27,235 0 0
Non-predictive 27,235 0 13,216
ART-based 17,844 9,391 1,874
We use the number of handoff control message as a criterion for the overhead of each four methods. According to the fully controlled handoff procedure of WiMAX End-to-End Network Systems Architecture [4], the number of handoff control message of ASN anchored mobility, CSN anchored mobility and relocation are showing at Table 11.
Table 11. Numbers of handoff control message
From table 11 we can find out that there are 24 messages will be transmitted between network components when MSs using ASN anchored mobility during inter-ASN handoff. CSN anchored mobility has 32 messages should be handled when MSs move between ASNs. Compare to ASN anchored mobility, there are eight additional messages for CSN anchored mobility and these extra messages are used for MSs to change their anchored ASN-GW. The eight extra messages also are the messages should be transmitted when an ASN-GW performs the relocation. Two of the extra messages are transmitted between ASN-GW and CSN to register the new Mobile IP tunnel and the registration may take a moment to complete. We apply the numbers of handoff control messages to the number of mobility method used in the experiment and calculate the total number of handover control message of each four methods. The table 12 shows the overhead of four methods.
Table 12. Numbers of handoff control message between network components Pure R3
Mobility
Pure R4
Mobility Non-predictive ART-based
MS ↔ BS 81,705 81,705 81,705 81,705
BS ↔ ASN-GW 381,290 381,290 381,290 381,290 ASN-GW ↔ ASN-GW 354,055 190,645 269,941 258,235
ASN-GW ↔ CSN 54,470 0 26,432 22,530
As the table 12 showing, with the same movement data in the experiment, the number of handoff control message of four methods between MS, BS, and ASN-GW are equal. Among the four methods, the pure R3 mobility has to deal with the most handoff control message between ASN-GWs and between ASN-GW and CSN because MSs change their anchored ASN-GW for every movement. On contrary, the pure R4 mobility handles the lowest number between ASN-GWs of handoff control messages with no relocations. However, the Non-predictive method does not perform the relocations properly and there are many unnecessary relocations will occur. Thus, with Non-predictive method, the number of relocation is large and also the number of the handoff message is high. The ART-based method can select suitable mobility method for the MSs when they are leaving to other ASNs and it also can relocate appropriate MSs to their serving ASN-GW. Therefore, the MSs using ART-based method has lower number to change their anchored ASN-GW and the number of handoff control messages is also low.
Chapter 5 Conclusion
In this thesis, we propose an ASN-GW relocation algorithm based on average residence time (ART-based). Using the ART-based method, ASN-GWs can select suitable mobility method when mobile stations perform inter-ASN handoff. Moreover, it also can select suitable mobile stations to relocate and choose proper timing to perform relocations. The simulation results show that the ART-based method makes the ASN-GWs have lower and stable loading than pure R4 mobility and Non-predictive ASN-GW relocation algorithm. Furthermore, the ART-based also has low overhead than pure R3 mobility and Non-predictive method. In conclusion, the ART-based method is one of the best solutions of trade-off between the pure R3 mobility with stable and lower load and pure R4 mobility with lower overhead handling handoff procedure.
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