Simulations are conducted to evaluate the performance by applying the conventional STA-centric approach, the network-assisted approach and the proposed dynamic load balancing scheme. For the conventional STA-centric approach, an STA always sends its request to the AP with the maximal signal strength. If the AP cannot accept this request, the request is rejected. For the network-assisted approach, the AP actively broadcasts the loads of APs, and advices the STA to request its QoS session to the APs with the minimal load. If all APs that the STA can attach are fully occupied, the service request is rejected. For the proposed dynamic load balancing scheme, an STA first sends its request to the AP with the minimal load. If the request is rejected, the dynamic load balancing scheme is activated to find a feasible load adjustment in order to accommodate the new request. If there is no load adjustment can be performed, the service request is rejected. The reject rate of the new service requests and the overhead which is introduced by employing the proposed method are both investigated. The reject rate of the new service requests is the percentage of new requests which are rejected by APs. The overhead here is defined as the numbers of STAs which are forced to change their serving APs in order to accommodate new requests.
In a simulation, a deployment scenario of a WLAN hotspot is first generated. A deployment scenario means a particular number of WLAN APs which are randomly deployed
in a fixed-size hotspot. To simplify the simulations, an AP only offers one association speed, i.e. 11Mbps, within the coverage area of an AP, which is a 30-meter-radius range. A WLAN hotspot is a 300 meters by 300 meters square area. In our simulations, three different network densities D which are D=1.5, D=3.0 and D=6.0 of WLAN hotspots are considered. The network density D here is defined as the average number of APs that an STA can detect at any location of a WLAN hotspot. For example, IEEE 802.11b/g has three non-overlapped channels, and operators may install IEEE 802.11b/g APs in a hotspot where STAs can hear APs in the three non-overlapped channels, i.e., Channel 1, Channel 6 and Channel 11. In such a deployment, the network density D could be approximately three. For IEEE 802.11a, there are twelve non-overlapped channels, the network density D could be twelve. If a WLAN hotspot installs both IEEE 802.11b/g and IEEE 802.11a APs, the network density D could be up to 15.
After a deployment scenario of a WLAN hotspot is settled, STAs that appear at random locations within a WLAN hotspot and send service requests to APs for establishing QoS connections are generated. An STA requests only one G.711 VoIP session which consumes 80Kbps downlink and 80Kbps downlink bandwidth of a WLAN AP. The length of a VoIP session is randomly generated between one to 30 minutes, and the call occupies the resources for the entire VoIP session. The arrival of the service requests is assumed a Poisson process.
Different arrival rates of the service requests are generated in order to simulate different loads
of APs in the hotspot. The QoSs of service requests from STAs are all identical. Therefore, each AP can support up to eight VoIP sessions concurrently. All simulations are based on the average results which are collected from a total of 100 randomly deployed scenarios of WLAN hotspots.
First, the percentage of service requests which are rejected by APs are evaluated under different loads of APs in a WLAN hotspot and different network densities. Figure 6 shows the reject rate of the service requests under different loads of WLAN APs and D=1.5. It can be learned from the figure while the loads of APs are low, e.g. less than 30%, the reject rates of a system by applying the three approaches are similar and all service requests can be accepted.
When the system load increases, the network-assisted approach and the proposed approach can reduce the reject rate of the service requests. Although, the network-assisted approach and the proposed approach both reduce the reject rate of the service requests, the improvements are marginal. That is because in these deployment scenarios with D=1.5, an STA can detect only one or two APs. It is very difficult for STAs to find many alternative APs to attach. The reject rate of the service requests cannot be improved too much by applying the network-assisted approach. Moreover, the proposed approach has to find STAs which can attach to more than one AP and then the algorithm can find load rearrangements between APs.
Without enough network densities, the benefit that the proposed approach can gain is very limited. Thus, we change the network density from D=1.5 to D=3.0. Figure 7 shows the
simulation results. It can be seen from the figure that both the network-assisted approach and the proposed approach reduce the reject rates of the service requests. The performance is significantly improved while the system is heavily loaded, i.e. 60% to 90%. For the network assisted approach, to select an AP with the minimal load can reduce the service request rate.
The proposed scheme further achieves lower service reject rates than that of the network-assisted approach by rearranging the loads between APs. Simulation results show that 10% improvement compared to the network-assisted approach can be achieved by employing the proposed scheme under the system load is 80%. For STAs can find more APs to attach, the proposed scheme can find more feasible load adjustment paths so that more new service requests can be accommodated when the system load is heavy. If the network density increases to six, the proposed method can further reduce the reject rate of the service requests than the network-assisted approach by 30% under the system load is 90%. Figure 8 illustrates the simulation results. We can conclude that for hotspots with high network densities and are heavily loaded, i.e. 60% to 90%, the proposed mechanism significantly minimizes the reject rate of the service requests.
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Figure. 6. Reject rate of service requests which are rejected under different load conditions and a WLAN hotspot with D = 1.5
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Figure. 7. Reject rate of service requests which are rejected under different load conditions and a WLAN hotspot with D = 3.0
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Figure. 8. Reject rate of service requests which are rejected under different load conditions and a WLAN hotspot with D = 6.0
Then, the overhead by employing the proposed scheme is evaluated. Figure 9 demonstrates the overheads of the proposed scheme in terms of roamed STAs under different network densities. The number of roamed STAs means that the number of existing STAs which have to roam from one AP to another AP in order to accommodate the new requests. It can be seen from Figure 9 that the numbers of roamed STAs increase while the network density and workload increase. This is because the proposed method is especially useful while network load and density are both high. For the system is almost fully loaded, i.e. more than 90%, the proposed method can not improve anymore since all requests are rejected.
influenced for accommodating a new request under D=3.0, and only 2.5 to 4 STAs have to roam from their current APs to other APs under D=6.0.
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Figure. 9. The overheads by employing the proposed scheme for a WLAN hotspot with D = 1.5, 3.0 and 6.0