As discussed, the distributed optimization problem proposed in Chapter 3 (for single class traffic) and Chapter 4 (for multi-class traffic) has been implemented in the LKM as an extension to the standard IEEE 802.11s set of protocols. The LKM solves the optimization, and finds out the optimum MAF limit such that the required traffic demand can be satisfied. In the testbed driver implementation, the MAF limit is exported as a system level parameter that can be set from the kernel. This feature of the hardware driver is explored to implement the LKM, where the LKM finds out the optimal MAF limit, and sets it to the hardware driver by triggering a interrupt procedure, at the beginning of every DTIM interval. On average, approximately 30 clients are randomly distributed in the testbed area, that connects to the mesh STAs for the Internet access. There are on average 5 different application flows from every client, with both TFTP and FTP as the application traffics. Approximately 20% of the total flows use TFTP traffic, and rest other flows use FTP traffic1.
In the graphs, ‘Direc Sched + ALM’ denotes the proposed directional scheduling along mesh path selection with ALM as the link metric, and ‘Direc Sched + S-ALM’ denotes the proposed directional scheduling along mesh path selection with S-ALM as the link metric (Chapter 3). Further, ‘Direc Sched + S-ALM + QoS’ denotes the service differentiation and call admission control over the scheduling and mesh path selection mechanism, as proposed in Chapter 4.
7.3.1 Single Class Traffic: Performance Improvement
To analyze the performance improvement of single class traffics in a directional mesh environment, based on the proposed scheduling and mesh path selection mechanism, three
1Now-a-days, most of the Internet traffics are TCP traffics. Similar scenario is used in the testbed traffic setup.
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Average MAC Throughput (Mbps)
Router Number IEEE 802.11s Direc Sched + ALM Direc Sched + S-ALM
Figure 7.2: Average MAC Throughput per Router
performance metrics are measured from the packet trace data obtained from the testbed - average per router MAC throughput, average per flow throughput and the fairness index.
Average per router MAC throughput is calculated as the average amount of data sent successfully per second. It can be noted that only successful data transmission is considered for the throughput calculation. Though the actual transmission rate may be much higher, the data may be dropped from the next hop relay due to the buffer overflow from the interface, which is a common problem in the multi-hop mesh path selection, as discussed in Chapter 2. Average per flow throughput is calculated as the average throughput of all the MAC layer flows in the network. For the calculation of the per-flow throughput, only MAC layer protocol overhead is considered. Application layer throughput may be less than the MAC layer throughput, based on the upper layer protocol overheads. Fairness index is measured in terms of the Jain Fairness Index [197].
Figure 7.2 shows the average MAC throughput for the 10 routers deployed in the testbed. Two different variants of the proposed joint scheduling and mesh path selection are implemented, one with the HWMP protocol operated along the ALM metric, and another with the HWMP protocol operated along the S-ALM protocol. As discussed in Chapter 3, S-ALM considers the effect of the interface scheduling while selecting the best forwarding path. The proposed joint scheduling and mesh path selection protocol is compared with the standard IEEE 802.11s set of protocols (MCCA with HWMP). The
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Average Flow Throughput (Mbps)
Time of the Day (Hrs) IEEE 802.11s Direc Sched + ALM Direc Sched + S-ALM
Figure 7.3: Average per Flow Throughput
figure reveals that the variation in the MAC throughput among the routers is very high in case of the standard IEEE 802.11s protocol. According to the figure, while router N1 attends 28 Mbps, router N3 attends only 6 Mbps. As discussed, all the routers in the testbed operate at 54 Mbps data rate. Router N3 is at the farthest hop away from the mesh gate, while router N1 is only in single hop away. That is why most of the packets from router N3 are dropped due to buffer overflow, because of the uneven flow distribution and scheduling in the IEEE 802.11s set of protocols. The proposed scheduling mechanism improves the average performance of the routers in terms of MAC layer throughput. Though the throughput for router N1 degrades a bit, the throughput for router N3 improves significantly. The scheduling and mesh path selection mechanism along with the S-ALM further reduces the variance in the MAC throughput of the routers by distributing the flows evenly in the network, based on selecting the forwarding paths depending on the scheduling information.
The reduction in the deviation in MAC throughput improves the end-to-end flow performance, as can be seen from Figure 7.3. The figure reveals that the average per flow throughput improves significantly for the proposed scheduling and mesh path selection mechanism, compared to the standard IEEE 802.11s protocols. Figure 7.4 shows the improvement in fairness among the flows, in terms of the Jain fairness index. The proposed scheduling mechanism distribute the total bandwidth evenly among the contending routers
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Average Fairness Index
Time of the Day (Hrs) IEEE 802.11s Direc Sched + ALM Direc Sched + S-ALM
Figure 7.4: Fairness Index
based on their traffic load, which in turn improves the end-to-end flow semantics in the network. The S-ALM metric further improves the fairness by selecting the best forwarding path for a new flow admitted in the network. Further, because of the variation in the network interference (interference among the sub-flows of a flow, as well as the interference among different flows), there are frequent drops in the fairness index, as shown in Figure 7.4. Whenever a new flow gets admitted, or an existing flow terminates, the fairness index gets dropped in case of the standard protocols. The proposed scheduling and mesh path selection along with the S-ALM metric reduce the sudden drops in the fairness index by considering the current scheduling information during the path selection procedure.
7.3.2 Multi Class Traffic: Inter-class Service Differentiation and Intra- class Fairness
To analyze the performance of the service differentiation and call admission control mechanism over the scheduling and mesh path selection mechanism proposed in Chapter 3, the QoS support has been compared with the scheme of Chapter 3 with respect to two parameters - average proportional fairness index, and average Jain fairness index. Average proportional fairness index is computed using the similar procedure discussed in Chapter 4.
Average Jain fairness index is computed as follows. Let there areqnumber of traffic classes
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Average Proportional Fairness Index
Time of the Day (Hrs) Direc Sched + S-ALM + QoS
Direc Sched + S-ALM
Figure 7.5: Average Proportional Fairness Index: Inter-class Service Differentiation
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Average Jain Fairness Index
Time of the Day (Hrs) Direc Sched + S-ALM + QoS
Direc Sched + S-ALM
Figure 7.6: Average Jain Fairness Index: Intra-class Fairness T C1, T C2, ..., T Cq. Then,
Average Jain Fairness Index = P
∀T Ci
J Fi
q (7.1)
where J Fi is the Jain Fairness index for the traffic class T Ci.
The standard four class traffic are distributed in the network. Voice over IP (VoIP) is used as the voice traffic, video streaming is used for the video traffic, FTP is used as the background traffic and TFTP is classified as best effort service. 10% of the total traffics is the voice traffic, 20% is the video traffic, 20% is the background traffic, and rest 50% is the best effort traffic.
Figure 7.5 shows the average proportional fairness index for the flows in the network, and Figure 7.6 depicts average Jain fairness index in the network. Proportional fairness index reflects the inter-class service differentiation, while the average Jain fairness index shows the intra-class fairness. Both the figures reveal that the proposed service differentiation and call admission control schemes improve the inter-class service differentiation as well as intra-class fairness in the network.