Active bandwidth estimation methods

The active methods usually estimate the bandwidth by transmitting some back-to-back probing packets at different rates and measure the dispersion. Since dispersion between probing packets is highly correlated with channel capacity, it can be used to calculate

the available bandwidth. Figure 3-1 shows the packet dispersions during the probe packet transmission process [51]. The sender sends out some back-to-back probing packets with the dispersion of which equals the time prepared to transmit a packet with size L. These packets are transmitted on a channel with capacity and the dispersion change to : The receiver will send back ACK frames with length l for each packet. If the receiver treats all the packets in the same way, the dispersion of these ACK framesA_inwill be:

out

in P

A = (3-7) If the time slot is big enough for a data packet, it is also big enough for an ACK frame, so the dispersion of the ACK frames will not be changed. So we could calculate this dispersion at the sender side. Because the relation between the three dispersion times is:

So, if packets are sent only in response to an ACK, the sender’s packet spacing will exactly match the packet time on the link.

Figure 3-1 Packet dispersions P_inand P_out during the probe packet transmission process

However, the packet size and transmission rate of probing packets have a large impact on the accuracy of estimation. Smaller size probing packets generates less interference compared to other traffic [52]. Because of the dependence of the link bandwidth on the transmission rate at the PHY layer which is related to the signal strength, the capacity of the link changes frequently. So some researchers utilize a two-stage algorithm to estimate the available bandwidth which will be shown later.

To increase the accuracy of active bandwidth estimation, the difference between probing traffic and data flow needs to be considered. SLOT [53] provides an accurate and fast convergence active method to estimate end-to-end bandwidth. It uses a two-stage method to estimate the available bandwidth. In the first stage, SLOT transmits packets with different probing time and transmission rate in order to discover a more accurate range for the available bandwidth. In the second stage, SLOT measures the available bandwidth similar as TOPP [54] (Train of Packet Pairs) which uses a linear search method to provide an accurate range for the available bandwidth. The main advantage of these active approaches is that they can provide additional traffic information such as the delay, jitter and packet loss of the estimated link. However, the transmission of back-to-back probing packets generates additional extra traffic load on the network

which may cause performance degradations to existing flows. Besides, they can require a long convergence time for the measurements, and produce low accuracy compared with other bandwidth estimation techniques. To decrease the convergence time, WBest (Wireless Bandwidth Estimation Tool) [55] utilizes packet pairs to estimate the WLAN effective capacity which are related to transmit rate in the first stage. In the second stage, WBest sends a packet train at the effective capacity rate to determine the achievable throughput and to infer the available bandwidth. This method avoids the need for a search algorithm to determine the range of the available bandwidth of the link. The first stage is fast to get the effective capacity rate because the number of supported transmission rates is small (in IEEE 802.11b, the number of supported rates is 2, while in IEEE 802.11g and IEEE 802.11a, the number is 8, which was discussed in section 2.4). In [56], the authors halved the time required by the time of the probing process by only sending probe packets at the receiver side. In a homogeneous network, this could be used to estimate the bandwidth effectively. However, in a heterogonous network, this method only measures the bandwidth of the link from sender to receiver not the bandwidth of the both directions and it could not be used to accurately measure the bandwidth.

There are some other ways to estimate the available bandwidth that are neither passive nor active methods. BART (Bandwidth Available in Real-Time) [57] uses a Kalman filter to estimate the available bandwidth and also the capacity of the bottleneck link.

BART injects probing packets into the target link and measures the one-way dispersion at the receiver side. The Kalman filter is used to estimate the available bandwidth when the probing rate exceeds the current available bandwidth. Yuan etc [58] developed a novel bandwidth estimation method which is based on a mathematical model that combines a TCP throughput model with an IEEE 802.11 DCF model. Packets should not be transmitted if there are some delayed packets in the queue even if the channel was sensed idle. In [59], it includes queue delay when it estimates the bandwidth because packets cannot be transmitted immediately even if the channel is idle if other packets are queued ahead of this packet. However, it is difficult to calculate the

bandwidth related to the queue delay. These methods estimate the available bandwidth in different ways. However they do not consider the impact of the traffic pattern and transmission rate.

The active bandwidth estimation algorithms discussed above don’t focus on the packet transmission process. Otherwise, they utilize probe packets to estimate the available bandwidth. They can be accurate if the probe traffic is similar to the traffic it will transmit. However, because the available bandwidth is related to the transmission rate, the active bandwidth estimation algorithms need to transmit probe packets at all the available transmission rate which will consume more of the precious bandwidth resource. Our proposed bandwidth estimation algorithm does not generate additional traffic. It monitors the available channels and predicts what will happen if the station joins in the channel.

In chapter 4, we will introduce a novel passive available bandwidth estimation method which not only takes into account of the traffic of neighbour stations but also of the traffic itself.