Keiichi Takagaki (email@example.com)
Graduate School of Engineering Science, Osaka University
Hiroyuki Ohsaki (firstname.lastname@example.org)
Graduate School of Engineering Science, Osaka University
Masayuki Murata (email@example.com)
Graduate School of Engineering Science, Osaka University
- 1 Introduction
- 2 Analytic Model
- 3 Stability Analysis
- 4 Numerical Examples
- 5 Simulation Results
- 6 Conclusion and Future Work
Abstract:A feedback-based congestion control mechanism is essential to realize an efficient data transfer service in packet-switched networks. TCP (Transmission Control Protocol), a sort of feedback-based congestion control mechanism, has been widely used in the current Internet. Recently-proposed TCP Vegas is another version of TCP mechanism, and achieves much better performance than current TCP Reno. In this paper, we focus on a window-based flow control mechanism based on the congestion avoidance mechanism of TCP Vegas, and analyze its stability using a control theoretic approach. The main objective of this paper is to analyze the dynamics of the window-based flow control mechanism when TCP connections have different propagation delays. Through the analysis, we show that the system can be stabilized by choosing the control parameter of each connection proportionally to its round-trip propagation delay.
A feedback-based congestion control mechanism is essential to realize an efficient data transfer services in packed-switched networks. TCP (Transmission Control Protocol), a sort of feedback-based congestion control mechanisms, has been widely used in the current Internet. In this paper, we are devoted to studying the functionality of a congestion control mechanism of TCP, which controls a congestion level of the network by regulating a window size of a source host according to feedback information obtained from the network (via the receiver host).
Recently, another version of TCP called TCP Vegas has been proposed by Brakmo et al., which can achieve better performance than TCP Reno . TCP Vegas has following advantages over existing TCP Reno: (1) a new time-out mechanism, (2) an improved congestion avoidance mechanism, and (3) a modified slow-start mechanism. In particular, the congestion avoidance mechanism of TCP Vegas controls the number of on-the-fly packets in the network. More specifically, TCP Vegas measures an RTT (Round Trip Time), which is elapsed time from a packet transmission to the receipt of its corresponding ACK (ACKnowledgment) packet. It then uses the measured RTT as feedback information from the network. Namely, if the measured RTT is getting large, the source host of TCP Vegas conjectures that the network is falling into congestion. Then, the window size is throttled. If measured RTTs become small, on the other hand, the source host recognizes that the network is relieved from the congestion, and increases the window size again. In TCP Vegas, it is not necessary for the source host to wait a packet loss in the network to detect congestion. This is an advantage of TCP Vegas over other versions of TCP. With this mechanism, the window size of a source host is expected to converge to a constant value in steady state. The simulation and experimental results show that the congestion control mechanism of TCP Vegas leads to 37-71 % higher throughput than that of TCP Reno .
Dynamics of TCP Vegas has been analytically investigated by several researchers [2,3,4]. In those papers, the evolution of a window size has been approximated by a fluid model, and the throughput of each connection has been obtained. However, the analytic model used in those papers has focused only on a single connection [2,3], or two connections . Therefore, those results are not applicable to a real network. In addition, in the above papers, stability of TCP Vegas has not been investigated at all. Since TCP Vegas is essentially a feedback congestion control, a stable operation of the control mechanism is very important, but the approach based on the fluid model cannot examine such an aspect.
One exception can be found in , where the authors explicitly derive the stability condition and optimal setting of control parameters by applying the control theory. Control parameters for best transient performance can also be investigated by their approach. Further, the analytic model considered in  allows multiple TCP connections. However, the authors assume a single bottleneck link, and a more important shortcoming is an assumption on the propagation delays; all connections have identical propagation delays. Since in the real network, each connection usually has a different propagation delay, and the difference of feedback delays must affect the stability and performance of TCP connections.
As a next step for making the control theoretic approach to be useful for the network using the feedback-based congestion control (i.e., the Internet), we consider a network where each connection is allowed to have a different propagation delay by extending a previous work . First, we derive fixed points of the window size and the number of the packets in the router's buffer in steady state. Based on these results, we then derive throughput of each connection in steady state, and show that the throughput of each connection become fair regardless of its propagation delay if we set a control parameter that controls the number of on-the-fly packets equally. We also derive the stability condition of the window-based flow control mechanism. Then, we quantitatively show how the stability region of control parameters is affected by network parameters such as the processing speed of the router and the propagation delay.
Analyses of feedback-based congestion control mechanisms using control theoretic approaches can be found in the literature. In particular, several papers including [6,7,8] have analyzed a feedback-based congestion control mechanism for the network model where each connection has a different propagation delay. The authors of these papers have focused on a rate-based congestion control mechanism in ATM networks, and have designed a rate controller based on the optimal control theory. However, their approaches are not applicable to the window-based flow control mechanism since the behaviors of a rate-based and a window-based congestion control mechanisms are essentially different.
Organization of this paper is as follows. In Section 2, we describe the window-based flow control mechanism based on the congestion avoidance mechanism of TCP Vegas, followed by introduction of its analytic model. In Section 3, stability analysis of the window-based flow control mechanism is performed by applying control theory. In Section 4, the effect of control parameters on system stability is investigated by illustrating several numerical examples. The numerical results presented in Section 4 are validated by using simulation experiments in Section 5. In Section 6, we conclude this paper and discuss future works.
2. Analytic Model
The analytic model used in this paper is illustrated in Fig. 1. In this figure, several TCP connections are established through a single bottleneck router. There are M groups of connections where connections in each group are assumed to have an identical propagation delay. Let be the propagation delay of connections in group m . We assume without loss of generality. We introduce an irreducible positive integer as the ratio of propagation delay , such that
By assuming that the waiting time of a packet at the router's buffer is negligible, the ratio of RTTs for connections in group m is given by . In TCP Vegas, a source host changes its window size once per RTT . Therefore, the system can be represented by a discrete-time model where its time slot is . In other words, connections in group m change their window sizes every slots.
Since the window-based flow control mechanism allows the source host to emit the number wm,n(k) of packets per RTT, a connection in group m sends the number of packets per slot on the average. Therefore, the number of packets in the router's buffer at slot k + 1 is given by the following equation
where is the length of a single slot.
In TCP Vegas, the source host measures its RTT and changes its window size based on the observed RTT. More specifically, source host n in group m calculates
from its measured RTT rm(k). Note that rm(k) is dependent on the number of packets in the router's buffer, and given by the sum of the propagation delay and the waiting time at the router's buffer. That is,
The source host changes its window size once every RTT according to dm,n(k). Namely, the window size is changed as
where and are control parameters that determine the number of on-the-fly packets in the router's buffer. In this paper, we modify Eq. (2) as
where is a control parameter that determines the amount of the window size change. The purpose of introducing is not only for enabling application of a control theory, but also for improving transient performance . In , it has been reported that fairness among connections cannot be satisfied when dm,n(k) lies in [, ]. In our analytic model, we therefore unify both and in Eq. (2) into a single one, , as in Eq. (3). With this modification, fairness among connections can be improved . Intuitively, controls the number of on-the-fly packets in the network for each connection.
3. Stability Analysis
In what follows, we assume that initial values of window sizes of all source hosts are identical, and also assume that control parameters of connections in the same group are identical. For brevity, the control parameter of source hosts in group m is represented by . Provided that all source hosts change their window sizes according to Eq. (3), the number of packets in the router's buffer at slot k + 1 is given by
Let wm*, q* and dm* be the fixed points of wm(k), q(k) and in steady state, respectively. By using Eqs. (1), (3) and (4), wm*, q* and dm* can be obtained easily. Let be the difference of the system state from its fixed points at slot k, i.e.,
Since wm(k) is a non-linear equation, we linearize it around the fixed point. By letting be the LCM (Lowest Common Multiple) of , , , can be written as
where is a state transition matrix. Stability and transient behavior of the system around the fixed point is determined by eigenvalues of the matrix . More specifically, the fixed point is locally asymptotically stable if and only if all roots of the characteristic equation D(s) = |sI - A| = 0 lie in the unit circle . It can be easily checked by using the Jury's criterion if the matrix satisfies this condition or not .
In the following, we discuss the case of M = 2 (i.e., two groups of connections) for and (i.e., the ratio of the propagation delays is 1:2) due to space limitation. For the cases of M > 3 or other ratios of propagation delays, the same approach can be easily applied.
In the case of M = 2, , and , the fixed points of the system are given by
When the waiting time at the router's buffer is very small compared to propagation delays, wm* and q* can be approximated as
Equation (9) indicates that the fixed point of the window size wm* is proportional to the processing speed of the router B and the propagation delay , and is inversely proportional to the number of connections N1 and N2. Equation (10) indicates that the fixed point of the number of the packets in the router's buffer q* is the sum of 's of all source hosts. Thus, the number of packets in the router's buffer in steady state can be controlled by appropriately choosing a control parameter
at each source host.
We then derive the throughput of each connection. The throughput of connections in group m is given by
From Eq. (1), we find that dm* converges to in steady state. Using Eq. (3) gives
Hence, the throughput is obtained as
The above equation suggests that the ratio of throughput of connections in groups 1 and 2 is simply determined by the ratio of and . It means that the ratio of throughput of connections is dependent only on the control parameter , and is independent of other parameters; the propagation delay and the number of connections Nm. If we choose the control parameter appropriately, the router would be fully utilized. In this case, is given by a simple equation.
Therefore, if connections become identical, leading to a fair bandwidth allocation among all connections. However, this sort of fairness can be achieved only when there exists a single bottleneck router in the network. We have shown that the throughput of TCP Vegas is determined not only by a control parameter but also by the number of routers on the path . Therefore, in real networks, it is insufficient to set equally to all connections for achieving a fair bandwidth allocation to connections. For example, should be chosen according to the number of bottleneck router's on the path. However, parameter tuning of for such network configuration is beyond the scope of this paper.
In the case of M = 2, , and , the matrix becomes
where am and bm (m=1, 2) are defined as
4. Numerical Examples
In this section, we present several numerical examples and discuss how the stability region is affected by a choice of control parameters and system parameters. Figure 2 shows boundary lines of the stability region on - plane for several values of processing speed of the router B. In this figure, the following parameters are used: the number of connections N1 = N2 = 10, the round-trip propagation delay = 1 [ms] and = 2 [ms], and the control parameter = = 3 [packet]. The processing speed of the router B is changed from 2 to 2,000 [packet/ms]. This figure means that the system becomes stable when the point (, ) lies inside the boundary line. That is, for the window-based congestion control mechanism to be stable, we should choose the point (, ) in the region surrounded by the boundary line and both x- and y- axes.
Figure 2: Stability region for different processing speeds of the router B ( N1 = N2 = 10, [ms], [packet])
One can find from the figure that the stability region heavily depends on the processing speed of the router B. One can also find that the maximum value of for connections with a large propagation delay is larger than the maximum value of for connections with a small propagation delay. This tendency becomes more noticeable as the processing speed of the router B becomes large. For example, when B = 2,000 [packet/ms], the maximum values of and for stable operation are 2 and 4, respectively. Note that the ratio of and is equal to the ratio of their propagation delays, and . This can be explained as follows. In TCP Vegas, a source host changes its window size once per RTT. Hence, a connection with a larger propagation delay changes its window size less frequently so that it has less influence on system stability. In other words, if a connection with a small propagation delay changes its window size excessively, the system is likely to become unstable. So if 's of all connections are equal, stability of the system is mostly determined by the connection with the smallest propagation delay.
Figure 2 also indicates that when the processing speed of the router B is small, the maximum value of is almost independent of the propagation delay. For example, when B=2 [packet/ms], the system becomes stable if both and are set less than 2. This is because when the processing speed of the router B is very small, the waiting time at the router's buffer is much larger than the propagation delay. Namely, the observed RTT of a connection is not so affected by its propagation delay since the waiting time at the router's buffer is the dominant part of its RTT. Consequently, frequency of the window size change becomes almost same in all connections so that the maximum value of becomes identical.
Figure 3: Stability region for different propagation delays (B = 20 [packet/ms], N1 = N2 = 10, [packet])
Figure 3 shows the stability region for different values of propagation delays from 0.1 [ms] to 100 [ms]. In this figure, values of control parameters and system parameters are equal to those in Fig. 2, whereas the processing speed of the router B is fixed at 20 [packet/ms]. In this figure, the ratio of propagation delays is fixed at 1:2. Namely, is always twice of . By comparing Figs. 2 and 3, one can find that boundary lines in these figures are almost identical. Such a correspondence can be easily explained from Eqs. (6)-(8) and (12). Namely, all B's and 's in these equations take the product form of . This suggests an interesting fact that the same effect is obtained by increasing the processing speed of the router B and by increasing the propagation delay in the window-based congestion control mechanism. Such a characteristic of the window-based flow control mechanism indicates that the bandwidth-delay product, , is one of key factors that determine system stability. Intuitively, the bandwidth-delay product, , represents the number of on-the-fly packets on all transmission links. Our analytic results suggests that the system becomes less stable as the number of on-the-fly packets increases.
We next show the stability region for different numbers of connections N1 and N2 in Figs. 4 and 5, respectively. In these figures, the following parameters are used: the processing speed of the router B=20 [packet/ms], the propagation delay [ms], [ms], [packet]. In Fig. 4, the number of connections N2 is fixed at 10 but N1 is changed from 1 to 1,000. On the contrary, in Fig. 5, N1 is fixed at 10 but N2 is changed from 1 to 1,000. These figures show that the maximum value of for stable operation becomes small as the number of connections becomes large. For example, one can find that the maximum value of becomes small as N1 becomes large in Fig. 4. This phenomenon can be explained as follows. Since RTTs of connections in a group are identical, these connections change their window sizes synchronously. When the number of connections in a group increases, the amount of the window size change becomes large. Therefore, the system tends to become less stable so that should be small for avoiding unstable operation.
Figure 4: Stability region for different numbers of connections N1 (B = 20 [packet/ms], N2 = 10, [ms], [packet])
Figure 5: Stability region for different numbers of connections N2 (B = 20 [packet/ms], N1 = 10, [ms], [packet])
However, Figs. 4 and 5 also show that when and , the system is always stabilized regardless of the number of connections. Since the number of active connections in a real network changes frequently, and since it is difficult to prospect, setting of control parameters of and would be desired for practical purposes. Moreover, it is expected from Figs. 2 through 5 that there exists a region where the system can always be stabilized for any network parameters. By increasing the processing speed of the router B, the number of connections Nm, the propagation delay or the control parameter to infinity, the stability region for any network parameters can be easily found: and . However, to select the optimal point of (, ), we should consider transient performance in addition to system stability. The optimal (, ) that leads to the best transient behavior can be calculated by the same method used in . However, the consideration about setting optimal (, ) for the best transient behavior is our future work.
Finally, we show the dynamics of the window-based flow control mechanism in stable and unstable cases. We choose the processing speed of the router B=20 [packet/ms], and (, ) = (3.0, 4.0) in Fig. 6 (stable case) or (, ) = (4.0, 5.0) in Fig. 7 (unstable case). The other parameters are identical to those used in Fig. 2. In these figures, the evolutions of the window size wm(k) and the number of the packets at the router's buffer q(k) are plotted. These figures are obtained by numerically computing the system state using Eq. (5). The initial values of the window size wm(k) and the number of the packets at the router's buffer q(k) are set to 80 % of the fixed points, w* and q*.
In the stable case (Fig. 6), the system becomes stable in 150 [ms] as expected. However, in the unstable case (Fig. 7), both the window size and the number of the packets at the router's buffer oscillate indefinitely.
5. Simulation Results
In this section, several simulation results are shown for validating our analysis presented in Section 3. We use the following parameters: the processing speed of the router B = 20 [packet/ms], the number of connections N1 = N2 = 1, the propagation delay [ms], [ms], the control parameter [packet]. For the control parameter , we use (, ) = (1.0, 3.0) as a stable case and (3.0, 1.0) as unstable case. Figure 8 shows the stability region for these parameters obtained from our analysis. Dynamics of the windows size wm(k) and the number of packets at the router q(k) are plotted in Figs. 9 (stable case) and 10 (unstable case), respectively.
Using Eqs.(6)-(8), the fixed points, wm* and q*, in this parameter setting should be
From Figs. 9 and 10, the fixed points in simulation experiments are
which shows close values to analytic ones. The difference between analytic and simulation results is possibly caused by our assumptions that the window size change is synchronous and the waiting time at the router's buffer is neglected.
By comparing Figs. 9 (stable case) and 10 (unstable case), it can be found that both the window size wm(k) and the number of packets at the router q(k) oscillate excessively when (, ) is out of the stability region. On the contrary, when (, ) satisfies the stability condition, the dynamics of the system becomes almost stable although there is slight oscillation in the window size wm(k) and q(k).
6. Conclusion and Future Work
In this paper, we have focused on the window-based flow control mechanism based on the congestion avoidance mechanism of TCP Vegas. We have analyzed its behavior in steady state by applying a control theory. We have considered a network model consisting of TCP connections with different propagation delays. We have first derived the fixed points of the window size and the number of packets in the bottleneck router's buffer. We have shown that a fair bandwidth allocation to all connections can be realized by setting 's of all connections identically regardless of the difference of their propagation delays. We have also derived the stability condition of the window-based flow control mechanism, and have investigated the relation between system stability and network parameters through several numerical examples. We have found that the system can be stabilized by choosing the control parameter of each connection proportionally to its round-trip propagation delay. Simulation results have also been presented for validating our analysis.
Our future work is to find the optimal setting of control parameters for achieving reasonable transient performance as well as stable operation of the system. Moreover, dynamics of the window-based flow control mechanism when a new connection is established should also be investigated. Our ongoing research is to analyze more generic network configurations where there exists more than two bottleneck routers in the network.
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