2009 International Conference on Computational Science and Engineering
Implementation of Rate Control in Distributed Wireless Multicast by Neural Network Prediction Naixue Xiong1,2 1 College of Computer Science Wuhan Univ. of Science and Engineering, China. 2 Computer Science, Georgia State Univ., USA
[email protected] Yuanyuan Zeng Dept of Comp. Scie. Univ. College Cork, Ireland
[email protected]
Ma Chao Computer School Wuhan Univ., China
[email protected]
Abstract
Jong Hyuk Park Dept of Comp. Scie. and Engi. Kyungnam Univ., Korea
[email protected]
1 Introduction
Recently considerable efforts have focused on the design of self-adaptive flow control schemes for wireless multicast service. This attention is significantly necessary due to the large scale heterogeneous wireless multicast receivers, especially those with large propagation delays, which means the feedbacks arriving at the source node are somewhat outdated and harmful to the control actions.
Wireless Multicast improves the efficiency of multipoint data distribution by building a distribution tree from a sender to a set of receivers [1]. Recently considerable efforts have focused on the design of self-adaptive flow control schemes for wireless multicast service to guarantee the quality of service (QoS). This attention is significantly necessary due to the large scale heterogeneous wireless multicast receivers, especially those with large propagation delays, which means the feedbacks arriving at the source node are somewhat outdated and harmful to the control actions. The major difficulty in designing of wireless multicast flow control protocols arises from the long and heterogeneous round-trip delays involved in the closed-loop control. In feedback control mechanism, the branch node in a wireless multicast tree consolidates the congestion information received from downstream branches, and transfers the consolidated information to its upstream node. Unfortunately, the reported algorithms [2-4] have some limitations in tacking the above problems. To attack the above problem, the paper [5] describes a novel wireless multicast flow control scheme that is based on the PINN (proportional integrative plus neural network) predictive technique. While this paper does not give the simulation analysis, which is solved in this paper. There are a lot of flow control schemes, which have been done on multi-rate multicast. We survey multicast in the categories of IP multicast, overlay multicast and application layer multicast. We also discuss the multicast mechanisms based on their implementation method, namely, layered multicast and coding multicast.
To solve the above problems, the paper [5] describes a novel, autonomous, and predictive wireless multicast flow control scheme, the so-called proportional, integrative plus neural network (PINN) predictive technique. The final sending rate of the multicast source is the expected receiving rates computed by PI controller based on the consolidated feedback information. The link bandwidth is fairly shared among multiple multicast sessions from different sources, and also shared between multicast flow and CBR flow. They analyze the theoretical aspects of the proposed algorithm, simply show how the control mechanism can be used to design a controller. In this paper, we describe more details on how this controller can support wireless multi-rate multicast transmission based on feedback of explicit rates, and give relevant simulation analysis. Simulation results demonstrate that this PINN scheme optimizes the QoS of wireless multicast networks in terms of fast response, scalability, intra-session fairness, inter-session fairness, and stability of buffer occupancy. Thus, the presented scheme makes the wireless multicast system achieve reliable performance and scalable application in the large scale heterogeneous wireless multicast system.
978-0-7695-3823-5/09 $26.00 © 2009 IEEE DOI 10.1109/CSE.2009.462
Laurence T. Yang Department of Computer Science St. Francis Xavier Univ., Canada
[email protected]
117
IP multicast For IP multicast, the flow rate is adjusted by some interesting heuristics [6-8]. A cost is associated with each network link, based on the aggregate rate of flows going through that link. The receiver calculates the overall network cost by aggregating the costs of links that are on its multicast path. It uses this information to adjust the streaming rate. This scheme is shown to converge to an optimal situation. Overlay multicast To resolve the deployment issues of IP multicast, overlay multicast and application layer multicast [9-12] have been proposed. In both approaches, certain nodes form a virtual network, and multicast delivery structures are constructed on top of this virtual network. Data packets are replicated at participating nodes and delivered through tunnels. Cui et al. [8] addresses the problem of optimal resource allocation in overlay multicast. They propose a distributed algorithm, which maximizes the aggregate utility of all multicast members. This scheme also suffers from degradation in the face of badly broken links to a few receivers - dealing with such situations is a difficult issue. Practically, their solution has to be purely end-host-based in accordance with the design objective of the overlay network. Application layer multicast Application-Layer Multicast protocols [9] implement multicast forwarding functionality exclusively at end-hosts, but they do not change the network infrastructure. Such application-layer multicast protocols are increasingly being used to implement efficient commercial content-distribution [13-14] networks. Layered Multicast Layered Multicast is a generic technique that has been used for achieving multirate multicast (MR-M) (e.g., Li et al. [15] and Liu et al. [16]). In short, a session consists of a basic layer and several enhancement layers, each of which has its own multicast tree. Receivers can receive at different rates, according to the enhancement layers they have joined. Receivers are then responsible for rate adaptation by experimenting with joining/leaving enhancement layers. Much work has been done with receiver-driven layered multicast to solve the problem of network congestion in [1718]. Rubenstein et al. [19] make two fundamental contributions for multi-rate session networks. First, they formally demonstrate the theoretical benefits in terms of fairness of using multi-rate (i.e., layered) sessions, and show that these benefits also exist in networks that support a combination of multi-rate and single-rate sessions. Second, they study the link-overuse of layered protocols, and demonstrate the drawbacks (in terms of fairness and efficiency of using available bandwidth) of having high linkoveruse. They evaluate, analytically and by simulation, the link-overuse caused by various layered schemes, leading to the conclusion that this remains a major issue. McCanne et al. proposed a notable extension, called
Receiver-driven Layered Multicast (RLM) [17], with the major advantage that it takes account of the complete system by including the receivers’ characteristics. So does our scheme. However, Lee et al. [20] point out some major drawbacks of RLM schemes, namely, that none of them pay attention to proof of network-wide inter-session fairness and algorithm stability, as well as the fact that they are inherently subject to coarse-grained layering. Rubenstein et al. [19] have also indirectly identified the negative implications of link-overuse, and directly studied its effect on fair allocations within a network. So it remains debatable whether Layered Multicast schemes can be made sufficiently safe for deployment [21]. In contrast, our algorithm resolves this problem. Coding multicast The introduction of network coding dramatically reduces the computational complexity of finding the maximum multicast rate and the strategy to achieve it. However, the potential of network coding to improve multicast rate is rather limited: bounded by a factor of 2 in theory and usually much smaller in practice [22-23]. The rest of this paper is organized as follows: In Section II, we gives the adaptive flow control model and conclude the adaptive and predictive algorithm PINN based on neural networks and control theory [5]. Section III describes the implementation of the BPNN predictor in detail. In Section IV, we use simulation to validate and evaluate the performance of our scheme. Finally, we conclude our work and discuss further work in Section V.
2 The ADAPTIVE CONTROL MODEL and PINN SCHEME 2.1
The ADAPTIVE MODEL
CONTROL
In a multicast tree, the root of the tree is the multicast source and the destination leave nodes are the multicast receivers. The multicast traffic flows from the source to all receivers along the tree. The tree can be established at connection establishment time, and it can be changed dynamically due to multicast membership change, or network topology change. To analyze the performance and the characteristic of the multicast, we focus on the following system model as shown in Figure 1, where (1 ≤ i < j < N ). We assume that the nodes from D1 to Dj have shorter feedback delays compared to the nodes from Dj+1 to DN . We place the neural network in the branch node that is the upstream node of the longer delay receivers. The network is a connection-oriented one, and time is slotted with the duration [n, n + 1) by the sampling period T . The associated data is transferred by fixed size packets. The buffer occupancy of the ith node is denoted by xi (n) at
118
CBR Receiver
W 0 W 01 W 02
W 02
Node
Multicast Source W 01 S2
W 03
Di
...
W 01
W i W 01 W 02
...
W 1 W 01 W 02
CBR Source S1 Branch
length. The component (xi (n − τi ) − x ¯i ) is the error signal of the buffer occupancy to the targetP buffer occupancy for the ith receiver node, the component τj=1 bj R(n − j) is the sum of the history signals of the sending rate during a round-trip time. In (2), it is seen that, the buffer occupancy of the ith receiver node is measured at the instances (n − τi ), after the feedback delay τi , the BCP reaches the source and the source then takes those buffer occupancy of the destination nodes at time t = n. The pseudocode of the proposed PINN scheme and control parameters analysis are given in paper [5]. Based on the control theory and [5, 24], when
D1
IJ j IJ 01 IJ 02
Dj D j 1
IJ j 1 IJ 01 IJ 03 IJ j 2 IJ 01 IJ 03
...
IJ N IJ 01 IJ 03
D j 2
DN
ai =
BP neural network
PI Controller
(τ + 1)ε − 1 , N
(3)
and jε − 1; (j ≤ τR1 ) jε − 1 − (a1 + a2 + ... + ai ); bj = (τRi < j ≤ τR(i+1) , i = 1, 2, 3, ..., N )
Figure 1. A PINN multicast configuration of single point to multiple points [5].
(4)
and ε < 1/(τ + 1), the controller is stable [24-25]. time slot n and the desired buffer level is denoted by x ¯i . The component N is the total number of receiver nodes. The packet number sending out by the ith receiver node in one interval T is denoted by Li . The ith receiver node has the forward delay τi and the RTD τRi . We further assume that τi and τRi are integers, which are reasonable by adjusting T . We assume τ is the maximum RTD among all the RTDs, and the link delay is dominant compared to other delays such as proceeding delays and queuing delays, etc. Each router schedules the packets in a first-come-first-serviced way. The component R(n) represents the transmission rate of the multicast source at time slot n. Under the above notations and assumptions, as far as Figure 1 is concerned, we normalize the dynamic system by adjusting T , then the buffer occupancy of the ith receiver node is determined by xi (n + 1) = xi (n) + R(n − τi ) − Li .
2.2
3 The PINN Predictive Technique This section puts forward the specific PINN algorithm. We can use the PI controller to compute the rate, which is used to adjust the multicast sending rate. To avoid the feedback noise, our main purpose is to consider the longer feedback delay receiver. Therefore, we place BP neural network in the branch point, which is the upstream of the longer delay receivers, so as to predict the effective buffer occupancy of the downstream receivers. According to the aggregated feedback, the source first computes the expected rate by PI controller, and then adjusts the sending rate. The PINN controller is based on the idea of the closed loop control and provides the on-line control and the training algorithm of neural network. On the basis of these two techniques, the response time is greatly decreased and the feedback noise is avoided in the scheme. Control method with short response time has the following advantages: When the buffer of receiver nodes is close to the threshold, one may notice the sending node to reduce the sending rate and prevent the loss of packets as soon as possible; When the bandwidth increases, the sending node can increase the sending rate quick to enhance the utilization of the bandwidth.
(1)
THE PINN ALGORITHM
Based on the paper [5], the PI controller is updated upon every T epoch by R(n) =
N X i=1
ai (xi (n − τi ) − x¯i ) +
τ X
bj R(n − j)
3.1
j=1
(2) where ai and bj are the proportional and integrative control gains respectively. The component x ¯i is the target queue
The BP Neural Network Architecture
The BPNN algorithm [26] is introduced in this paper as a predictive mechanism. Here a multi-layer feed-forward BP
119
xi (n)
xˆi (n 1)
xˆi (n S )
xi (n 1)
xˆi (n)
xˆi (n S 1)
R(n W k
W 01 W 03 1)
W 03 m S )
W 03 m S 1)
...
R ( n W k W 01
xˆi (n S l 1)
Rˆ (n W k
W 01 W 03 1 S ) Rˆ (n W k
W 01 W 03 2 S ) ...
...
...
R(n W k W 01
...
W 01 W 03 2)
R(n W k W 01 W 03 )
Neural Network
R(n W k
xˆi (n l 2)
Neural Network
R(n W k W 01 W 03 1)
Neural Network
xi (n l 1)
...
...
...
neural network is implemented in wireless multicast control model, and it is composed of input layer, implicit layer and output layer. The implicit layer may be one single layer or multi-layers. The previous layer and the next layer are connected by weight parameter. In this paper, we use BP algorithm, whose principle is as follows: If the output data are reasonable, the algorithm completes; otherwise, the difference information is sent back through the original route with correlative algorithm, and the algorithm adjusts neural network weight and threshold with gradient functions, until the difference information is small enough to be within a reasonable scope [2, 27]. Based on the above theory, we can resolve the redundant problem through setting up active nodes. The specific steps for setting up active nodes and the algorithm of adjusting weight are as follows [2, 27]:
R ( n W k W 01 W 03 m)
Figure 2. The Neural Network Back Propagation S-steps ahead prediction.
Step 1: Set the initial value. Step 2: Input study sample: the input matrix and the output matrix; Step 3: Compute the weights between implicit nodes and input layer nodes. At this time, the weight between implicit node and input layer node is set, and the implicit nodes are set, too. Step 4: Adjust the weight between the implicit layer and output layer, until they satisfy the expected difference. Based on BPNN theory [2, 27], we assume the total goal function (total error signal) is J. If J ≤ ε0 , ε0 is a constant that is small enough and ε0 > 0, then the algorithm is terminated; Otherwise, the difference information is sent back through the original route with correlative algorithm, and the algorithm adjusts neural network BPNN weight and threshold with gradient functions, until the difference information is small enough to be within an expected reasonable difference scope.
3.2
Multi-step Neural Predictive Technique
The following idea of using neural networks is similar motivation to that reported in [28-29], but a more specific and more complete treatment of the multicast model has been developed in our approach. We apply a BPNN predictive technique to determine how a BP-based algorithm satisfies multi-rate multicast data transfer requirement [5]. This section sets xk (n) as the buffer occupancy at time n for the longer backward delay k th (k = j + 1, j + 2, ..., N ) receiver. Thus, in order to predict buffer occupancy efficiently, the neural network model for the above system can be expressed as: x ˆk (n + 1) = fˆ[xk (n), ..., xk (n − l + 1), R(n − τk + τ01 + τ03 − 1), ..., R(n − τk + τ01 +
120
τ03 − m − S)], where xk (n − i) (0 ≤ i < l) is the history buffer occupancy of longer backward delay receiver k, and R(n − j) (τk − τ01 − τ03 + 1 ≤ j ≤ τk − τ01 − τ03 + m + S) is the sending rate of the multicast source. xk (n − i) and R(n − j) are as the scalar input of neural network. S is the number of predictive step, S = τk +1 , and S, m is constant ˆ is the unknown function, which may be exinteger. f[.] pressed by the BP neural network. The explicit mechanism of BP neural network S-step ahead prediction is shown in Figure 2. The value of buffer occupancy xk (n), the history buffer occupancy (xk (n−1), ..., xk (n−l +1)), and the past source sending rates (R(n − τk + τ01 + τ03 − 1), ..., R(n − τk + τ01 + τ03 − m − S)) are used as the known inputs of neural network. Every layer denotes one step ahead predictive, so the buffer occupancy xˆk (n + S) of the k th receiver node with longer backward delay in the output layer is the S-step prediction of xk (n). At the next instant n + 1, we can get new measured value xk (n + 1) and new history measure values: xk (n), ..., xk (n − l + 2), R(n − τk + τ01 + τ03 ), ..., R(n − τk + τ01 + τ03 − m − S + 1) as the next instant inputs of neural network. When the buffer occupancy x ˆk (n + S) of the longer backward delay k th receive node is predicted. By using PI controller and BPNN predictive technique, the PINN scheme can satisfy different wireless musticast services, especially those with large propagation delays.
4 SIMULATION RESULTS To evaluate the performance of the proposed multicast flow control scheme, we focus on the following simulation model (Figure 3), and are mostly interested in analyzing the transient behaviors of the network. In the performance analysis, the duration of response time and steady state are the main concerns. Simulations are carried out over a wide
1m
sec
CBR Receiver 1 Multicast Receivers (D ~ D20 )
1 msec
1m
1
s ec 3m
s ec 3m
1m s ec
s ec 1m
sec
Multicast Source
CBR Receiver 2 Multicast Receivers (D21~ D40)
3 msec 4m
CBR Sources
se c
1
BP neural network
9 msec
s ec 2m
12 m s
ec
CBR Receiver 3 Multicast Receivers (D ~ D ) 41
Figure 4. The sending rate of the multicast source.
60
Figure 3. The simulation model.
Table 1. The Parameters in the Simulation Models notes
τRi (msec)τi (msec) Li (mbps) x ¯i (M b)
di (Km)
R1)
5
3
3
80
600
R21
13
7
4
120
1400
R41
57
29
2
400
5800
15
8
10
800
1600
BPNN
Figure 5. The sum of the three CBR sources sending rates.
range traffic patterns and propagation delays between the branch point and the receiver node representing the LAN (Local Area Network), WAN (Wide Area Network) and the Internet cases. The model for simulation shown in Figure 2 is representative in real multicast networks considering the fact that one can group those nodes together, which have a small variation of time delays and sending rates. One thus specifies those groups with the same delays and the same rate. In the model for simulating, we have devised three groups, namely Group 1 (D1 to D20), Group 2 (D21 to D40) and Group 3 (D41 to D60). We assume the distance between multicast source and receiver i is di . The maximum sending rate of the multicast source is 6 M bps and the sampling time T is 1 msec. The relevant notations and assumptions are listed in the following Table I, and pertain to the simulation. Since the situation of every node in each group is similar, we only choose one node from each group as a representative.
According to the introduced stability test for selecting the control gains, we set ε to be 1/80 and 1/18 for PI controller and PINN controller, respectively. When ε = 1/80 for PI controller, we calculate a1 = a2 = ... = a60 = −11/2400, and [b1 , b2 , b3 , ..., b5 ] = [−79/80, −78/80, −77/80, −76/80, −75/80]. [b6 , b7 , b8 , ..., b13 ] = [−2209/2400, −2179/2400, −2149/2400, −2119/2400, −2089/2400, −2059/2400, −2029/2400, −1999/2400]. [b14 , b15 , ..., b57 ] = [−1958/2400, −1928/2400, −1898/2400, −1868/2400, −1838/2400, ..., −668/2400], where it is clear that b(14+i) = (−1958 + 30i)/2400, (i = 1, 2, 3, ..., 43). When ε = 1/18 for PINN controller, the actual RTD is 13 msec. We calculate a1 = a2 = ... = a60 = −1/270, and [b1 , b2 , b3 , ..., b5 ] = −17/18, −16/18, −15/18, −14/18, −13/18], [b6 , b7 , b8 , ..., b13 ] = [−179/270, −164/270, −149/270,
121
Table 2. The specific description of simulation results Parameter
PINN
PI controller
Steady time of source
150 msec
195 msec
buffer time of Node 1
25 msec
62 msec
buffer time of Node 21
26 msec
51 msec
buffer occupancy, Node 41
24.8 Kb
24.8 Kb
Sending rate, steady
2Mbps
2Mbps
source
Figure 6. The transmit response of buffer occupancy in Receiver 1.
−134/270, −119/270, −104/270, −89/270, −74/270]. We propose to use a direct multi-step neural predictive architecture with multiple three-layer neural networks, wherein the number of the input data, the input neurons and the output neurons are all (L + m + l). There are l terms of buffer occupancy x (l = 8) and (L + m) terms of the total input R. The value of the forward delay from the branch node with neural network to the longer receivers k is τk , the prediction horizon is L = τk + 1, and the control horizon is N = L − τk + 1. One notes that the function of the rate control is processed in the following manner. At the first stage in the time interval [0, τ1 ), the multicast source transmits data packets at the maximum rate, and the receivers have not received any packet because the packets are still on flying during this interval. But in the time interval [τ1 , τR1 ), data packets begin to accumulate in the buffer of the nodes from the first node to the 20th node. Because the consolidation BCPs in the middle branch nodes have not still reached the source, the multicast source still transmits packets at the maximum rate. After a round-trip delay τR1 , the consolidation BCPs with the feedback information from the nodes from the first node to the 20th node reaches the source node, the controller starts to adjust the transmission rate of the source. Owing to the execution of the neural network implemented at the branch point, as soon as the remaining BCPs issued from other receivers and the branch nodes arrive at the source, the integrated control actions govern the source’s sending rate. Subsequently, the source adjusts the sending rate gradually to make the buffer occupancy and the sending rate become steady. In every figure, the broken line expresses the performance of PINN controller, and the solid line stands for the performance of PI controller. Figures 6-8 shows the buffer transient response of the receive node 1, node 21 and node
Figure 7. The transmit response of buffer occupancy in Receiver 21.
41, respectively. The sending rate in the multicast source is shown in Figure 4. From 0 msec to 150 msec, the rate of PINN and PI controller fluctuate drastically, and the rate of PI is with a little larger fluctuation; From 150 msec to 195 msec, the rate of PINN become steady while the rate of PI controller still slightly fluctuate; From 195 msec to 1000 msec, the rate of PINN and PI controller are both in a steady state at 2 Mbps. Their comparisons in the duration of the response time and steady state of buffer occupancy are as follows (Table II): For Figure 5 and Figure 9, during the period from 0 msec to 300 msec, only CBR source 1 sends data at 0.5 Mbps; From 300 msec to 500 msec, the CBR source 2 begins to send data at 0.5 Mbps, so at this time, the total sending rate of CBR source is 1 Mbps; From 500 msec to 700 msec, the CBR source 3 also sends data at 0.5 Mbps, then the total sending rate of CBR source is 1.5 Mbps. The receiver has corresponding delay, and the rate of CBR at the receiver is shown in Figure 9. Figure 5 and Figure 9 demonstrate the performance of CBR traffic when both CBR traffic and multicast traffic
122
(1) The long backward delay in wireless multicast is a bottleneck problem for control theory algorithm. Because the long delay leads to high rank for characteristic polynomial equation, and has great computing complexity. The long delay also causes irresponsiveness of a wireless multicast flow. This paper uses BP neural network to predict the buffer occupancy x ˆk (n + τk − τ01 − τ02 ) caused by the longer delay receiver k. According to Equation (3), we can compute the required multicast source rate R(n) using PI algorithm. Thus the neural network plus control theory method obviously decreases the response delay than only using the control theory. This method is believed to be useful in most real network models and can be used in QoS improvement. (2) To analyze the performance and characteristic of the neural network plus control theory method, we investigate the PI controller based on control theory, then integrate the BP neural network with the PI controller and present the PINN method. Moreover, we manage to simulate these two kinds of case in one simulation scenario, and provide a comparison in simulation figures. Simulation results demonstrate that the proposed scheme optimizes the QoS of wireless multicast networks in terms of fast response, scalability, intra-session fairness, inter-session fairness, and stability of buffer occupancy. Thus, the presented scheme makes the wireless multicast system achieve reliable performance and scalable application in the large scale heterogeneous wireless multicast system.
Figure 8. The transmit response of buffer occupancy in Receiver 41.
(3) This paper introduces the combination method of the neural network prediction and the explicit rate control in multicast, and efficiently improves the system performance. Figure 9. The available rates of CBR Receiver 1, Receiver 2 and Receiver 3.
5 CONCLUSIONS AND FUTURE WORK Recently considerable efforts have focused on the design of Self-adaptive flow control schemes for wireless multicast service to guarantee the QoS.
share the same link. We use CBR sources to create dynamic traffic on the links. The simulation demonstrates that the method is good in terms of dynamic adaptation and friendliness. From the simulation results, compared in terms of the duration of response time and steady state of buffer occupancy and sending rate, the performance of the network implemented by PINN is better than that of PI controller. Control method with short response time has following advantages: when the buffer of receiver nodes is close to the threshold, one may notice the sending node to reduce the sending rate and to prevent the loss of packets as soon as possible; When the available bandwidth increases, the sending node can increase the sending rate timely and enhances the utilization of the bandwidth. The main merits of our approach are summarized as follows:
In this paper, we have given more details on how the PINN controller can be designed to adjust the rates of the data service based on the feedback explicit rate mechanism. Under a variety of load conditions, simulations have been carried out over a wide range traffic patterns (LAN and WAN). Simulation results demonstrate that the proposed PINN controller performs well in the sense that it leads to fast response of the buffer occupancy as well as of the controlled sending rate, small steady overshoots of multicast traffic and good utilization of network links. The PINN controller works better than PI controller. Future research would investigate into the TCP-friendly related issues in wireless multicast flow control along this line of study.
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