TCP Based Estimation Method for Loss Control in ...

TCP Based Estimation Method for Loss Control in OBS Networks Mohamed Faten Zhani, Halima Elbiaze and Wael Hosny Fouad Aly Department of Computer Science University of Qu´ebec in Montr´eal Canada

Abstract— Optical Burst Switching (OBS) has been developed as an efficient switching technique for the next generation optical Internet. A critical issue for OBS networks is the burst loss which could occur due to contention and/or insufficient offset time. Burst Loss Ratio (BLR) is used as the main performance parameter in bufferless OBS networks. This paper proposes a new TCP statistics based method to predict the BLR without using any feedback information from the network. The idea is to estimate the BLR based on the TCP statistics available at the edge node. Our proposed BLR prediction method is then integrated into the closed loop feedback control model [1–3] to control the BLR inside the network. Our simulation results clearly show that our proposed method improves the efficiency of the closed loop feedback control model while avoiding the use of any feedback information from the network. Index Terms—Optical Burst Switching, TCP, Feedback Control Theory, ARMA

I. I NTRODUCTION Optical Burst Switching (OBS) is one of the most promising all optical architectures for the next generation Internet. Leveraging the strengths of optical switching technologies, optical burst switching is a potential method by which future optical networks may use the available optical resources more effectively [4]. The OBS network is composed of ingress and egress nodes. At the ingress node, the received data packets that are directed to the same egress node are aggregated into bursts. This operation is performed by the burst manager. The number of burst managers at each ingress node is equal to the number of egress nodes i.e. a burst manager for each destination. The rate by which the bursts are sent into the network is called the burstification rate. Before delivering the data burst, a control packet is sent in order to reserve a free data wavelength for the data burst at each intermediate node. The control packets are sent on dedicated wavelengths, called control wavelengths. The remaining wavelengths, called data wavelengths, are dedicated for burst transmission [5–7]. The time interval separating the control packet and its corresponding data burst is called the offset time [8, 9]. A burst is dropped when a contention happens or when the offset time is insufficient. A contention occurs when the control packet does not find an available data wavelength for the data burst. Various studies have already been made for improving OBS bandwidth efficiency, with several enhancement features for resolving wavelength contention [4, 7, 10, 11].

In order to resolve wavelength contention, five methods have been essentially investigated: (1) deflection routing where a contending burst is switched at an optical cross connect onto an alternate outgoing fiber containing an available wavelength channel, (2) buffering with the use of fiber delay lines (FDLs), (3) wavelength conversion where a lightpath can be made of a contiguous sequence of several wavelength channel, (4) burst segmentation where the contending part of a contending burst is forwarded on a primary path while the non-contending part is forwarded on an alternate path [4, 8], and (5) admission control at the edge nodes where feedback control techniques insure dynamic adjustment of the burstification rate (BR) in order to control the burst loss ratio (BLR) inside the network [1–3, 10, 11]. Our work focuses on admission control where the burst injection rate is adjusted in order to reduce burst contention. The admission control method using feedback control techniques has been shown to be an efficient technique that is able to stabilize the BLR around the desired value [1–3]. This comes at the cost of a periodic feedback information needed by the ingress node from the network in order to estimate the burst loss and to use this information to adjust the controller input parameters. Proposals in this area often require some nodes to have global information about the whole network, which limit the scalability of the used techniques. Having this feedback information is not an easy nor a straight forward process since it needs more complicated nodes and leads to more control traffic in the network especially for larger topologies. In this paper, we propose a new technique to predict the BLR at the OBS edge node, without using any feedback information from the core network. Our approach uses TCP segments’ loss rate which is obtained from TCP statistics. Estimating loss rate at the ingress node is a problem which has been addressed in the previous work focusing on TCP behaviour [12]. However, we show that the estimation differs when using TCP over OBS. We also developed a mapping formula that maps the TCP segment loss rate to the burst loss rate. To the best of our knowledge, no one has tried yet to estimate the BLR at the ingress node. This gives the ability to monitor the network at the edge node. It also makes wide-scale measurements easier than the case of monitoring points on both ends (source and destination nodes). We integrated our approach to our proposed feedback

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control model [1–3]. Applying the new approach improves the controller efficiency while avoiding the feedback network information shortcoming. The enhanced model controls the loss rate of a burst flow precisely in the OBS network. A global network information is not required at any node. The paper is organized as follows. Section II describes the proposed approach to estimate the burst loss ratio. Section III presents the proposed improvement for the control system. The simulation results for NSFNET topology are presented in Section IV. Finally, Section V offers conclusions and suggests future work. II. B URST L OSS R ATIO ESTIMATION BASED ON TCP STATISTICS

The aim of this section is to present how to perform an estimation of the Burst loss ratio between any source and any destination. We first define the estimation method variables. BRsi dj (τ ) is the burst rate from the source si to destination dj at time interval τ th . The time interval is of length I. Hereafter BRsi dj (τ ) will be denoted as BR for simplicity. Same notation is taken for the rest of the variables: • BR : Burst Rate i.e. the number of sent bursts per time interval. • SR : Segment Rate i.e. the number of sent segments per time interval. • BL : Burst Loss Rate i.e. The number of lost bursts per time interval. • SL : Segment Loss Rate i.e. The number of lost segments per time interval. • BLR : Burst Loss Ratio BL (1) BLR = BR • SLR : Segment Loss Ratio SLR = •

SL SR

(2)

SpB : Segment Per Burst

SR (3) BR • Lv : Loss Probability of bursts of size v segments. Among these variables, BR, SR, SpB can be measured easily at the ingress node without the use of feedback from the network. Our goal is to estimate the SLR and BLR (so-called EBLR and ESLR respectively) using only information from the TCP layer (i.e. without using any feedback information from the network). The idea is to exploit the retransmission information reported by TCP in order to estimate the burst loss rate at the OBS layer. The proposed approach does not require any extra complexity in the TCP layer implementation. In fact, in OBS networks, each ingress node examines every IP packet to classify it according to its destination in all cases. In the proposed approach, the OBS layer reads also a single bit in SpB =

the TCP telling if it is a retransmitted packet. This bit can be easily added in the TCP or IP header options. If this bit is set to one, a counter of retransmitted segments RTS(s,d) is incremented. It is the only information preserved in the edge node. We start by estimating the segment loss rate ESL, then we calculate the estimation of the burst loss rate EBL. • Estimation of the Segment Loss Rate (ESL) The first step is to estimate the number of lost segments SL. We assume that the OBS layer is aware of the TCP layer. Thus, it can detect if a sent segment (from the upper layers) is a retransmitted segment. This assumption is realistic and it can be easily implemented using a flag in the options of TCP segments. Thus, the OBS layer can evaluate the number of retransmitted segments RT S per time interval. RT S(τ ) refers to the number of retransmitted segments measured at the τ th time interval. ESL(τ ) is estimated as

ESL(τ − 1) = RT S(τ )

(4)

In order to predict the value of the ESL(τ ), we use the AutoRegressive Moving Average (ARMA). A time series y(t) is an ARMA(p,q) process if it is stationary and if for every t: y(t) = φ1 y(t − 1) + .. + φp y(t − p) +(t) + θ1 (t − 1) + .. + θq (t − q)

(5)

where the φi and θj are constants. (t) are error terms assumed to be independent, identically distributed sampled from a normal distribution with zero mean and finite variance σ 2 . The parameter p denotes the number of lags used by the model and q denotes the number of error terms. In our case, the y(t) is the ESL(τ ). In order to simplify, the parameters p and q are set to 1. The φ1 and θ1 are easily estimated from the last values of collected data using least squares regression[13]. Simulations presented in Section IV show that these parameters are enough to have an accurate prediction. Eq.(5) becomes: ESL(τ ) = φ1 ESL(τ − 1) + (t) + θ1 (t − 1)

(6)

All variables are updated each interval of time I. Ideally, the value of I is a multiple of the retransmission timeout (RTO) since the packets are retransmitted every RTO. • Comparison with previous work In previous works, it has been found that the number of retransmission is not a good estimation for the number of segment loss [12, 14, 15]. We believe that it does not hold for OBS networks. It was also found that there is a discrepancy between retransmits and losses in Reno or Tahoe TCP due to the use of slow start when the retransmission timeout expires [12, 14, 15]. In this case, TCP retransmits some packets even though they were not lost.

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Fig. 1.(a) shows an example of a segment (Seg 1) which has been lost in an optical network (packet or circuit switched). The segment is retransmitted after the RTO. TCP retransmit segments 2 and 3 even though they were not lost. Thus, the number of lost segments is lower than the number of retransmitted segments (SL < RT S). Fig. 1.(b) shows another example where segment 1 is lost in an OBS network. However, the following segments are also lost because they belong to the same burst. Thus, the number of lost segments is equal to the number of retransmitted segments (SL = RT S). Generally, the number of segments sent by a TCP connection before receiving an acknowledgement is equal to the size of the TCP window (W ). This number is usually very low compared to the burst size (for instance, simulations in Section IV show that the average window size is 10 compared to 77 segments in one burst). This increases the probability that the W segments belong to the same burst and increase the probability to have the case of Fig. 1.(b). This shows that, in the case of burst switched networks, the retransmit count will be a good estimation of the number of lost segments. source

Destination

source

S eg 1

RTO

RTO

S eg 3

v max SL = BLR. v.Lv BR v=v

Using Eq.(3), we have v max SL.SpB = BLR. v.Lv SR v=v

S eg 2

S eg 3

S eg 3

SL=1 RTS=3

SL=3 RTS=3

Using Eq.(2), we have vmax SLR = BLR.

(11)

Then, we have BLR = α.SLR

(12)

where α is defined as v.Lv

(13)

(14)

Validation of the estimation The performance criterion used to evaluate the accuracy of the prediction is the coefficient of determination [16]: τ =n 2 2 =0 (BLR(τ ) − EBLR(τ )) (15) R (BLR) = 1 − τ τ =n 2 τ =0 (BLR(τ ) − BLR) •

Burst

Estimation of the burst Loss Rate (EBL) The second step is to express BL using SL then BLR using SLR. Since Lv .BL is the number of lost bursts of size v segments, Then v × (Lv .BL) is the number of segments in the (Lv .BL) bursts. That is, v × (Lv .BL) is the number of lost segments in bursts of size v. Therefore the Segment Loss rate can be expressed by: •

v.(Lv × BL)

SpB

EBLR = α.ESLR

(b) OBS Network

v max

v.Lv

Then, if we are using ESLR instead of SLR, we have

Fig. 1. Sample TCP retransmit pattern in optical network vs. OBS network.

SL =

v=vmin

v=vmin

Lost (a) Optical Network

(10)

min

S eg 3

S eg 2

(9)

min

SpB α = vmax

S eg 2

S eg 1

(8)

By dividing by BR and using Eq.(1), we find

Destination

S eg 1

v.Lv

v=vmin

S eg 1

S eg 2

v max

SL = BL.

(7)

v=vmin

where vmin and vmax are the minimal and the maximal burst sizes respectively expressed in number of segments. We have

BLR is the mean of the real values of BLR(τ ) and n is the number of the estimated values. An R2 (BLR) of 100% indicates that the estimation is perfect. We also evaluate the R2 (SLR) by replacing BLR by SLR in Eq.(15). III. C ONTROLLING BLR USING FEEDBACK C ONTROL The following is a brief description of the components of the control system graphically depicted in Fig.2, where: • OBS Network: The target system to be controlled. • Burstification Rate: The tuning parameter that affects the behavior of the target system. It can be dynamically adjusted. • Burst Loss Ratio: The system’s controlled output parameter which is the parameter that represents an aspect of the target system’s behavior. • Sensor: Measures the value of the controlled output parameter i.e. the BLR and send it back to the ingress node. The value is then provided to a comparator. The sensor is usually located in the network or the destination node where it is able to measure the lost bursts.

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Comparator

Burst Manager Controller

Tuning parameter

Target System

Controlled Output Parameter

Ingress node Reference Burst Loss Ratio

Error

Burstification Rate

BMC

Fig. 2. Comparator

Egress node

OBS Network

Sensor

Simple Closed Loop Feedback Model Diagram [1, 2].

Burst Manager Controller

Tuning parameter

Target System

Ingress node Reference Burst Loss Ratio

Error

BMC

Controlled Output Parameter Predicted Burst Loss Ratio

Fig. 3.

Egress node Burstification Rate

OBS Network

Predictor

The Proposed Model without Closed Loop Feedback.

Reference value for the BLR: A reference value that the controlled output parameter value should be at or hover around. This reference is set by the network administrator. • Error: It represents the difference between the reference value and the measured value provided by the sensor. • Burst Manage Controller (BMC): Takes the error value as an input and generates a tuning parameter accordingly based on a control law. The aim is to stabilize the Burst loss ratio around the reference value. A control law describes how the controller changes the value of a tuning parameter. Due to its simplicity and yet efficiency, an integral control law was used [17]. The integral controller produces a control action that continues to increase its corrective effect as long as the error persists. If the error is small, the integral controller increases the correction slowly. If the error is large, the integral action increases the correction more rapidly [17]. The integral controller has the following general time domain formula: •

BRsi dj (τ ) = BRsi dj (τ − 1) + Ksi dj Esi dj (τ − 1)

(16)

where BRsi dj (t) is the burstification rate tuning parameter from ingress node at source si to egress node at destination dj which changes over time. Ksi dj is the integral gain associated with each burst manager at each ingress node si that sends bursts to egress node at destination dj , and Esi dj (τ − 1) is its associated error value that the controller’s goal is to eliminate. The error is given by: Esi dj (τ ) = REFsi dj − BLRsi dj (τ − 1) where REFsi dj is the reference value.

Measured Burst Loss Ratio

(17)

The control law used in this work states that a maximum allowed rate of bursts allowed to be injected into the OBS network is to be adjusted dynamically based on the previous values of the BLR and the corresponding control error. More information about the controller are available at [1–3, 18]. Our previous work on the control system proved that the controller is able to control the BLR inside the network. However, the main shortcoming of the model (Fig.2) is that feedback information is needed from the sensor, located at the destination or at the network, to periodically inform the source about lost bursts. This information is used to calculate the burst loss ratio (BLRsi dj (τ )) from a source si to a destination dj . The enhancement proposed in this work consists of replacing the sensor module located at the destination by a predictor module located at the source (Fig.3). The predictor estimates the BLRsi dj (τ ) based on the available TCP statistics according to the TCP-based estimation method presented in section II. Thus, there is no need to have a feedback from the network. The control of the burstification rate will be totally autonomous. IV. S IMULATIONS AND R ESULTS This section discusses the simulated topology followed by the preliminary experiments and the results. A. Simulation Setup In order to evaluate the performance of the proposed algorithm, we implemented it as a module in Network Simulator 2 (ns-2) [19]. NSFNET network [20] used in the simulations is shown in Fig. 4. It consists of 14 nodes and 21 bi-directional links. The figure shows the propagation delay of each link

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6

5 1

0

3 6

13

5

3

10

1 10

3 10

Fig. 4.

8

2

7

5

2

9

11

12

3

4

0.05 0.045

2

12

14 8

5

4

6

4

Segment Loss Ratio

4

NSFNET network.

0.04 0.035 0.03 0.025 0.02 0.015 0.01

SLRs d 0 6 ESLRs d

0.005

0 6

0

B. Simulation Results Due to space limitations, we show only some of the simulation results for a couple of nodes of the NSFNET network. Statistics of the burst size distribution show that more than 40% of the bursts in the network are formed of 77 segments which is the maximum burst size. The burst loss size distribution (Lv ) shows that more than 90% of lost bursts are formed of 77 segments which shows that larger bursts have a larger loss probability. This is expected since it is harder to find a void for large burst than for small bursts. Fig. 5 shows the real segment loss ratio from source s0 to destination d6 compared to the estimated one using Eq.(6). It shows that the estimation is quite accurate with an R2 (SLR) of 83%. Fig. 6 shows the real burst loss ratio from source s0

0

20 40 60 80 100 120 140 160 180 200 Time (sec)

Fig. 5. Real and Estimated Segment Loss Ratio from source s0 to destination d6 (Without Controller)

0.035 0.03 Burst Loss Ratio

expressed in milliseconds. Nodes with discontinued line act as edge and core nodes. The others are only core nodes. There is 20000 FTP connections between every couple of edge nodes. Arrivals of sessions follow a Poisson process. The average of inter-arrival time is set to 0.01 sec. FTP connections are transmitting files of Random size following a Pareto distribution using a shape parameter 1.2 and average 10 M byte [21]. We used TCP Tahoe implementation which includes Slow-Start, Congestion Avoidance, and Fast Retransmit [22, 23]. We set the TCP segment size to 1500 bytes, TCP acknowledgment (ACK) packet size to 40 bytes, and the TCP maximum window size is 64 Kbytes (43 segments) which is used in most operating systems. The RTO is around 0.5 sec. Thus, the interval I is set to 2 sec. The maximum burst size is 120 Kbyte (around 77 segments). The processing delay for the control packet is 10μs. The same offset time is set to 1 ms. The nodes support wavelength conversion but there is no buffering. Fixed shortest path is used for routing. Each link has 4 channels operating at 10 Gbps, one of which is used as a control channel and the rest are data channels. The reservation scheme is the JustEnough-Time (JET) protocol [4]. The real segments and burst loss ratios are read from simulation log files. A gain Ksi dj = 500 is used for the controller law. When the controller is used, the reference burst loss ratio is set to 10−2 . We run experiments 5 times, the confidence interval is around 94%.

0.025 0.02 0.015 0.01 BLRs d 0 6 EBLRs d

0.005

0 6

0 0

20 40 60 80 100 120 140 160 180 200 Time (sec)

Fig. 6. Real and Estimated Burst Loss Ratio from source s0 to destination d6 (Without Controller)

to destination d6 compared to the estimated one using Eq.(14). The estimation performance is around an R2 (BLR) of 78%. Fig. 7 depicts the obtained BLR using the feedback control. The value used to tune the burstification rate is the real BLR (measured from simulation logs). As expected, the BLR hover around 10−2 , the reference value. Fig 8 shows also the BLR obtained using the controller. However, the estimated BLR has been used to tune the burstification rate. We can observe the good match between the estimated BLR and the real BLR that hovers around the reference value. This proves that the estimated BLR can replace the measured BLR. That is no more feedback is needed from the network. The ingress node becomes autonomous for controlling the BLR inside the network. V. C ONCLUSION AND F UTURE W ORK In this work, we proposed a new approach to estimate the BLR at the OBS edge node, without using any feedback

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as High-speed TCP [24] and FAST [25] which are proposed specifically for high bandwidth networks.

Burst Loss Ratio

0.025

R EFERENCES

0.02 0.015 0.01 0.005 BLRs d 0 6 0 0

20 40 60 80 100 120 140 160 180 200 Time (sec)

Fig. 7. BLR controlled with real BLR from source s0 to destination d6 (using Controller)

Burst Loss Ratio

0.025 BLRs d 0 6 EBLRs d

0.02

0 6

0.015 0.01 0.005 0 0

20 40 60 80 100 120 140 160 180 200 Time (sec)

Fig. 8. BLR controlled with estimated BLR from source s0 to destination d0 (using Controller)

information from the core network. The estimation is based on TCP statistics available at the ingress node. Extensive simulations on NSFNET network show that using the number of retransmissions sent by TCP provides a good estimate of the number of lost segments in the case of OBS networks. We derived a formula that maps the segment loss ratio to the burst loss ratio. Results show that the estimated BLR is close to the measured BLR with a coefficient of determination that did not fall below 80%. The estimation method was then integrated to the feedback control model proposed in [1–3]. Simulations show that the estimation model provided an accurate BLR to the controller module. The enhanced model controlled the BLR efficiently inside the network without feedback from the network or any end-to-end measurements. Further study should be considered to evaluate the accuracy of the estimated BLR when using different TCP versions such

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978-1-4244-4148-8/09/$25.00 ©2009 This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE "GLOBECOM" 2009 proceedings.