Ethernet Congestion Manager Characteristics, Calibration and Analysis

Hela Mliki, Lotti Kamoun SFAX National School of Engineers

Laboratory of Electronic and Information Technology Sfax, Tunisia

mliki.hela@gmail.com, LotfiKamoun@isecs.rnu.tn

Abstract- Following the dominance of Ethernet in local area,

campus and enterprise network, efforts are in progress in the Institute of Electrical and Electronics Engineers (IEEE), the

Metro Ethernet Forum (MEF), and the International Telecommunication Union (ITU) to extend Ethernet into metro networks (11). The major challenge for deploying this technology in metro networks and Wide Area Network (WAN) is to

guarantee the Quality of services (Q08) (I). Nevertheless, congestion problems can be harmful to Q08. Actually, networks are increasing in size and complexity and new network services are deployed, the management becomes more complex and congestion can become a serious problem. Congestion control

mechanisms control congestion in nodes which suffer from lack of resources. Congestion control mechanisms have to be complying with IEEES02.1 specifications. Our investigation will be concentrated on ECM (Ethernet Congestion Manager)

mechanism. We have studied ECM algorithm, we have suggested some scenarios and tests then based on simulations results ECM

performance analysis are deduced.

Keywords- ECM, congestion control, metro-Ethernet.

I. INTRODUCTION

Ethernet services continue to grow in popularity thanks to its capacity of providing the scaling and features needed for future carrier networks. Ethernet network success is also due to its low-cost, high bandwidth connectivity and ease of use. Nevertheless, it is necessary to implement new techniques in order to fill some gaps. IEEE802.1 Qau [20] has worked on congestion control mechanism for Ethernet network since 2007. The standard specified protocols, procedures and managed objects that support congestion management of long lived data flows within network domains of limited bandwidth delay product. This is achieved by enabling bridges to signal congestion information to end stations capable of transmission rate limiting to avoid frame loss.

Congestion problem take place if the total sum of demands on a resource is more than its available capacity. Mathematically representation:

L Demand> Available Resources (1)

Congestion control is a problem that has been addressed by several researches in the past.

Lamia Chaari High Institute of Computer and Multimedia Sfax

Laboratory of Electronic and Information Technology Sfax, Tunisia

lamia.chaari@,tunet.tn

Some of these congestion control schemes are briefly described below:

For timeout-based congestion control schemes, the load on the network should be reduced if there is packet loss. Later, if there is no further loss, the load is increased slowly. CUTE (Congestion Using Timeout at the End-to-end layer) schema [ 12] is based on this idea. It required that the window is decreased to one on a timeout, and only one packet is retransmitted regardless of the window. Later, the window is increased from W to W+ 1 after receiving acknowledgments for W packets without any timeouts. In a similar scheme by Jacobson [13], another version called 'slow start' where the window WO at timeout is remembered, and the increase is linear up to WO/2 and parabolic thereafter.

Another development in the area of congestion control is the introducing of the concept of congestion avoidance. Congestion avoidance allows the network to operate in the region of low delay and high throughput. These schemes prevent a network from entering the congestion state which the packets are lost [14] [15]. A DECbit scheme was proposed for congestion avoidance which requires adding a binary congestion bit to each packet header. The congestion indication with this schema is communicated back to the users thanks to a congestion indication bit on packets setting and flowing in the forward direction from the routers [ 16].

In addition, there were schemes requiring explicit feedback from the network to avoid congestion. These explicit messages are sent from the congested source to the control point. Such messages have been called choke packets, source quench messages, or permits. The sources reduce their loads upon the receipt of choke packets or source quench message and increase it if these are not received an example of such scheme is described in [17].

Also, there were implicit feedback schemes; the timeout­based scheme described earlier is an example of an implicit feedback scheme for congestion control. It consists on measuring delay and adjusting the traffic depending upon the delay [18].

In this paper we are interesting by the explicit feedback scheme BCN (Backward Congestion Notification).

The basic components of BCN (Backward Congestion Notification) architecture which integrates control are

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congestion points and reaction points. The congestion point is the entity where congestion can be detected and where congestion signals are generated. It reflects negative and positive feedback values back to reaction point. The reaction point is the entity where the flow injection rate is changed due to congestion signals. It transmit regular Ethernet frames, when congestion messages arrives it perform multiplicative decrease and active increase algorithm [19].

In this paper, we describe in the second section the ECM (Ethernet Congestion Manager) mechanism; in the third section we provide our measurements and simulation results; the forth section is for discussing ECM performance based on simulation results, and the last section is to conclude this paper and to suggest some prospects.

II. ECM: ETHERNET CONGESTION MANAGER

It is also known as BCN (Backward Congestion Notification). ECM is a layer two congestion management mechanism. Its assumption consist in pushing congestion from the core of the network to the edge, use rate limiters at edge to shape flows causing congestion and control injection rate based on feedback coming from congestion points [2].

A. Detection For congestion detection, the buffer utilization at possible

congestion point should be monitored. Thus we have to check that queue thresholds are not exceeded [4], [2]. The queue thresholds specifications are shown in Figure. 1 (where Qlen=Current queue length and Qold=Queue length at previous sample).

B. Signalling Congestion point sends a notification to the end node in

order to report congestion. Notification is defmed by BCN message which hold status and variation of queue buffer length [4], [2]. Feedback signals are illustrated in Figure.2.

C. Reaction The reaction point adjusts the rate limiter setting according

to the received BCN message. Traffic shaping is defmed by the Additive Increase Multiplicative Decrease algorithm (AIMD)[4], [2]. So, if a reaction point receives negative feedback (FB<O), the source decreases its rate else if it receives a positive one it will increase its rate. A severe congestion reactions consist in setting current rate to zero and stating a random timer TE[O,Tmax]. When the timer T expires, the rate takes a minimum value R "-Rmm, next severe congestion causes exponential back -off. So T max will increase T max ..-T max *2 and Rmin will decrease Rmin+- Rmm 12. For next positive feedback, T max and Rmin will be reset to their initial values. At regular intervals Td current rate will be increased, R ..-R+Rd. Its beneficial consists in grabbing safely an extra bandwidth and could resolve the case of losses congestion notification. We have gathered the reaction point states parameters and configuration variables in TABLE I.

TABLE!. REACTION POINT PARAMETERS

State Configuration R= Curent rate. W=Weight of derivative component. CPID= Curent Congestion Gd=Decrease gain. Point ID. Gi=Increase gain. Rmin=Minimum back -off rate. a=Maximum rate decrease.

I3=Maximum rate increase. T d=SeIf increase timer. Rd=SeIf increase amount. Rmin=minimum back -off rate. Tmax=Maximum back -off time.

Figure l. Queue in core switch

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III. ECM PERFORMANCE ANALYSIS

In order to analyse the performance of congestion control ECM mechanism, we have implemented ECM algorithms under the discrete event simulator OMNeT++[5][8][9]. We have modelled the network as several users generating messages (Jobs) to a destination. These messages are queuing before achieving their destination. We have modelled flow as a unidirectional transfer from the source to the destination, and a feedback pass over the network by the opposite direction on the same path. Furthermore, we assume a FIFO discipline in queuing network. We choose to delete the arriving packets when the queue is full.

For all simulations, the link bandwidth is set to lOMbits/s. Senders sent packets which size are fixed at 1530 byte. Packets achieve their destination by passing through a switch queue with capacity equal to 100 packets. The simulation duration is set to 10 hours (36000 seconds). Several scenarios are used to carry Ethernet packets across network. Parameters used for simulations in reaction points are giving in TABLE II.

TABLE II. EACTION POINTS PARAMETERS

Parameters Definition Values CR current Rate 10"bitis W weight of derivative comoonent 2 aloha maximum rate decrease 0.5 beta maximum rate increase 0.5 Rmin current minimum back-off rate lO"bitis Gd decrease gain 0.00026667 Gi increase gain 0.53333 Rd self increase amount 100 bitls Td self increase timer Is Tmax current maximum hack-off time 1s T current hack-off time random value

A. Network with one reaction point We consider a network with one sender, queue which

represents the congestion point and an Ethernet packets receiver node.

1) Scenario 1: for this scenario we have chosen the following queue parameters: Qeq=20 and Qsc=60, simulations result are shown by Fig.3, FigA, Fig.5, Fig.6, and Fig.7. During simulation, we notice that Queue's curve (FigA) variates between stability oscillations around congestion severe

threshold and zero value which indicate an underutilization of the queue (FigA). Underutilization push queue to send positive feedback, positive feedback (Fig.5) wich rest maximum back­off time (Fig.6) and minimum back-off throughput (Fig. 7) Then, reaction point take back its high throughput and queue resume oscillation around congestion severe threshold.

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2) Scenario 2: for this scenario we have choose these queue parameters: Qeq=50 and Qsc=95, simulation results are given by Fig.8 and Fig.9. During simulation, throughput takes during 10 frrst second, maximum values (Fig.8). This maximum values was taken allow reactions points to send their packets faster. Then, the queue was glut. To overcome the congestion state, reaction point minimizes its throughput. Minimum back-off throughput decreases and maximum back­off time curve increases, this is caused by the severe congestion notification sent by the queue to the reaction point. The lack of feedback positive in the rest of simulation run prevent reactions points from resetting minimum back-off throughput and maximum back-off time values. At start, the queue is overloaded, then it decrease its values until being empty (Fig.9 a) then it stabilizes around the equilibrium threshold.

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B. Network with six reaction points We consider a network with six senders, queue which

represents the congestion point and a receiver node.

For this scenario we have choose these queue parameters: Qeq=20 and Qsc=60, simulations results are presented by Fig.lO, Fig.ll, Fig.l2, and Fig.13. Times for increases throughput are explained by positive feedback and for decreases throughput account for negative feedback are not the same for the various reactions points. For example node[l] (red curve in Fig. I 0) increases its throughput when the other nodes receive negative feedback. Because of the positive feedback that node[l] has been received, minimum back-off throughout and maximum back-off time have been reset. So we observe, at the moment 24358.46 second of the simulation run (Fig.l3), the maximum back-off time curve decreases into its initial value (3 second) and the minimum back-off throughput (Fig.l2) curve increases into its initial value (lO�itJsec). At the beginning of simulation, the queue fills up until it reaches congestion state (Fig.ll), then it start empty out and get oscillations around the congestion severe threshold. In around the interval [24000, 24600] of the simulation run (Fig.l2), queue gets values under the equilibrium threshold, so it takes the opportunity to send positive feedback which increase the throughput of reaction point node[ 1].

Figure 9. Queue length variation in 36000 second simulation time

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Figure 10. Throughput variation in 3000second

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IV. SIMULATION RESULTS INTERPRETATION

A. Stability

It represents the quality of maintammg a constant behaviour of a mechanism in the presence of forces which try to threat or to disturb or to change its behaviour. From simulation results, it's recommended to avoid low parameters values for the equilibrium threshold and severe congestion threshold. Actually, we notice that low values make the queue drag on in zero value for so notable opportunity. Face to an underutilization, the queue will try to demand increasing throughput by sending an important number of positives feedbacks, positives feedbacks stimulate reactions points to reset maximum back-off time and minimum back-off throughput. Thus, the throughput of reactions points will increase notably, and the length of the queue will get over faster the low value of the thresholds, and may exceeded the queue capacity in case of rising number of reactions points, the queue will send negative feedback notifications and it come back to trailing again in zero values. Furthermore, low values of threshold could stabilize the queue around severe congestion threshold, in that case we have no or rare positive feedback because the queue length did not come under queue equilibrium threshold, so maximum back-off time and minimum back-off throughput will never be reset and reactions points send their packets in low throughput. Nevertheless we notice that in simulations which we increase threshold values and we take Qeq=50 and Qsc=95, the queue come to stabilize around equilibrium threshold after an underutilization or an overutilization.

Ethernet Congestion Manager (ECM) allows an anticipated reaction thanks to queue thresholds control. When equilibrium threshold is exceeded, reaction points start decreasing their throughputs. And, Throughput is set to zero when the severe congestion threshold is exceeded. Then, queue size grows less significantly. Thereby we notice that oscillation around equilibrium threshold hasn't stabilized for all simulation time. As a result, stability is not well maintained for ECM mechanism.

B. Fairness

We can say that an algorithm is "fair" if it takes the completion times of similar flows equal, Fig.l4 below, for the scenario that suppose Qeq=50 and Qsc=95, shows the number of packets sent during simulation from different reactions points to the same destination. We note that since bandwidth is shared by all reactions points' flows, the number of packets were being sent during simulation time is not the same. As an example, in simulation with 9 node, node[ I ] sent 6389 packets while node[5] sent 1348 packets. Thus, we can deduce that faimess with ECM mechanism is not guaranteed between stations.

C. Scalability

It indicates the ability of a mechanism to handle the increasing sum of flow in smooth way.

Fig. I5 shows packet error rate for different scenarios.

Packet error rate is equal to 0.78% for a scenario with one reaction point and is equal to 66.5% for a scenario with fifteen reaction points.

Packet Error Rate= [ I -Number of frame delivered to destination! Total of frame sent to destination] x 100

(2)

As a consequence to the growing number of reaction points, negative feedback messages get increasing so that oblige source points to reduce their throughput in congestion case.

Ethernet Congestion Manager allows, on the one hand avoiding congestion by controlling queue thresholds Qeq (equilibrium threshold) and Qsc (severe congestion threshold), on the other hand it reacts by setting throughput to zero value in case of severe congestion or to execute a multiplicative decrease. When network resource is limited and the number of competitive senders is important as shown in Fig. I 0, ECM mechanism looks for adjusting throughput in according to resource size. Thus, throughput takes reduced value when queue current length doesn't success to be reduced under equilibrium threshold (Qeq).

Then, Ethernet Congestion Manager is able to maintain scalability.

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V. CONCLUSION

We propose in this paper a study of congestion control protocol ECM (Ethernet Congestion Manager). ECM is based on congestion detection in core switch network then sends a notification to the edge node. Reaction points will take a suitable reaction to reduce or avoid congestion and carry out the Additive Increase Multiplicative decrease algorithm (AIMD). This work takes place to cover overcharge networks problematic. In fact congestion management is substantive to ensure that applications have sufficient network resources to communicate effectively and appropriately for the variety of traffic.

This paper also points out and analyses the ECM mechanism behaviour. Notably, we have tested some scenario of ECM algorithm and discuss simulation results; we have found that low thresholds values annoyed to queue stability. In fact, with low thresholds values queue could fall in case of underutilization or a case of stabilisation around congestion severe threshold. Simulation results also illustrates that fairness is not guaranteed with ECM algorithm and stability is not well maintained but scalability could be maintained. There is much ongoing and future work. It would be interesting to analyse ECM behaviour with other complex topology, to study other congestion control techniques and to elaborate a comparative analysis between them.

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