chp-3A10.1007-2F978-3-642-22606-9_16

S. Aluru et al. (Eds.): IC3 2011, CCIS 168, pp. 123134, 2011. Springer-Verlag Berlin Heidelberg 2011

Variable Length Virtual Output Queue Based Fuzzy Adaptive RED for Congestion Control at Routers

Pramod Kumar Singh and Santosh Kumar Gupta

Computational Intelligence and Data Mining Reasearch Laboratory ABV-Indian Institute of Information Technology & Management Gwalior, India

[email protected], [email protected]

Abstract. Internet routers play an important role during the time of network congestion. All the Internet routers have some buffer at input and output ports, which hold the packets at the time of congestion. Many queue management algorithms have been proposed but they focus on fixed queue limit. Recognizing the fact that active queue management algorithms have fixed maximum queue limit, we direct our attention to variable length queue limit for Combined Input Output Queued (CIOQ) switches. We incorporated our proposed technique, which is a fuzzy logic control based generic variable length active queue management scheme in TCP/IP networks, to the drop-tail and the Adaptive RED (A-RED) algorithm. The empirical results show low packet loss and high queue utilization in modified algorithms (augmented with variable length active queue management scheme) in comparison to the original drop-tail, RED and A-RED algorithms.

Keywords: Congestion Control, Fuzzy Logic Controller (FLC), Active Queue Management (AQM), Virtual Output Queue (VOQ), Combined Input Output Queued (CIOQ) Switch, Adaptive RED (A-RED).

1 Introduction

Congestion is a critical issue as it reduces the overall throughput of the network and users experience greater delay. Though the routers play an active role in its resource allocation to effectively control/prevent congestion, it is still a major cause of concern because of ever growing Internet and ever growing number of users; they increase the amount of data to be carried over the Internet. Todays Internet routers have some buffer at input and output ports. The buffer size of the router should be large enough to accommodate the packets during the time of congestion but, at the same time, should also take care of the queuing delay. It demands for an optimum size and efficient management of buffers at the input/output ports. This is known as active queue management (AQM) [6].

Most of the Internet routers run drop-tail gateways. However, drop-tail buffer management introduces large queuing delays in bursty traffic [13]. The RED algorithm [4] [12] [14] manages the (buffer) queue more effectively and monitors the average queue length. The average queue length is compared with minimum threshold (minth) and maximum threshold (maxth). If the queue length is less than minth then all

124 P.K. Singh and S.K. Gupta

the incoming packets are accepted. If the queue length is in between minth and maxth then packets are dropped with a probability that increases linearly up to maximum drop probability (maxp) and if the queue length exceeds the maxth then all the incoming packets are dropped. The most important advantage of RED is that it keeps the average queue length low to allow occasional burst of packets in queue. However, it has several shortcomings, e.g., a high degree of sensitivity towards its operating parameters, unfairness to flows with different round-trip times, the problem of global synchronization. A related weakness of RED is that throughput is also sensitive to the traffic load and the RED parameter. In particular RED does not perform well when the average queue length becomes larger than maxth. It results in a significant decrease in throughput and a significant increase in the drop rate [5] [17].

The A-RED [11] [13], proposed by one of the authors of RED, attempts to solve the problem of need for continuously (re)tuning RED parameters. In particular, A-RED adjusts the value of maximum drop probability (maxp) to keep average queue length within a target range half way between the minth and maxth. It is shown in Figure 1. Though A-RED attempts to tune the RED parameters for a robust behavior, it fails to do so in various dynamic cases as A-RED retains REDs basic linear structure.

Every interval seconds:

if (avg > target and maxp 0.5) increase maxp ; maxp maxp+ ; else if (avg < target and maxp > 0.01)

decrease maxp: maxp maxp * ; Variables: avg: average queue length Fixed parameters: Interval: time; 0.5 seconds; target : target for avg ; [minth +0.4*( maxth minth ), minth + 0.6*(maxth - minth)]. : increment; min (0.01, maxp / 4) : decrease factor; 0.9

Fig. 1. Adaptive RED algorithm

Many other active queue management schemes are also reported in the literature, e.g., Random Exponential Marking (REM) [2], fuzzy Proportional Integral (PI) [19], and Adaptive Virtual Queue (AVQ) [15]. All these existing active queue management schemes are based on the equation model. These equation models use various control parameters, which are dependent on different network parameters, e.g., number of flows, round trip time. However, it is very difficult to set these parameter as TCP/IP network is dynamic in nature. Many researchers, e.g., [3], [7], [8], [10], adopted fuzzy logic controller (FLC) to set these parameters dynamically in the congestion control algorithms because of its strength in controlling highly nonlinear, complex systems.

In this paper, we design a fuzzy logic controller (FLC) based on fuzzy logic set theory [23], [24] for computing the change in virtual output queue (VOQ) length [1],

Variable Length Virtual Output Queue Based Fuzzy Adaptive RED 125

[21], [18] according to its instantaneous queue length. It is a generic variable length active queue management scheme, which may be incorporated to any VOQ based active queue management congestion control mechanism to improve its performance while keeping the basic structure of the original algorithm same. In this paper, we incorporate our proposed generic method to the drop-tail and the A-RED and obtained encouraging results.

Rest of the paper is organized as follows. Section 2 discusses about variable length VOQ. In Section 3, we present our proposed generic variable length active queue management method and its application to drop-tail (Fuzzy drop-tail) and A-RED (Fuzzy A-RED). The rule base design of FLC is presented in Section 4. We discuss the performance of proposed method through a set of extensive simulation and compare the obtained results with well-known methods in Section 5. Finally in section 6, we present our conclusion.

2 Variable Length VOQ

The combined input and output queuing scheme uses buffers at both input and output modules of a switch, and a switch that employs this queuing scheme is called a CIOQ switch [9]. Every input port maintains a virtual queue, known as virtual output queue (VOQ), at its input buffer for each output port. In other words, for an NN switch, each input port i (1 i N) maintains a separate set of FIFO queues for each output port j (1 j N), named as VOQi,j. Therefore, there are N sets of VOQi,j queues at each input port. The incoming packets are stored in appropriate VOQ according to their destination address; the VOQi,j buffers packet at input port i, which is destined to output port j. The buffer space of an input port is divided according to the number of VOQs and each VOQ has a fixed maximum queue limit to store the incoming packets. If there is no space for incoming packet at VOQ, the packet is dropped. However, it is desirable and a good strategy to vary (increase or decrease) the address space of VOQs at run time as per the requirement for efficient utilization of the queue while the buffer size at the input port is fixed.

Fig. 2. In Case 1, the maximum queue limit of both VOQs is equal; in case 2, the maximum queue limit for VOQ11 is less than the VOQ12 and in case 3, the maximum queue limit for VOQ11 is greater than the VOQ12

126 P.K. Singh and S.K. Gupta

Our approach is based on the fact that we can vary the buffer size of the VOQ during the processing time as per the requirement. Figure 2 shows three possible cases for 22 switch. We can modify any VOQ based active queue management algorithm which uses a fixed queue limit. The amount of variation in the maximum queue limit of VOQs (while the total buffer size of an input port is fixed) is calculated by using the fuzzy logic controller.

3 Fuzzy Logic Controller

The fuzzy Controller is a controller which is based on the fuzzy logic based rules and often contains nonlinear mapping. The idea of FLC was initially introduced by Zadeh [24] and first applied by Mamdani [16] in an attempt to control systems that are difficult to model mathematically. FLC may be viewed as a way of designing feedback controllers where it is convenient and effective to build a control algorithm without relying on formal models of the system. The control algorithm is encapsulated as a set of commonsense rules. FLC has been applied successfully in control system for which analytical models are not easily obtained or the model itself, if available, is too complex and highly non-linear.

Our approach is to design a non-linear fuzzy logic controller, which operates at each input port of the router. For example, in a 22 switch at input port1 the fuzzy controller has two virtual queues, namely VOQ11 and VOQ12, which may be in the low, average and high queue length states (refer, Figure 3). The low, average and high represent the status of input variables in linguistic form, which change dynamically over time. In order to determine the linguistic values of input and output, we partitioned the input and output space. The controller changes the maximum queue limit (varies the maximum buffer size of each VOQ) according to status of the inputs. Each of the input variables is represented by a fuzzy set.

Fig. 3. Fuzzy Logic Controller for Queue Management System Model

Variable Length Virtual Output Queue Based Fuzzy Adaptive RED 127

A notation convention for fuzzy sets [22], when the universe of discourse, X, is discrete and finite, is shown in Eq. 1. Here, A is a fuzzy set.

A= { .}={ } (1)

For our algorithm, the fuzzy sets are represented as follows (refer, Eq. 2):

VOQ11= {

}

VOQ12= {

} (2)

Where VOQ11 and VOQ12 are fuzzy variables; low, average, and high are the possible values for fuzzy variables, and 1, 2, and 3 are the membership functions of the fuzzy variable.

3.1 Variable Length VOQ for Drop-Tail Algorithm

In modified drop-tail algorithm the maximum queue limit of each VOQ is not fixed. The FLC uses the instantaneous queue length as feedback, which is measured periodically, to compute new limit of the queue length. In other words, the controller varies the maximum queue length limit (refer, manipulated variable in Figure 3) as per sampled queue length of each VOQ. Variation in queue limit gives the low loss rate in comparison to simple drop-tail algorithm while the average queue length of both the algorithm is approximately same. It means modified drop-tail algorithm gives higher queue utilization in comparison to original drop-tail.

3.2 Fuzzy Adaptive RED

The overall guideline for fuzzy A-RED algorithm is same as A-RED algorithm except the variable target. In A-RED algorithm (refer, Section 1) the target value is fixed and is calculated as follows:

Target value: [minth + 0.4*( maxth minth ), minth + 0.6*(maxth - minth)]

In modified A-RED algorithm, which has the variable length VOQ, the value of

maxth and minth is not fixed. As a result the target value of the A-RED algorithm changes in each time interval with the varying values of minth and maxth according to fuzzy logic controller. For nth time interval the target value is calculated as follows:

Target value: [minth (n) + 0.4*( maxth (n) minth (n)), minth (n) + 0.6*(maxth(n) - minth(n))]

Here the minth(n) and maxth(n) is the minimum and maximum threshold at n

th time interval respectively. Fuzzy A-RED removes A-REDs dependence on target value. Adapting the target value results in low packet loss and low average queue length in comparison to A-RED.

128 P.K. Singh and S.K. Gupta

4 Rule Based Design

The FLC uses some linguistic rules for each input port. These linguistic rules control the system under different operating conditions. Usually multi-input FLC makes it easier to describe the system dynamics linguistically. We expect that we can tune the system and improve the behavior of active queue management algorithm, e.g., drop-tail, A-RED, by using variable length VOQs. The fuzzy rule base (IF-THEN rules) for the 22 CIOQ switch is presented linguistically in Figure 4 and same is represented in tabular form in Table. 1. The control surface defined by the FLC rules is shown in Figure 5.

Usually to define the linguistic rules of a fuzzy variable, Gaussian, triangular or trapezoidal shaped membership function are used. Since triangular and trapezoidal shaped function offer more computational simplicity, we have selected them for our rule base. The rule base is fine-tuned by observing the progress of simulation, such as packet loss occurrences and number of buffered packets at each VOQ.

/* linguistic rules for each Input port */ /* initially voq11 and voq12 queue limit is equal and set to maximum queue limit.*/ /* if voq11 queue limit increases, then the voq12 queue limit decreases to make input port buffer size fixed */ /* Set of linguistic rules defining the control surface of FLC */

if voq11 is low and voq12 is low then voq11_modified_queue_ length is equal to max queue limit. if voq11 is average and voq12 is low then voq11_modified _queue_ length is greater than max queue limit. if voq11 is high and voq12 is low then voq11_modified _queue_ length is greater than max queue limit. if voq11 is low and voq12 is average the voq11_modified_queue_length is lesser than max queue limit. if voq11 is average and voq12 is average then voq11_modified_queue_length is equal to max queue limit. if voq11 is high and voq12 is average then voq11_modified_queue_length is greater than the max queue limit. if voq11 is low and voq12 is high the voq11_modified_queue_length is lesser than max queue limit. if voq11 is average and voq12 is high then voq11_modified_queue_length is lesser than max queue limit. if voq11 is high and voq12 is high then voq11_modified_queue_length is equal to max queue limit.

Fig. 4. Fuzzy rule base for 22 CIOQ switch

Variable Length Virtual Output Queue Based Fuzzy Adaptive RED 129

Fig. 5. Control decision surface of the Fuzzy Logic Controller shaped by rule base and linguistic variables

Table 1. Rule base of fuzzy controller

Input variable Output (manipulated variable) VOQ11 VOQ12 VOQ11 Queue limit VOQ12 Queue limit low low equal to max queue limit equal to max queue limit

average low greater than max queue limit less than max queue limit

high low greater than max queue limit less than max queue limit

low average less than max queue limit greater than max queue limit

average average equal to max queue limit equal to max queue limit high average greater than max queue limit less than max queue limit low high less than max queue limit greater than max queue limit average high less than max queue limit greater than max queue limit high high equal to max queue limit equal to max queue limit

5 Simulation Result

In this Section, We compare our scheme with the original drop-tail, RED and A-RED algorithm.

5.1 Experimental Setup

We perform our simulation on 22 CIOQ [21] switch as shown in Figure 6. The size of the buffer at input and the output port is 120 and 100 respectively. Buffer size at the input port is segmented according to the number of VOQs. Each input port carries multiplexed TCP Reno flows. The TCP flows are generated at separate source nodes and then multiplexed into the backbone before reaching at the input port of the switch. In this experiment, we use 4 source nodes at each input port, hence, total 8 source nodes generates TCP flows in the range of 50 to 500 sessions to the input ports of the switch. The size of the packets is 1000 bytes. The queue monitoring interval is set to 0.0001 sec. In A-RED algorithm is set to 0.01 and is set to 0.9.

130 P.K. Singh and S.K. Gupta

Fig. 6. 22 CIOQ switch

5.2 Input Queue Length

Figure 7 displays the average input queue length in the queue management unit when we use the Variable length VOQ based drop-tail and Fuzzy A-RED. The corresponding simulation results of original RED, drop-tail and A-RED are also shown for the comparison. For this simulation, the number of TCP sessions is 250 and speedup varies from 0.5 to 2.0. For RED, the minimum threshold is set to 19 packets and the maximum threshold is set to 59 packets for each VOQ. We use drop-tail algorithm at output ports for all the comparative algorithms.

Fig. 7. Average input queue length v/s speedup for 22 Switch (load 200 TCP sessions)

On the input port, the drop-tail algorithm has the longest queue Length as it drops packets only when the buffer overflows. The suggested change in drop-tail algorithm, i.e., the variable length VOQs, has the same average queue length as original drop-tail

Variable Length Virtual Output Queue Based Fuzzy Adaptive RED 131

algorithm whereas the RED algorithm has lower queue length as it drops packets even before buffer is overflow.

As A-RED keeps the average queue length away from maxth, the input queue length for A-RED is less than RED algorithm. As mentioned in section 3, the target value is not fixed in the Fuzzy A-RED algorithm; hence the result is even better than original A-RED algorithm.

5.3 Loss Rate

Loss rate is the ratio of the number of packets dropped and the number of packets sent. In this experiment, the speedup is fixed at 1.1whereas the load varies from 50 to 500 TCP sessions. Figure 8 shows that the loss rate of the fuzzy A-RED algorithm is lowest. Adjusting the maxp and target value of the Fuzzy A-RED algorithm avoids the higher packet loss as the average queue length oscillates near to the target value. We observe that the performance of fuzzy A-RED is comparatively better than the drop-tail, RED and the original A-RED because of the efficient management of the buffer space among the VOQs as per the requirement and the variation in the target value as well.

Fig. 8. Loss Rate of the 22 Switch at speedup 1.1

5.4 Buffer Size Variation

We investigate the buffer size of drop-tail with fixed length VOQs (maximum queue limit for drop-tail is set to 60 packets) over time. Figure 9 shows the buffer size variation of VOQ11 and VOQ12 respectively. The buffer size of the both VOQs is not more than the 60 packets at any time instant.

Figure 10 shows the buffer size variation of proposed algorithm. The result shows that maximum queue limit of VOQ11 and VOQ12 is varying from 54-65 packets according to suggested approach, but at any instant of time the number of packets in

132 P.K. Singh and S.K. Gupta

Fig. 9. Buffer size variation of VOQ11& VOQ12 over time (for drop-tail algorithm)

both of the VOQs simultaneously is not more than 60. The reason is that at that instant of time the other VOQ had relatively less queue length hence it could relinquish a portion of its unused address space to share with other heavily loaded VOQ. Therefore, the proposed variable length VOQ algorithm minimizes the packet loss and provides efficient management of the buffer space at input ports.

Fig. 10. Buffer size variation of VOQ11&VOQ12 over time (variable length VOQ for drop-tail)

As fuzzy A-RED algorithm has the basic structure of the A-RED algorithm, the size of the buffer oscillates near to the target value. Figure 11 shows that the number of packets buffered is half way between maximum and minimum threshold.

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Fig. 11. Buffer size variation of VOQ11&VOQ12 over time (Fuzzy A-RED)

6 Conclusion

Our proposed scheme is a generic concept which may be used to improve the performance of any active queue management scheme. In this paper, we have incorporated variable length VOQ with the active queue management algorithms, e.g., drop-tail, A-RED algorithm, which improves the performance in terms of queuing delay, average queue length and loss rate. Variable length VOQ for drop-tail is the improvement over simple drop-tail algorithm which utilizes the queue more efficiently. In fuzzy A-RED target value dynamically change with change in VOQ length which gives improvement over A-RED algorithm in dynamic network environment. We formulate an effective and efficient technique for queue management using the fuzzy logic control, to solve the problem of congestion in TCP/IP networks. We have demonstrated that in the real world for the non-linear and complex system the fuzzy logic control gives the acceptable solution with the help of linguistic models. Additionally, it does not require any change in the switch hardware.

In future work, we plan to explore the proposed scheme on other active queue management algorithms such as REM, AVQ and in other network scenario like Diff-Serv, and in different types of traffic condition. We also plan to check the stability and performance of the proposed method on the larger switches.

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Variable Length Virtual Output Queue Based Fuzzy Adaptive RED for Congestion Control at RoutersIntroductionVariable Length VOQFuzzy Logic ControllerVariable Length VOQ for Drop-Tail AlgorithmFuzzy Adaptive RED

Rule Based DesignSimulation ResultExperimental SetupInput Queue LengthLoss RateBuffer Size Variation

ConclusionReferences

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