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Chude-Olisah CC, Chude-Okonkwo UAK, Bakar AK et al. Fuzzy-based dynamic distributed queue scheduling for packet switched networks. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 28(2): 357–365 Mar. 2013. DOI 10.1007/s11390-013-1336-2

Fuzzy-Based Dynamic Distributed Queue Scheduling for Packet Switched Networks Chollette C. Chude-Olisah1 , Uche A. K. Chude-Okonkwo2 , Member, IEEE Kamalrulnizam A. Bakar1 , Member, ACM, and Ghazali Sulong1 1

Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru 81110, Malaysia

2

Wireless Communication Center, Universiti Teknologi Malaysia, Johor Bahru 81110, Malaysia

E-mail: [email protected]; {uche, knizam}@utm.my; [email protected] Received November 2, 2011; revised January 9, 2013. Abstract Addressing the problem of queue scheduling for the packet-switched system is a vital aspect of congestion control. In this paper, the fuzzy logic based decision method is adopted for queue scheduling in order to enforce some level of control for traffic of different quality of service requirements using predetermined values. The fuzzy scheduler proposed in this paper takes into account the dynamic nature of the Internet traffic with respect to its time-varying packet arrival process that affects the network states and performance. Three queues are defined, viz low, medium and high priority queues. The choice of prioritizing packets influences how queues are served. The fuzzy scheduler not only utilizes queue priority in the queue scheduling scheme, but also considers packet drop susceptibility and queue limit. Through simulation it is shown that the fuzzy scheduler is more appropriate for the dynamic nature of Internet traffic in a packet-switched system as compared with some existing queue scheduling methods. Results show that the scheduling strategy of the proposed fuzzy scheduler reduces packet drop, provides good link utilization and minimizes queue delay as compared with the priority queuing (PQ), first-in-first-out (FIFO), and weighted fair queuing (WFQ). Keywords

1

fuzzy logic based decision method, priority, queue limit, queue scheduling

Introduction

Generally, the routers provide connection between networks and keep certain broadcast traffic under control. Primarily, the routers receive packets from the sender (the source) and forward the packets to a predetermined destination (the receiver). Upon the transmission of data packets, congestion might occur due to traffic overflow, thereby making some of the traffic not to get to the destination. For this reason, the router must make decisions on the best possible way to schedule traffic efficiently. This implies that the router must exert certain level of control so as to minimize packet drop. Let us consider a network scenario whereby users are transmitting applications such as voice over Internet telephony (VoIP), video and file transfer protocol (FTP). These applications are assumed to be transmitted on a single global link. Let us assume that the first traffic to the router is the FTP traffic, and it is then queued to await service. And while the first packet of the FTP traffic is scheduled for transmission, VoIP

traffic arrives and is also queued to wait its turn. The challenge here is in determining which of the packets of the FTP and VoIP traffic is to be scheduled for the next transmission. In the above scenario, the varying quality of service (QoS) demand that each application makes on the network and the classification of the traffic based on its QoS demands become important issues. In order to address these issues, the scheduler must put the following service policies into consideration: 1) the various QoS requirements of arriving packets in terms of priority, 2) the queue to select when considering delay and loss, and 3) the approach that deals with bursts of traffic and overflow of queues[1] . Some of the existing queue scheduling methods such as first-in-first-out (FIFO), priority queuing (PQ) and the weighted fair queuing (WFQ) consider one or none of the “service policies” stated above. FIFO schedules traffic for transmission without considering any of the service policies above. In the case of PQ, only service policy 1) is observed and traffic scheduling is prioritized. The scheduling operation in PQ does not take into

Regular Paper This work was supported by the Ministry of Science and Teknologi Malaysia eScience under Grant No. 4S034 managed by Research Management Centre of Universiti Teknologi Malaysia. ©2013 Springer Science + Business Media, LLC & Science Press, China

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account the 2) and 3) service policies. In WFQ, service policies 1) and 2) are observed. Some of the merits of WFQ are discussed in [2]. However, in [3] the problems associated with WFQ when integrated into a busy link was observed. In such scenario, the delay susceptible packets suffer loss. Based on the inability of the existing schemes to address all the service policies enumerated above, the development of a scheduling scheme that puts all the service policies into consideration is necessary. Now, the question is, how can we define a class of scheduling algorithm that can take the service policies enumerated above into account? To address this question, we consider the concept of fuzzy logic theory. Since, the dynamic nature of the network traffic follows the fuzzy process, makes the fuzzy logic approach very appropriate to traffic scheduling and control. The concept of fuzzy logic has been adopted in literatures and has been proven to be simple, and it is a powerful tool for dynamic systems. To mention but a few, the concept of fuzzy logic was utilized to provide fairness in scheduling by minimizing queue level[4-5] . It was used to realize control through controlled allocation of shared resources[6] and achieve priority of packets based on estimated number of packet hops and buffer size[7] . In [8], fuzzy logic was used to obtain the quality of video packets through data rate policing of traffic entering the traffic-shaper. However, these literatures do not address the performance of the fuzzy systems with respect to the utilization of the link resources while the network delay and traffic drop are balanced and minimized. In [4] the fuzzy inference system was used to dynamically schedule queues by considering priority. However, only the queue capacity was utilized as the decision parameter and the performance of the mechanism with respect to delay was not investigated. In [9] the inductive approach to packet scheduling using the fuzzy logic concept, though is promising, might be challenging in real-time due to the computational complexity in the proposed agent learning. And in [6] and [7], the delay for real-time system was considered in the code-division multiple access (CDMA) and mobile adhoc networks (MANET), respectively. But, only the delay at the user interface was considered in [6]. Contributions. In this paper, a fuzzy-based packet scheduling method that achieves the three service policies is proposed. The decision methodology of the proposed system follows closely to that presented in [10]. The fuzzy logic based decision concept is utilized at the scheduler, which is simply referred as the fuzzy scheduler. The fuzzy scheduler controls the order that queues are scheduled and the rate of packet dropping for queues, but with particular concern for the queue with

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delay susceptible packets. For the scheduler to achieve that, a set of parameters such as the queue priority, drop priority and queue type are fed to the scheduler. These parameters are jointly used to achieve the degree of fairness for each queue type serviced. Threshold values are obtained based on the fuzzy system parameters. And then are fed into the edge routers to exert some level of control over how packets are dropped and how queues are serviced. However, each packet is queued based on their QoS demands using the differentiated services model[11] . The network of study in this paper is the point-to-point wide area network (WAN). The performance gain of the fuzzy scheduler is shown with respect to the delay at the router nodes, link utilization and traffic dropped. Organization. This paper is organized as follows. We present in Section 2 the background on queue scheduling and the limitations of the conventional scheduling methods in details. Section 3 introduces the proposed fuzzy logic based decision approach employed at the fuzzy scheduler. Section 4 presents the simulation results and discussions. Finally, the conclusions are provided in Section 5. 2

Queue Scheduling

The Internet is the network of queues[1] . The term “the network of queues” used here and throughout this paper represents the network scenario of multiple users with different applications that are of varying QoS demands. And at the same instance, the users are establishing a communication between the transmitter and the receiver. In such a network, traffic congestion is envisaged. And for the problem of congestion to be avoided or controlled, the switch system (the router) plays a significant role, especially in its resource allocation mechanism. The router must implement queuing discipline that governs how the packets are queued while they are waiting to be transmitted. A queuing discipline can be used to control which packets get transmitted (depending on bandwidth allocation) and which packets get dropped (depending on buffer space). As noted in [12], the queuing discipline affects the latency that the packet experiences. A longer waiting time for packets in the queue translates the delay of packets that are delay susceptible. However, there should be a balance in delay and traffic dropped in such a network so as to efficiently maximize the link resources. According to [13-14], addressing queuing at switches or routers can control congestion and provide guaranteed QoS for traffic, especially the delay susceptible traffic. A typical scenario of a network of queues is illustrated in Fig.1. In the structure TX represents

Chollette C. Chude-Olisah et al.: Fuzzy-Based Dynamic Distributed Queue Scheduling

the transmit point, that is, the packet outbound direction. The queuing challenges are defined at the areas noted with TX. This is because once a packet is in the inbound direction it is assumed to have consumed a fraction of the network’s bandwidth.

able level of service that offers balance in link utilization, minimal delay as well as minimal drop of traffic. The fuzzy scheduler is structured in a way that takes into account the dynamic nature of the Internet traffic as regards to its time-varying traffic arrival process that affects the network states and performance. The efficiency of the fuzzy scheduler will be verified via simulations. 3

Fig.1. Network scenario showing QoS concern areas along the network path.

Most routers by default implement the FIFO scheduling mechanism. It works in a way that the first packet to arrive at the router output port is the first to be transmitted. When the queue is full, the router drops the packet without regard to the flow that the packet belongs to or the QoS requirements of that packet. In overcoming the challenges of the FIFO scheme, the priority queuing (PQ) had its way into queuing. In this scheme, how packets are ordered in the queue is important. It varies from the FIFO queuing in the sense that each packet is marked with a priority mark and placed in the queue according to its priority mark. In that way each queue class is managed by the scheduler in the order of priority. The problem with this mechanism is that packets of low priority class may never be scheduled. The fair queuing proposed in [15] emerged to dispute the efficacy of PQ. The advantage of the fair queuing over PQ is in the ability of ensuring that each flow is maintained in a separate queue. The scheduler then services these queues in a round robin fashion. However, the limitation of the fair queuing is in its lack of support for flows with different demands for network resources. For this reason, Demers in [16] analyzed and proposed the assigning of weight to each separate queue. This method is termed as Weighted Fair Queue (WFQ). The concept of WFQ is to achieve fair link sharing by assigning weights to each packet. The weights are maintained for each flow sharing the same outgoing link[17] , but at the expense of delay[2] . WFQ could be advantageous in terms of bandwidth utilization for lower traffic flow[18] . But a notable neglect of WFQ is on enforcing priority when there is outflow of lower priority traffic at the time the higher priority packets are waiting to be served. In order to address the limitations of FIFO, WFQ and PQ schedulers, this paper proposes the utilization of the fuzzy logic based decision approach for a better scheduler. The proposed method envisages an accept-

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Fuzzy Scheduling Approach

For control systems, the concept of fuzzy logic can be used to model the uncertainty that characterizes such systems. The term “uncertainty” refers to the class of systems that do not have precisely defined criteria of membership[19] . This type of class is typical for most physical real-world classes. The membership criteria for fuzzy logic systems are achieved using sets of inference rules. A general structure of the fuzzy control systems follows the classical schema of Mamdani fuzzy logic systems[20] . There exist three main steps, namely, fuzzification, inference and defuzzification. The combined operation of these systems provides the fairness level f for each queue type to be selected on the basis of the queue state (pj , dj , QLj ) which equals (xi , . . . , xn ) at every schedule step. The variables pj , dj , QLj , f are the queue priority, drop priority, queue limit, and degree of fairness for each queue type (deg.Fairness (queue type)), respectively. In the fuzzification process, the inputs (pj , dj , QLj ) to the fuzzy system and the degree of membership which each of the aforementioned inputs belong to each of the appropriate fuzzy sets A(A = {Ai , . . . , An }) is determined using membership function µA(xi ), where xi denotes the crisp values for the inputs and µA(xi ) defines the degree of membership of xi in the fuzzy set A. Generally µA(xi ) is described as a curve that specifies the mapping of each point from the input space to a degree of membership in the range from 0 to 1. This range represents the normalized values for the fuzzy input variables, which can be presented in the form: ½ 1, if x = x0 , Ai (x) = (1) 0, otherwise. For the inference mechanism step, mapping of input fuzzy sets Ai (Ai = (Ai,1 , Ai,2 , . . . , Ai,n )) to the output fuzzy sets Bi are achieved by sets of rules. The Mamdani’s fuzzy inference method applied in [21] is used in this paper for the inference process of mapping from input to output. The inference mechanism is dependent on the rule base, which comprises of sets of If-Then rules. The implication and aggregation of the rules are vital for decision-making, which is required for the fuzzy control systems. The defuzzification step

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takes the fuzzified input and returns a single crisp output value which we call the degree of fairness level f . Here the centre of gravity (CoG), which returns the centre of the area under the curve, is given as[22] : X 1 f = COG = µoutput (xi , . . . , xn )c, (2) output where c is the centre point for each output membership function in the output fuzzy set Bi and µoutput (xi , . . . , xn )c is the strength of the output membership function. CoG is adopted here, due to the fact that it is extensively used in literature[20] . 3.1

Fuzzy Scheduler

The proposed fuzzy scheduling system is illustrated in Fig.2. The structure is to serve as a pictorial representation of the output port of the Internet router.

each queue type, then delay and minimal drop of packets can be achieved for a system of finite capacity. 3.2

Fuzzy Rule Base

In order to capture the real network for the estimation of the behavior of the system, all possible combinations of input variables are considered. There are about 27 rules (the possible combinations) employed in this paper, which can be seen in Table 1. The rules are fed into the fuzzy logic controller to get the desired output values. The system of rules known as the fuzzy rules comprises of a set of If-Then rules used to control a dynamically evolving system. The rules are developed from the relationship between the input and output variables drawn from the knowledge of the erratic behavior of packets[23] . The scheduler is tuned to suite the observed system behavior in real time. Table 1. Fuzzy Scheduler Rule-Base of Three Inputs and Three Fuzzy Sets

Fig.2. Queue scheduler controllable architecture.

Here we consider three queues, q0, q1 and q2, where q0 < q1 < q2 with limited capacities. All the queues are subject to priority, that is, packets are placed in queues according to their priority classes identified by the classifier. The choice of prioritizing packets influences how queues are served. The challenge here is in determining the particular queue to be served by the fuzzy scheduler while the link resources are consistently distributed across queues in order to achieve near fairness. Our approach closely relates to PQ but differs in the fact that it considers other aspects of queuing challenges that the switch system experiences. They are as follows: 1) the time-varying arrival of packets changes the queue length from its initial state to a new state, possibly an increased state; and 2) the direct consequence of 1) is that the drop rate of packets increases due to congestion. The challenge here is on how the drop rate of delay sensitive packets can be minimized, yet achieving the transmission control for all queue types. The aforementioned switch state occurs when too many packets seek to use resources with limited network capacity. If a certain queue limit (that is the maximum number of packets waiting in each queue) and drop susceptibly (expressed as priority in percentage) are determined for

Rule No. p d 1. Lw Lw 2. Lw Lw 3. Lw Lw 4. Lw M 5. Lw M 6. Lw M 7. Lw H 8. Lw H 9. Lw H 10. M Lw 11. M Lw 12. M Lw 13. M M 14. M M 15. M M 16. M H 17. M H 18. M H 19. H Lw 20. H Lw 21. H Lw 22. H M 23. H M 24. H M 25. H H 26. H H 27. H H Note: Lw : Low, M: Medium, H: High, SL: Slightly low, SH: Slightly high.

QL f S SL M SL Lg H S Lw M SL Lg SL S Lw M M Lg H S M M SH Lg H S SL M Lw Lg M S Lw M SL Lg M S H M SH Lg Lw S SL M Lw Lg M S Lw M Lw Lg SL S: Short, Lg : Long,

A general fuzzy control rule consists of a set of linguistic rules which can be represented as[24] : Ri = If xi is Ai,1 and/or · · · xn is Ai,n Then fi is Bi ,

Chollette C. Chude-Olisah et al.: Fuzzy-Based Dynamic Distributed Queue Scheduling

where (xi , . . . , xn ) denotes the fuzzy system inputs and Ai,m and Bi are the associated fuzzy sets for input xm and output for rule i, respectively. As it can be inferred Ai,m and Bi represents the antecedent and consequent part of the rules respectively. All the 27 rules adopted in the fuzzy controller can be expressed as: 1) If p is Low, d is Low, and QL is Short Then f1 is Slightly Low; 2) If p is Low, d is Low, and QL is Medium Then f2 is Slightly Low; 3) If p is Low, d is Low, and QL is Long Then f3 is High; 4) If p is Low, d is Medium, and QL is Short Then f4 is Low; 5) If p is Low, d is Medium, and QL is Medium Then f5 is Slightly Low; 6) If p is Low, d is Medium, and QL is Long Then f6 is Slightly Low; 7) If p is Low, d is High, and QL is Short Then f7 is Low; 8) If p is Low, d is High, and QL is Medium Then f8 is Medium; 9) If p is Low, d is High, and QL is Long Then f9 is High; 10) If p is Medium, d is Low and is Short Then f10 is Medium; 11) If p is Medium, d is Low, and QL is Medium Then f11 is Slightly High; 12) If p is Medium, d is Low, and QL is Long Then f12 is High; 13) If p is Medium, d is Medium, and QL is Short Then f13 is Slightly Low; 14) If p is Medium, d is Medium, and QL is Medium Then f14 is Low; 15) If p is Medium, d is Medium, and QL is Long Then f15 is Medium; 16) If p is Medium, d is High, and QL is Short Then f16 is Low; 17) If p is Medium, d is High, and QL is Medium Then f17 is Slightly Low; 18) If p is Medium, d is High, and QL is Long Then f18 is Medium; 19) If p is High, d is Low, and QL is Short Then f19 is High; 20) If p is High, d is Low, and QL is Medium Then f20 is Slightly High; 21) If p is High, d is Low, and QL is Long Then f21 is Low; 22) If p is High, d is Medium, and QL is Short Then f22 is Slightly Low; 23) If p is High, d is Medium, and QL is Medium Then f23 is Low;

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24) If p is High, d is Medium, and QL is Long Then f24 is Medium; 25) If p is High, d is High, and QL is Short Then f25 is Low; 26) If p is High, d is High, and QL is Medium Then f26 is Low; 27) If p is High, d is High, and QL is Long Then f27 is Slightly Low. 3.3

Fuzzy Logic Application Example

In order to clarify how the fuzzy reasoning strategy of the scheduler and the concepts discussed in the previous subsections aid in queue selection, the Mamdani fuzzy logic control system steps shown in Fig.3 used to derive f are presented. f is a crisp value that indicates the degree of fairness service for the queue type (deg.Fairness(queue type)) (in its priority) selected.

Fig.3. Fuzzy logic control system inference engine.

The Mamdani fuzzy inference method is used because it is simple to implement and is a better representation for the system modeled. It is likewise considered appropriate for multi-input single-output model[21] . Given the linguistic variables, the values that represent the appropriate membership functions and the rules are defined. We set the AND “minimum” logical operators, for the antecedent and consequent parts of the implication process for each and every rule. The minimum truncates the output fuzzy set. A weight value of 1 is assigned to all the rules such that the implication process is not affected. For the implication process, a single number is given by the antecedent as the input and the output is a fuzzy set known as the consequent. All the rules are combined into a single fuzzy set using maximum operation. This process is referred to as the aggregation process. After the aggregation is achieved, the single independent fuzzy set is then defuzzified. The defuzzification process returns a single crisp output value from the set. CoG given in (2) is used for the defuzzification process. The output of the aggregation and the

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Fig.4. Rule viewer for degree of fairness for each queue type selected.

Fig.5. Surface viewer that shows the inference engine output.

defuzzification can be seen in Fig.4. Fig.5 shows the surface viewer for the inference engine output. It can be observed that for the queue p with priority value 70%, if the queue limit QL is about 38% and is set to a drop rate of 7% (probability of dropping the packet in the queue), the degree of fairness f for serving the queue will be high. The single crisp value 75% shows that the degree of fairness for the high priority queue is high. 4 4.1

riving traffic includes the voice, video and file transfer protocol (FTP) traffic. The voice application is configured with the pulse code modulation (PCM) so as to maintain speech quality. A differentiated service code point (DSCP) “11011000” is assigned to the voice application due to its intolerance to delay. We set the video application to take a value that maintains low resolution. The expedited forwarding (EF) class, the DSCP of “1011000”, is assigned to the video traffic. And the FTP application is configured with high load and best-effort service (0) because it is not strictly delay susceptible. The connections of all nodes are set to exponential distribution with mean outcome of 300 seconds.

Fig.6. Implementation scenario.

Simulation Results and Discussions Network Scenario

To demonstrate the performance of the fuzzy scheduler, the typical network scenario of concern in this paper is shown in Fig.6. The diagram illustrates the Internet, which we refer to as the network of queues. Four IP router nodes, the edge routers (ER 1 and ER 2), core routers (CR 1 and CR 2) are given. These routers are set to be of class-based scheduling capabilities and are connected by the point-to-point protocol digital signal, level 1 (PPPDS1) link of 1.544 Mb/s T1 interface. The Ethernet workstations and servers that are linked to the edge routers are connected with the 10BaseT links. The ar-

4.2

Implementation at Edge Router

We show through simulation that by controlling the probability of packet drop and the number of packets waiting in queue, using queue priority that each queue type can achieve near fairness. The threshold values can be seen in Table 2. The values were derived from the fuzzy inference system process presented in Figs. 3∼5. Subsequent values asides the one shown in Fig.4 were derived by tuning the fuzzy inference system offline to determine values that will enforce some level of control for the scheduler. As can be observed in Table 2, drop priority for the q0 is high. This is due to the fact that q0 queues the

Chollette C. Chude-Olisah et al.: Fuzzy-Based Dynamic Distributed Queue Scheduling Table 2. Thresholds Adopted in the Simulation Scenario

q0 q1 q2

Queue Type (%) 25 50 75

Queue Limit (packets) 80 50 20

Drop Priority (%) 60 20 5

non-critical packets that are not affected by delay. However, it can be seen from Table 3 that the proposed fuzzy method (Fuzzy) keeps delay low across the nodes. This is due to the fact that the fuzzy mechanism maintains minimal level of delay at q1 and q2 interfaces. Even though the fuzzy scheduler operates in a more vigorous mechanism as compared to PQ it still achieves low average delay at the router nodes (IF10 and IF11 interfaces), and differs from PQ by 0.01 second, which can be seen from Table 3. However, a large difference between the fuzzy method and FIFO and WFQ can be observed in Table 3. In Fig.7 the link utilization for the proposed fuzzy method, PQ, WFQ and FIFO are shown. The performance of the mechanisms under an increasing traffic load is observed. It is evident that the link utilization increases as the traffic load increases. The fuzzy method is proven to be more efficient in terms of link utilization over other mechanisms as shown in Figs. 7(b)∼7(d). It can be seen that it maintains a continuous level (steadiness) of utilization above 85% over time at increased traffic load as compared with WFQ, PQ and FIFO. The regression of the graph for FIFO, WFQ and PQ shows an occurrence of congestion collapse. This graph regression is as a result of the inability of the mechanisms to handle the increasing of-

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fered load at the time the regression occurs. The regression effect observed for PQ and WFQ is seen to be below 50%, while that for FIFO is below 40%. These percentages indicate link underutilization. A consequence of the link underutilization for FIFO, WFQ and PQ can be seen in Fig.8. In Fig.8, it can be seen that the proposed fuzzy method has minimal number of dropped packets. The Table 3. Queue Delay Statistics for Fuzzy Controlled Scheduler (FCS), PQ, FIFO, WFQ Scheduling Interface Queue Method

Fuzzy

IF10

IF11

PQ

IF10

IF11

q0 q1 q2 q0 q1 q2

Max. Delay Average Variation (s) = Delay (s) maximum delay − minimum delay 0.00 0.16 0.51 0.01 0.00 0.52 0.01

q0 q1 q2 q0 q1 q2

0.00 0.48 0.02 0.00 0.52 0.01

0.17

FIFO

IF10 IF11

q0 q0

2.41 2.35

2.38

WFQ

IF10

q0 q1 q2 q0 q1 q2

0.48 8.20 0.01 0.75 8.26 0.01

2.95

IF11

Fig.7. Link utilization in percentage. (a) Fuzzy. (b) PQ. (c) WFQ. (d) FIFO.

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Fig.8. Number of packets dropped per second.

fuzzy method drops about 150 packets per second whereas PQ, WFQ and FIFO scheduling mechanisms drop over 200 packets per second. 5

Conclusions

In this paper the fuzzy logic based decision algorithm was employed to achieve certain level of control at the queue scheduler. A significant performance gain by the fuzzy scheduler in comparison with FIFO, PQ and WFQ was in terms of the traffic dropped. It also achieved minimal queuing delay at the router nodes and good link utilization. It should be noted that though the queue delay performances for the fuzzy method and PQ are close despite the fuzzy method’s consideration for the three stipulated service policies itemized in Section 2, the fuzzy method still outperformed in terms of traffic dropped and link utilization. As future work, we aim to improve the delay performance of the fuzzy scheduler. References [1] Sheldon T. Encyclopedia of Networking and Telecommunications (Network Professionals Library). USA: Osborne/McGraw-Hill Press, 2001. [2] Guo Z, Zeng H. Simulation and analysis of weighted fair queuing algorithms in OPNET. In Proc. ICCMS, Feb. 2009, pp.114-118. [3] Padjen R, Keefer L, Thurston S et al. Flannagan and Martin Walshaw. Cisco AVVID and IP Telephony Design & Implementation. [4] Cho H, Fadali M, Lee H. Dynamic queue scheduling using fuzzy systems for Internet routers. In Proc. the 14th IEEE Int. Conf. Fuzzy Systems, May 2005, pp.471-476. [5] Cho H, Fadali M, Lee J et al. Lyapunov-based fuzzy queue scheduling for Internet routers. Journal of Control, Automation and Systems, 2007, 5(3): 317-323. [6] Bolin N, Lemin L. Novel fuzzy scheduling supporting quality of service for wideband CDMA cellular networks. In Proc. IEEE Int. Conf. Communications, Circuits and Systems, May 2005, pp.368-373. [7] Gomathy C, Shanmugavel S. An efficient fuzzy based priority scheduler for mobile ad hoc networks and performance analysis for various mobility models. In Proc. IEEE WCNC,

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Chollette C. Chude-Olisah received the B.Sc. degree in computer science from Anambra State University, Nigeria, in 2006 and the M.Sc degree in computer science from Universiti Teknologi Malaysia (UTM), in 2011. Currently, she is pursuing her Ph.D. degree in UTM. Her research interest includes image processing and understanding, pattern recognition, multimedia communication and processing.

Chollette C. Chude-Olisah et al.: Fuzzy-Based Dynamic Distributed Queue Scheduling Uche A. K. Chude-Okonkwo obtained the B.Sc. degree in electrical/electronic engineering from Nnamdi Azikiwe University, Eastern Nigeria, in 1999, and the M.Sc. degree in communication engineering from University of Lagos, Western Nigeria, in 2003. In 2010, he received the Ph.D. degree in electrical engineering from UTM. Currently, he is a senior lecturer with the Wireless Communication Center, UTM. His research interests include signal processing for wireless communication and bioinspired system design. Kamalrulnizam A. Bakar obtained his Ph.D. degree from Aston University, Birmingham, UK, in 2004. Currently, he is an associate professor in computer science at UTM and a member of the “Pervasive Computing” research group. His specialization includes mobile and wireless computing, information security and grid computing.

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Ghazali Sulong received his Ph.D. degree in computing and M.Sc. degree in computing from University of Wales College Cardiff, UK in 1989 and 1982 respectively, and B.Sc. degree in statistic from National University of Malaysia (UKM), in 1979. Currently, he is a professor and principle researcher in image processing in UTM.