An Improvement Over Random Early Detection Algorithm: a Self-Tuning Approach

http://jecei.srttu.edu Journal of Electrical and Computer Engineering Innovations JECEI, Vol. 2, No. 2, 2014 SRTTU Regular Paper An Improvement over Random Early Detection Algorithm: A Self-Tuning Approach Shahram Jamali1, Neda Alipasandi2,*, and Bita Alipasandi3 1Computer Engineering Department, University of Mohaghegh Ardabili, Ardabil, Iran 2Sama technical and vocational training college, Islamic Azad University, Ardabil Branch, Ardabil, Iran 3Young Researchers and Elite Club, Ardabil Branch, Islamic Azad University, Ardabil, Iran *Corresponding Author’ Information: Tel.: +98-45-33361902, E-mail address: [email protected] ARTICLE INFO ABSTRACT Random Early Detection (RED) is one of the most commonly used Active ARTICLE HISTORY: Queue Management (AQM) algorithms that is recommended by IETF for Received 15 December 2013 deployment in the network. Although RED provides low average queuing Revised 1 February 2014 delay and high throughput at the same time, but effectiveness of RED is Accepted 15 February 2014 highly sensitive to the RED parameters setting. As network condition varies largely, setting RED's parameters with fixed values is not an KEYWORDS: efficient solution. We propose a new method to dynamically tuning RED's Internet parameters. For this purpose, we compute the rate of which the queue is Congestion control occupied and consider it as a congestion metric that will be forecasted Active Queue Management when the queue is overloaded. This meter is used to dynamically setting (AQM) RED parameters. The simulation results show the effectiveness of the Random Early Detection proposed method. According to the results, we achieve a significantly (RED) higher utilization and less packet loss comparing to original RED Self-Tuning algorithm in dynamic conditions of the network. 1. INTRODUCTION congestion control by two different ways [1]: scheduling and queue management. Scheduling is not Congestion control of internet plays an important role in this scope. Queue management algorithms manage in availability and stability of internet network. the queue occupation by dropping packets when Nowadays, Transmission Control Protocol (TCP) is the necessary or appropriate [1]. Queue management dominant protocol among the Internet protocols. A algorithms, from the point of dropping packets, can be major reason is that congestion control and avoidance categorized into two classes: The first class is Passive mechanism employed in TCP contributes to internet Queue Management (PQM), which does not apply any congestion control. TCP flows at the end host use preventative packet drop before the router buffer packet loss or Round-Trip Time (RTT) difference to becomes full or attains a predefined value. The second detect and react to congestion by decreasing the class is Active Queue Management (AQM), which transmission rate. Many researches [1]-[3] apply preventative packet drop before the router demonstrate that end-to-end congestion control buffer becomes full. mechanisms (such as one employed in TCP) are Drop-tail is one of the traditional PQM algorithms obligatory, but they are not adequate, so congestion that drops incoming packets when the queue is full. control should be assisted by routers; otherwise the The drawbacks of using PQM algorithms in routers danger of congestion collapse still exists, because the are that, first, there will be high queuing delays at end host cannot singly dominate the unresponsive congested routers [4], because queue is often full; this and non-TCP-compatible flows. The routers chip in happening is called full-queue phenomenon, second, a 57 J. Elec. Comput. Eng. Innov. 2014, Vol. 2, No. 2, pp. 57-61 An Improvement over Random Early Detection Algorithm: A Self-Tuning Approach few number of flows can monopolize the queue below maxth, drop probability of packets will be whereas the other flows perpetually get fined; this p(avg). event is called lock-out phenomenon and caused unfairness [1], [5], [6]. = (1 − ) + (1) Random Early Detection [7] (RED) is one of the most prominent AQM algorithms as a solution for the − () = (2) full-queue and lock-out phenomenon. RED is − suggested by Internet Engineering Task Force (IETF) for deployment in the network [1]. Although, RED Lock-out and full-queue phenomenon that lead to provides simultaneously high throughput and low global synchronization can be prevented by RED average queuing delay [4], but effectiveness of RED is gateways. Also, RED gateways can reduce packet loss highly sensitive to the RED parameters setting [8]- rates, and minimize biases against burst sources [16]. [10]. However original RED uses fixed values for its All of these, as regards that RED has low-overhead parameters, and as regards network condition varies and simple algorithm have led the IETF to recommend largely, fixed parameters setting of original RED the use of RED as a default active queue management algorithm is not efficient. Many researchers [11], [12] in Internet routers. Since RED was introduced in investigated the behavior of the RED in various 1993, many researches have been done to study its conditions or proposed modification in [13], [14] over performance [8]-[12], [17] and many enhancements RED algorithm. or variants were proposed [4], [16], [18]-[27]. These In this paper, to rectify the shortcoming of the studies shown that although RED can surpass original RED algorithm, we propose a new method to traditional drop-tail queues and can improve TCP dynamic tuning of RED's parameters based on performance, but the performance of RED network condition. For this purpose, we introduce a considerably depends on the values of its control new measure that foresights network future load and parameters. then RED's parameters are adjusted based on it. The In heavily congested networks, RED's congestion proposed algorithm has been implemented in NS2 notification must be more aggressive, in order to [15] simulator. Simulation results show the avoid packet loss due to buffer overlow [16]. On the effectiveness of the proposed algorithm which solves other hand, in lightly congested networks, RED's the parameters setting problem and enhances RED's congestion notification must be less aggressive, in performance, remarkably. order to both preventing excessive packet drop and to The rest of this paper is organized as follows. maintain link utilization at an acceptably high level. Section 2 briely describes RED algorithm. In Section But, the aggressiveness level of RED significantly 3, we introduce our new measure which called QRT, depends on setting of its control parameters. and then describe our improved RED algorithm which Nonetheless the value of these control parameters in called QRTRED. Section 4 presents the simulation original RED algorithm is invariable and hence as results, and inally section 5 brings concluding regards network condition varies largely, the remarks. invariant aggressiveness of RED is not efficient in dynamic condition of network. 2. ORIGINAL RED ALGORITHM AND ITS PROBLEM In order to RED router to be efficient, its control parameters should be set to proper values according The original idea of RED algorithm, which was first to the condition of the network (congestion level, the proposed by Floyd and Jacobson [7], is preventing number of active connections, link bandwidth, and so buffer overflow by early detection of full queue. For on). However, as noted by other researchers [9] , [10] this purpose, RED uses four control parameters which it is infeasible to gain one parameter set to make the are minimum threshold min , maximum threshold th RED algorithm work effectively in dynamic condition max , maximum mark (drop) probability Max , queue th p of network. If the control parameters of RED are not weight w . RED detects incipient congestion status by q configured correctly, then its throughput is often calculating weighted average of the queue length under that of drop-tail routers. which hereinafter is denoted by avg, and alerts TCP senders that congestion has happened by dropping packets deliberately at a certain probability which 3. PROPOSED SOLUTION hereinafter is denoted by p(avg). avg and p(avg) are To overcome the limitations of RED algorithm, we deined as Equation (1) and (2), respectively. In introduce a new meter which indicates network original RED algorithm the value of minth and maxth condition, and then, RED's control parameters are are 5 and 15, respectively. If avg was lower than minth, configured dynamically based on it. Our new metric, then drop probability of packets will be zero. If avg that called QRT, infers network conditions from was higher than maxth, then drop probability of rapidity of the buffer occupancy in the router. To do packets will be one. If avg is higher than minth but still this we measure speed of change in the quantity of occupied cells of buffer. If the quantity of occupied 58 Shahram Jamali et al. cells is increased, then the QRT will have a positive dynamic sources are used to simulate the dynamic value proportional to the network congestion level, condition of network. The simulation time is 100 sec. meaning that offered load to the network is In our simulation, we compare tree router discipline. increasing. If the quantity of occupied cells is The first discipline is the RED with default value, decreased, scilicet the quantity of free cells is second is the RED with minth=10 and maxth=40, and increased, then the QRT will have a negative value, third is the QRTRED. meaning that offered load to the network is decreasing. So, with QRT metric that indicates network dynamic conditions, we can dynamically adjust the RED's control parameters proportional to network dynamic conditions. In our algorithm, the aggressiveness of RED is tuned by updating of only maxth and minth based on QRT value. The Fig. 1 shows the proposed algorithm to dynamically RED's parameter setting, where T is the Figure 2: Network simulation topology total buffer size and Ei is the number of occupied cell of buffer in interval i.

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