A novel self-tuning feedback controller for active queue management supporting TCP flows

Abstract

Wireless access points act as bridges between wireless and wired networks. Since the actually available bandwidth in wireless networks is much smaller than that in wired networks, there is a bandwidth disparity in channel capacity which makes the access point a significant network congestion point. The recently proposed active queue management (AQM) is an effective method used in wired network and wired–wireless network routers for congestion control, and to achieve a tradeoff between channel utilization and delay. The de facto standard, the random early detection (RED) AQM scheme, and most of its variants use average queue length as a congestion indicator to trigger packet dropping. In this paper, we propose a Novel autonomous Proportional and Differential RED algorithm, called NPD-RED, as an extension of RED. NPD-RED is based on a self-tuning feedback proportional and differential controller, which not only considers the instantaneous queue length at the current time point, but also takes into consideration the ratio of the current differential error signal to the buffer size. Furthermore, we give theoretical analysis of the system stability and give guidelines for the selection of feedback gains for the TCP/RED system to stabilize the instantaneous queue length at a desirable level. Extensive simulations have been conducted with ns2. The simulation results have demonstrated that the proposed NPD-RED algorithm outperforms the existing AQM schemes in terms of average queue length, average throughput, and stability.

Introduction

Internet congestion occurs when the aggregated demand for a resource (e.g., channel bandwidth) exceeds the available capacity of the resource. Congestion, due to the speed mismatch between a wired network and wireless LAN (WLAN) at the access point (AP), is regarded as a critical problem that affects the overall performance of WLAN. Congestion typically results in long delays in data delivery, wasted resources due to dropped packets, and the possibility of a congestion collapse [1], [2], [46]. Congestion control is an essential technology on the Internet, which can usually be performed by two methods: (1) by an end-to-end protocol, such as TCP and (2) by an active queue management (AQM) scheme, which is implemented in routers [3], [47], [48] and employed to control traffic [4], [5]. The basic philosophy of AQM is to trigger packet dropping (or marking, when explicit congestion notification [6], [7] is enabled) before the buffer overflows, and the drop probability is proportional to the degree of congestion. AQM can achieve smaller queuing delays and higher throughput by purposely dropping packets. AQM [8], [9] can be classified into two types: (1) rate-based, which controls the flow rate at the congested channel (e.g., [1]), and (2) queue-based, which controls the queue at the congested channel (e.g., [10], [11]). There are several AQM schemes that have been reported in the recent literature for congestion control.

Random early detection (RED) [11], [12], [13], [14] recommended for deployment by the Internet Engineering Task Force (IETF), is the most prominent and well-studied AQM scheme [15], [16]. It is based on queue management, and has been widely implemented in routers for congestion control on the Internet. The main objective of RED is to keep the average queue length (average buffer occupancy) low. To accomplish this target, RED randomly drops incoming packets with a probability proportional to the average queue length, which makes the RED scheme suitable for irregular bursts of traffic. One important factor in measuring the performance of a traffic controller is stability: the stability of packet drop rate and the stability of queue length. A drawback of the RED method is that it is difficult to set the parameters of the RED traffic controller to stabilize the system under the diversity of Internet traffic [17], [18]. The problem becomes especially severe when the average queue length reaches a certain threshold, which may result in a sharp decrease of throughput and an increase of the drop rate [3].

There are several variants of RED that have been proposed to address the above problem, such as Adaptive-RED [10], [19], [20], Proportional Derivative RED controller (PD-RED) [3], Proportional Integral controller (PI-RED) [21], [22], and so on. With RED [11], [12], [13], [14], the resulting average queue length is very sensitive to the level of congestion and initial parameter settings, which makes its behavior unpredictable [21]. Adaptive-RED attempts to stabilize router queue length at a level independent of the active connections, by using an exponentially weighted moving average as an integral controller [10], [19], [20]. Sun et al. proposed a new RED scheme [3] based on the proportional derivative control theory, called PD-RED, to improve the performance of the AQM. Unfortunately, neither Adaptive-RED nor PD-RED provides any systematic method to configure the RED parameters. Moreover, the control gain selection in both methods is based only on empirical observation and simulation analysis, which can only work well in certain given situation. A theoretical model and analysis for control gain selection and parameter setting are required. Holot et al. [21] proposed a Proportional Integral controller, PI-RED, as a means to improve the responsiveness of the TCP/AQM dynamic, and stabilize the router queue length around the target value. Similarly, Deng et al. [23] proposed a Proportional Integral Derivative model, to improve system stability under dynamic traffic conditions. Both methods used feedback control theory to describe and analyze the TCP/RED dynamic. However, the use of a highly simplified linear quadratic Gaussian controller [24] limited their discussion to the classical control elements. Consequently, their methods can only directly channel traffic control parameters to one of the AQM objectives, which leads to poor global performance. Xiong et al. proposed a Self-tuning Proportional and Integral RED (SPI-RED) [25] on average queue length, to regulate queue length. The average queue scheme keeps the average queue length low, but still allows occasional bursts of packets in the queue. In contrast, this paper proposes a novel proportional and differential control based on instantaneous queue length, called NPD-RED, which effectively avoids the problem of occasional bursts of packets.

Wireless access points act as bridges between wireless and wired networks. Since the actually available bandwidth in wireless networks is much smaller than that in wired networks, there is a bandwidth disparity in channel capacity which makes the access point a significant network congestion point. Only a handful of research papers have explored the AQM issues in WLAN. In [26], Xu et al. presented an AQM scheme for WLAN, but the performance analysis of the proposed AQM scheme was limited, because the authors only considered the delay from wired network to WLAN. On the other hand, throughput and average queue length are generally accepted as more important metrics in evaluating an AQM performance. In [27], Pang et al. mainly focused on the comparative analysis of different versions of TCPs, particularly TCP Veno [28] and TCP Reno under RED and Tail-Drop (TD) Queue. This study concluded that RED does not help in throughput in WLAN. However, it lacks the detailed analysis about the reason why RED can result in the performance degradation [29].

The main contributions of this paper are as follows. First, we propose a novel feedback control scheme, called NPD-RED, for the TCP/RED dynamic time-delayed model in wired network and wired–wireless network routers (refer to [13], [30]). The core idea is new probability function for packet dropping. At the packet level, NPD-RED uses the changes in the instantaneous queue and the differential in queue length to update packet drop probability upon the arrival of new packets. On larger time-scales, NPD-RED dynamically adjusts the packet drop probability using the measured packet loss ratio. Second, we provide a theoretical analysis of the stability of the proposed probability function, and give theoretical guidance in determining the parameters for the proposed NPD-RED method. Our theoretical analysis is based on a TCP dynamic model, and uses optimal control methodologies. Furthermore, in the proposed NPD-RED method, these key parameters are decoupled from other tuning parameters, as well as from the parameters related to network conditions. The controlled parameters are adapted within dynamically changing ranges, which are determined by the stability condition. This makes the system analysis more realistic, and we propose this new configuration of NPD-RED to enhance the network performance. Finally, we conduct extensive simulations to compare the performance of our proposed method with other existing schemes. The simulation results demonstrated that the NPD-RED algorithm outperforms the existing schemes (RED, PI-RED, Adaptive-RED, and PD-RED).

The remainder of the paper is organized as follows. In Section 2, we present the system model and definitions. Section 3 discusses the NPD-RED algorithm, and develops guidelines based on theoretical analysis for choosing the parameters to achieve system stability. In Section 4, we carry out simulations under a variety of network scenarios, and analyze the performance of the proposed method. Section 5 explains more work related with AQM. Finally, we make conclusions and discuss future work in Section 6.