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SAM: Support Vector Machine Based Active Queue Management

Recent years have seen an increasing interest in the design of AQM (Active Queue Management) controllers. The purpose of these controllers is to manage the network congestion under varying loads, link delays and bandwidth. In this paper, a new AQM controller is proposed which is trained by using the SVM (Support Vector Machine) with the RBF (Radial Basis Function) kernal. The proposed controller is called the support vector based AQM (SAM) controller. The performance of the proposed controller has been compared with three conventional AQM controllers, namely the Random Early Detection, Blue and Proportional Plus Integral Controller. The preliminary simulation studies show that the performance of the proposed controller is comparable to the conventional controllers. However, the proposed controller is more efficient in controlling the queue size than the conventional controllers.

INTRODUCTION [3]. All algorithms have their own design and different parameters with some advantages and problems. The research is being carried out to get more and more stable controller.
SVMs are robust classifiers and can be used in variety of fields where classification is required. SVMs can predict the unknown data. This paper introduces SAM controller that actively manages the queue using SVM algorithm.
The remaining paper is organized as follows.  TCP is connection oriented and reliable protocol. TCP is the dominating protocol throughout the world due its features since last two decades. It identifies the congestion only after a packet has been dropped [4]. It uses TCP window to avoid the network congestion with slow start and gradual rise in window size if acknowledgment is received and vice versa [5].
AQM is considered as an effective way for congestion control [6]. It effectively achieves a balance between link utilization and delay [7]. It controls the queue before queue becomes full and packet drops occurs. AQM maintains the small queues and reduces the delays. AQM has natural capacity to absorb packet bursts without any drops. During overflow conditions more packets drop without AQM [8].
The major objectives of AQM are to maintain stable and fair queues [9]. RED, FRED, Blue, SFB and CHOKe were compared and performance of RED and Blue was found better than others in link utilization and others maintained fairness [10].

RED algorithm was introduced by Floyd and Jacobson
in 1993 [1]. It is widely used AQM controller. RED maintains two queue thresholds, such as minth and maxth. No packet will be dropped when queue is less then minth, but, if it exceeds maxth the all incoming packets will be dropped. The RED controller is composed of two major parts. First is the average queue size estimation and other is the decision function to drop the packets. The marking probability will be maintained if the queue is between minimum and maximum [8].
RED has some major issues related with its design. If it is designed aggressively it will show extremely fast response but low margin for stability. If it is designed to be more stable it has very slow response time. RED creates a relationship between queue length and loss probability which results in steady state errors [3]. RED causes oscillations and instability due to the parameter variations [11].
Blue algorithm was introduced by Feng, et. al. [2]. The Blue AQM controller manages queue based link ideal time and packet loss instead of queue lengths. Blue maintains a single Pm (marking probability) to mark/drop packets.
Blue decreases its Pm when link is idle and increases Pm when it finds continuously packets drop due to buffer overflow [12].
Problems associated with Blue are large queue size cannot absorb TCP Bursts and packets have to wait (Increased Round Trip Time). Its performance also suffers with wide variation in network load [8].
The PI is well known controller that is widely utilized in the industry. It is highly robust and easy to understand. It results in good performance under instability. PI AQM controller decides for packet dropping based on its dropping probability. This dropping probability is calculated from old probability, current difference of queue length with reference queue and old difference of queue length with reference queue.
Problems associated with PI are that, it is only suitable for linear and deterministic systems and signals. Internet/ Network congestion is non linear and stochastic in nature.
The performance of PI therefore cannot be optimal. The linearization introduces modeling errors. Average queue length increases as number of senders increases. It mainly depends on reference queue [8].
SVMs are one of the successful classifiers in industry and academia. Due to their performance and accuracy to solve the complex problems they are used extensively.
There are two major categories of SVMs, Linear and Non-Linear. Their models are robust to parameter deviation and capable to generalize the unseen data [13,14]. They are also well recognized in continuous adaptive learning.
These features are extremely necessary in network environments [13].

SAM: SVM BASED AQM
The idea behind this controller is to actively manage the queue using machine learning algorithm.  Packet drop rate is shown in Fig. 4. The overall performance of the controllers has been summarized in Table 1. PI performs best and Blue performs worst in terms of throughput and Blue performs best and PI performs worst in terms of extra network overload.

This
Similarly SAM performs better than RED in terms of throughput and RED performs better than SAM in terms of network overload. In broader view each controller has its own advantages and disadvantages. this AQM controller will be tested with better training models.