Managing Network Congestion with a
Modified Kohonen-based RED Queue
Maria Priscilla
Asst.Professor. S.N.R. Sons College, Coimbatore, TamilNadu, India
Dr. Antony Selvadoss Thanamani
Professor and Head, Dept. of Computer Science, NGM College, Pollachi, Tamilnadu, India.
Abstract:
The robustness of today’s Internet depends heavily on the TCP congestion control mechanism.Congestion is a key factor in transmission control protocol (TCP) which leads to many researches. Congestion control techniques are extensively used in order to maintain the stability and reliability of the network. One of the important issues in the congestion control study is the Queue management technique employed by the network. An exponential increase in network traffic causes increasing packet loss rates. So, the IETF is considering the deployment of active queue management techniques to stem the increasing packet loss rates. Even though the packet loss rates are reduced in the internet by means of active queue management, the prevention of high loss rates is still a major concern for the present techniques. The severity of congestion is indicated by the queue lengths which is the main problem in the present queue management algorithms. Therefore, a fundamentally different active queue management algorithm called Random Early Detection (RED) is proposed to overcome the above mentioned problem. BLUE uses packet loss and link idle events to manage congestion. Using simulation and controlled experiments, RED is shown to perform significantly better than other techniques both in terms of packet loss rates and buffer size requirements in the network. Then RED approach is combined with Kohonen technique that enables a stable queue length without complex parameters setting and passive measurements. This paper extends the Kohonen RED technique with the modified Kohonen based RED queue technique. It is clearly observed from the experimental results that the proposed approach provides better recognition accuracy with very low training time.
Keywords: Random Early Detection (RED); Kohonen based RED KRED); Congestion and Adaptive Queue Management (AQM).
1. Introduction
The high pocket loss rates in the internet are very important concern. All of the resources it has consumed in transit are wasted, if the packet is dropped before it arrives at the destination. This situation may lead to congestion collapse in extreme cases. In the past few years, one of the key research fields is the improvement in the congestion control and queue management techniques [5, 6] in the Internet. While a number of proposed enhancements have made their way into actual implementations, connections still experience high packet loss rates [2]. In case of heavy congestion, the loss rates are vey high, when a large number of connections compete for limited network bandwidth. Most recent measurements have shown that the mounting demand for network bandwidth has driven loss rates up across various links in the Internet. In order overcome the increasing packet loss rates caused by an exponential increase in network traffic, the IETF is considering the deployment of explicit congestion notification (ECN) along with active queue management techniques. While ECN is necessary for discarding packet loss in the Internet, this approach shows that RED [3], even when used in conjunction with ECN, is ineffective in preventing packet loss.
exceeds a maximum threshold, all packets are dropped or marked. Even though, RED [8] overcomes the demerits of the previous techniques, it also suffers certain disadvantages. The major drawback of RED technique and other existing active queue management techniques are the usage of queue lengths as an estimator of congestion. While the existence of a determined queue indicates congestion, its length gives very little information as to the severity of congestion, that is, the number of competing connections sharing the link. In the peak time, a single source transmitting at a rate greater than the bottleneck link capacity can cause a queue to build up just as easily as a large number of sources can. Because the RED algorithm relies on queue lengths, it has an inherent problem in determining the severity of congestion. As a result, a wide range of parameters are needed by RED to operate perfectly in different congestion environment. Though RED [9] can achieve an ideal operating point, it can only do so when it has an adequate amount of buffer space and is suitably parameterized. To overcome the problems stated above, Kohonen-based RED queue is presented which that facilitates an even queue length without complex parameters setting and passive measurements. This technique also has disadvantages in the accuracy of selecting the queue length and also takes more time. To neglect all these disadvantages, this paper presents a Modified Kohonen-based RED queue method which can produce better accuracy with less training time.
2. Related Work
There are different techniques exist for the network congestion management [11, 12] which are provided by different authors. Those techniques deal with the queue scheduling and queue memory management. Some of the techniques used are discussed in this section. The AQM algorithms [1] are categorized depending on the decision whether to drop the packets from the queue or not. There are two types of algorithms reactive and proactive. A reactive AQM algorithm means congestion may occur in this state but earlier detection is made. This focuses on congestion avoidance and the decisions are depends on the current congestion. A proactive AQM algorithm acts intelligently and drops packets. This results in congestion prevention from ever occurring. Proactive aims for ensuring high degree of fairness between flows. These types of algorithms are based on expected congestion. The reactive AQM algorithms are further classified into four different categories they are as follows.
2.1. Average queue length queue management
The decision to drop a packet is based on the queue length. This can be based on the available bandwidth or even give attention to fairness.
2.2. Packet loss and link utilization queue management
This classification is based on packet loss and link but does not depend on the queue length. Here a single probability is used to mark or drop packets when they in queue. The single probability is incremented as the buffer is continuously full.
2.3.Class based queue management
Class based management depends on which class the incoming packets belongs. It may be on TCP or UDP [10, 13]. This approach assigns network resources to classes of flows in a predefined manner. When packets arrive, the mechanism not only suggests a solution to the problem of congestion but also offers potential performance guarantees for the multimedia traffic class.
2.4. Control theory based queue management
Control theory maintains the queue length to a reference input. The packet drop probability p is adjusted periodically, based on the error signal and the sum of previous queue length deviations from reference input. With the above said result of these periodic updates, an AQM [14] control system can be modeled in a discrete-time fashion. The random early detection algorithm is a queue length based queue management. The router sends a single packet implicitly to warn the source that congestion has occurred. By noticing the packet the source is expected to reduce the transmission rate to avoid buffer overflow [4]. The parameters used are minth which is the minimum threshold maxth the maximum threshold maxp is the maximum value for the upper bound on the actual marking probability and wq is the queue weight. The average queue length is calculated using
The marking probability pa is calculated as follows:
pa = pmax ((qavg− minth) /(maxth− minth))…………..(2)
2.5. Flow RED (FRED)
FRED tries to provide fair buffer allocation for non responsive flows and for aggressive flows which misbehaves. FRED allows equal queue capacity to maintain uniformity during congestion but for those flows that exceed the average fair share obtain more than their fair share. This is an alternative to RED in order maintain higher degree of fairness. This protects the fragile flows by using per-active-flow that imposes a loss rate which depends on the buffer usage. The parameters minq(i) and maxq(i), represent minimum and maximum number of packets that each flow i is allowed to buffer. Avgcq is the average per-flow buffer count, qlen(i), currently buffered packets , strike(i) for each flow, which is a count of the number of times the flow has failed to respond to congestion notification.
2.6. Adaptive RED (ARED)
ARED gateway measures or uses the average packet enqueue rate as a congestion indicator, and judiciously signals end hosts of incipient congestion, with the objective of reducing packet loss ratio and improving link utilization. When more aggressive early detections or less aggressive early detection occur due to large number of flows or less number of flows a changing parameter algorithm is required and that is called Adaptive RED. Adaptive RED’s marking function changes depending on the setting of maxp. In times of light congestion, the marking/dropping probabilities remain quite low until the average queue length reaches maxth. In times of heavy congestion [15], the marking/dropping probabilities increase quickly as the average queue length exceeds minth. The parameters used are the same RED α and β are scaled constant factors.
2.7. Stabilized RED (SRED)
The basic goal of SRED is to increase the occupancy of the (FIFO) buffer, independently of the number of active flows, by estimating the number of active flows. SRED approximates the number of active flows and uses the quantity to indicate the level of congestion. It uses a zombie list to store recently seen flows with the information about each packet and its time stamp of its arrival. Count measures the number of hits. Hit occurs when a packet matches the zombie when it arrives and the count is incremented a packet arrives, count and the time stamp is set to the arrival time of packet in the buffer. When the two are not of the same flow this is called miss. With a probability p the flow identifier of packet is written on that zombie and count decreased by one. It remains unchanged with a probability 1 - p. The drop probability, however, does not depend on a hit or a miss. If the buffer occupancy demands it, then a packet is dropped.
2.8.Class-based Threshold (CBT)
This releases UDP, flows from strict per-flow punishment while protecting TCP flows [7] by adding a simple class-based static bandwidth reservation mechanism to RED. In fact, CBT implements an explicit resource reservation feature of CBQ on a single queue that is fully or partially managed by RED without using packet scheduling. Instead, it uses class thresholds that determine ratios between the numbers of queue elements that each class may use during congestion. CBT defines three classes: tagged (multimedia) UDP, untagged (other) UDP and TCP. For each of the two UDP classes, CBT assigns a pre-determined static threshold and maintains a weighted average number of enqueued packets that belong to the class.
2.9.Dynamic-CBT
3. Methodology
In RED approach, the evolution of the queue occupancy oscillates between both thresholds (minth,maxth).
Packets arrival rate in the queue should able to withstand by the queue without loss of packets. It is clearly represented in figure 1.
Self-configuring RED schemes update the maxp value as a function of the arrival rate in order to stabilize the
queue size between thresholds, minth and maxth. This technique describes the queue length variation by the need
of dynamically changing maxp as function of the queue occupancy. This technique recomputes this probability
following an AIMD algorithm. The update is done as function of the average queue size. If the average queue size is around maxth, the algorithm increases maxp to drop more packets and decreases maxp if the value is
around minth.
Figure 1: Adaptive RED
Kohonen Self Organizing Map (SOM) is the neural network used in this approach. It consists of a one or two dimensional information processing layer of functional entities called neurons. It is connected to input data seen as input vectors and provides output data also as vectors. Table I represents the entries used to feed the neural network in both cases and the resulting output. The input vector contains the previous and the current queue length and the output vector the maxp probability. The input data is fully plotted onto the Kohonen layer’s
neurons which respond to this data according to the weight assigned to the connexions between input vectors and neurons and deliver an output response vector. To begin with, the neural network is presented a learning set of example input vectors and adjusts (i.e. learns) appropriate weights for its neurons by comparing the input vectors to the weight vectors for each neuron thus electing a ”winning” neuron ”close” to the input vector. In addition to this, the Kohonen SOM deals with a topological learning feature, this implies neural neighborhood generalization of a correct learning experience so as to create clusters of neurons responding to similar input vectors without necessarily having explicitly learnt them. If a neuron learns that a given input vector is a vector it should respond to, its neighbors will learn they also should respond, only in a lesser way, depending on their topological distance to the first ”winning” neuron. This way, the Kohonen SOM is well adapted to stability preservation tasks presented here. Once the learning procedure is over, i.e. when the neural network produces an acceptable amount of erroneous responses during learning, the weights of the neural connexions to the data input are freezed. That means that the training process needs to be done only once without specific scenario and should work for every kind of situation.
Given the Kohonen SOM algorithm, the neural network can generalize its learnt experiences to other input vectors it has never seen before and produce adapted responses. In this way, the conservation of a direction, equilibrium or the correct parameter to adjust a RED mechanism is made possible although there is no way of predicting the way the neural network learns to solve this particular problem. For case presented in this work, the learnt sequences of input vectors are not the ones used in the tests, in order to prove that the learning method provides a general purpose neural network for the resolution of the problem deal with here. Once it has learnt, it can be used indefinitely for the task it has been trained for.
Such a neural network is well adapted to pattern and shape recognition problems, whilst a SOM such as the Kohonen SOM could be better suited to the task of stability preservation which is deal with here. Indeed, this Kohonen SOM algorithm preserves topological relationships between neighboring vectors. Each time a packet is enqueued, the Kohonen network computes a new maxp following the previous and the current average queue
3.1. Modification to Kohonen-based RED queue Technique
Classification of the input vectors with a set of m× n weight matrix is the fundamental operation of Kohonen’s network where m is the number of nodes in input layer and n is the grid size. Learning system in Kohonen-based RED queue Technique considers the previously learned vectors while adopting the weight matrix for the current input vectors that is avoided by the proposed subsystem.
The following modifications are performed on existing system:
Adoption of Weights: Conventional recognition techniques deals with the previously stored vectors which are already been learnt. It increases the learning time exhaustively. Learning time for each vector is a factor of the number of previously learned vectors. But the modified system only tries to operate on the recently given pattern sample. It avoids the previously learned vectors for the swiftness of learning process.
Regular recognition system using KRED offers the modification of weights for all the connections among the two layers. It indicates the static size of neighbors. Due to the rapid change of neighborhood size, number of weight adoption easily decreased with the time. The modified MKRED system proposes a function for changing the neighborhood size along with the change of the distance of winner node:
The proposed method helps in achieving the better accuracy for recognition and also the time required for training will be lesser when compared to the conventional methods.
4. Experimental Results
The performance of the proposed Kohonen’s RED approach is determined by comparing the performance with the conventional techniques. In case of small number of patterns, the performance of the proposed technique is almost same as the conventional techniques. When the number of inputs increases rapidly, the performance of the proposed technique also differs rapidly. The experimentation is performed with up to 25 sample pattern learn at a time.
Table 1: Mean and Standard Deviation for Queue Delay (ms)
AQM Mean Standard
Deviation
RED 29.17 3.05
KRED 21.92 3.02 MKRED 18.23 2.95
The mean and standard deviation for the different AQM technique for queue delay is represented in table 1. From the table, it is clearly observed that the mean obtained for RED technique is 29.17 and for KRED 21.92. The proposed technique results in the mean value of only 18.23. When the standard deviation is considered, the proposed technique obtained only 2.95, whereas, it is higher for existing methods (i.e. 3.05 for RED and 3.02 for KRED).
The learning time comparison between KRED and MKRED is shown in figure 2. Initially, the training time with few patterns is almost same for both techniques. When the number of patterns increases, the time taken by MKRED is only lesser, but it increases rapidly for the training using KRED method. Thus the proposed method results in major advantage of reduced training time for any number of patterns.
5. Conclusion
This paper has demonstrated the inherent weakness of current active queue management algorithms which use queue occupancy in their algorithms. In order to address this problem, a fundamentally different queue management algorithm called Modified Kohonen-based RED queue (MKRED) technique has been designed and evaluated. In the proposed approach, the adoption of weight and recognition is modified from Kohonen-based RED queue (KRED) method. From the experimental results, it is clearly observed that the proposed approach produces better prediction of buffer size for any number of patterns. The mean and standard deviation for the proposed approach are 18.23 ms and 2.95 ms respectively, which are very less compared to the conventional techniques. Moreover, the learning time taken by the proposed method is very less compared to the conventional methods.
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Maria Priscilla Jerald is a Sr.Lecturer in the Department of Computer Science at SNR Sons College, Coimbatore, India. She received her M.Sc. degree at Bharathiar University in 1999 and M.Phil degree at Bharathidasan University in 2004. She is currently pursuing her Ph.D at Mother Teresa University. Her area of interest is Computer Networks and her current research focus on Active Queue Management for Congestion Control.
