Comparative Study on New AQM Mechanisms for Congestion Control

As usage of network goes increasing day by day, managing network traffic becomes a very difficult task. It is important to avoid high packet loss rates in the Internet. Congestion is the one of the major issue in the present networks. Congestion Control is one of the solutions adopted to solve the congestion issue and to control it. Numbers of queue management algorithms are proposed for congestion control and to reduce high packet loss rates. Active Queue Management (AQM) is one such mechanism which provides better control over congestion. In this paper a study is made on recent load based AQM techniques that are proposed and its merits and shortfall is presented

Comparative Study Of Congestion Control Techniques In High Speed Networks

Congestion in network occurs due to exceed in aggregate demand as compared to the accessible capacity of the resources. Network congestion will increase as network speed increases and new effective congestion control methods are needed, especially to handle bursty traffic of todays very high speed networks. Since late 90s numerous schemes i.e. [1]...[10] etc. have been proposed. This paper concentrates on comparative study of the different congestion control schemes based on some key performance metrics. An effort has been made to judge the performance of Maximum Entropy (ME) based solution for a steady state GE/GE/1/N censored queues with partial buffer sharing scheme against these key performance metrics.Comment: 10 pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS November 2009, ISSN 1947 5500, http://sites.google.com/site/ijcsis

A Distributed Control Law for Load Balancing in Content Delivery Networks

Large Internet-scale distributed systems deploy hundreds of thousands of servers in thousands of data centers around the world. Internet-scale distributed system to have emerged in the past decade is the content delivery network (CDN, for short) that delivers web content, web and IP-based applications, downloads, and streaming media to end-users (i.e., clients) around the world. This paper focuses on the main research areas in the field of CDN, pointing out the motivations, and analyzing the existing strategies for replica placement and management, server measurement, best fit replica selection and request redirection. In this paper, I face the challenging issue of defining and implementing an effective law for load balancing in Content Delivery Networks. A formal study of a CDN system, carried out through the exploitation of a fluid flow model characterization of the network of servers. This result is then leveraged in order to devise a novel distributed and time-continuous algorithm for load balancing

Reduction of queue oscillation in the next generation Internet routers

The Internet routers employing the random early detection (RED) algorithm for congestion control suffer from the problem of chaotic queue oscillation. It is well known that the slowly varying nature of the average queue size computed using an exponentially weighted moving average (EWMA) used in the RED scheme causes this chaotic behavior. This paper presents a new mathematical function to model the weighting parameter used in the EWMA. The proposed weighting function incorporates the knowledge of the dynamic changes in the congestion characteristics, traffic characteristics and queue normalization. Using this pragmatic information eliminates the slowly varying nature of the average queue size. It is evident from our simulations that the proposed approach not only reduces the chaotic queue oscillation significantly but also provides predictable low delay and low delay jitter with high throughput gain and reduced packet loss rate even under heavy load of traffic conditions

GA-PSO-Optimized Neural-Based Control Scheme for Adaptive Congestion Control to Improve Performance in Multimedia Applications

Active queue control aims to improve the overall communication network throughput while providing lower delay and small packet loss rate. The basic idea is to actively trigger packet dropping (or marking provided by explicit congestion notification (ECN)) before buffer overflow. In this paper, two artificial neural networks (ANN)-based control schemes are proposed for adaptive queue control in TCP communication networks. The structure of these controllers is optimized using genetic algorithm (GA) and the output weights of ANNs are optimized using particle swarm optimization (PSO) algorithm. The controllers are radial bias function (RBF)-based, but to improve the robustness of RBF controller, an error-integral term is added to RBF equation in the second scheme. Experimental results show that GA- PSO-optimized improved RBF (I-RBF) model controls network congestion effectively in terms of link utilization with a low packet loss rate and outperform Drop Tail, proportional-integral (PI), random exponential marking (REM), and adaptive random early detection (ARED) controllers.Comment: arXiv admin note: text overlap with arXiv:1711.0635

Multi-Agent Adaptive Mechanism Leading to Optimal Real-Time Load Sharing

We propose a new real-time load sharing policy (LSP), which optimally dispatches the incoming workload according to the current availability of the operators. Optimality means here that the global service permanently requires the engagement of a minimum number of operators while still respecting due dates. To cope with inherent randomness due to operator failures as well as non-stationary fluctuating incoming workload, any optimal LSP rule will necessarily rely on real-time updating mechanisms. Accordingly, a permanent monitoring of the traffic workload, of the queue contents and of other relevant dynamic state variables is often realized by a central workload dispatcher. In this contribution, we abandon such a "classical" approach and we propose a fully decentralized algorithm which fulfils the optimal load sharing process. The underlying decentralized decisions rely on a "smart tasks" paradigm in which each incoming task is endowed with an autonomous routing decision mechanism. Incoming jobs hence possess, in this paper, the status of autonomous agents endowed with "local intelligence". Stigmergic interactions between these agents cause the optimal LSP to emerge. We emphasize that beside a manifest strict relevance for applications, our class of models is analytically tractable, a rather uncommon feature when dealing with multi-agent dynamics and complex adaptive logistics systems

An adaptive active queue management algorithm in Internet

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An Optimization Problem of Internet Routing

As the Internet usage grows, existing network infrastructure must deal with increasing demand. One way to deal with this is to increase network capacity, and another, is to set network parameters appropriately. In this dissertation we contribute to the latter approach by determining the unique network paths data must flow over from its origin to its destination, while accounting for an Active Queue Management method, Random Early Detection (RED). We formulate a mixed integer non-linear program to determine the data paths, referred to as a routing policy. We prove that determining an optimal routing policy that accounts for RED is NP-Hard. Furthermore, in order for the generated routing policies to be real-world implementable, also known as realizable, we must determine weights for all arcs in the network such that solving the all pairs shortest path problem using these weights reproduces the routing policies. We show that determining if our generated routing policies are realizable is NP-Hard. Fortunately, using traffic data from three real-world networks, we are able to find realizable routing policies for these networks that account for RED, using an off-the-shelf solver, and policies found perform better than those used in those networks at the time the data was collected