Multipath Wireless Network Coding: A Scheduling Constraint in Game Perspective

This paper considers wireless networks in which various paths are obtainable involving each source and destination. It is allowing each source to tear traffic among all of its existing paths, and it may conquer the lowest achievable number of transmissions per unit time to sustain a prearranged traffic matrix. Traffic bound in contradictory instructions in excess of two wireless hops can utilize the “reverse carpooling” advantage of network coding in order to decrease the number of transmissions used. These call such coded hops “hyper-links.” With the overturn carpooling procedure, longer paths might be cheaper than shorter ones. However, convenient is an irregular situation among sources. The network coding advantage is realized only if there is traffic in both directions of a shared path. This project regard as the problem of routing amid network coding by egotistic agents (the sources) as a potential game and develop a method of state-space extension in which extra agents (the hyper-links) decouple sources’ choices from each other by declaring a hyper-link capacity, allowing sources to split their traffic selfishly in a distributed fashion, and then altering the hyper-link capacity based on user actions. Furthermore, each hyper-link has a scheduling constraint in stipulations of the maximum number of transmissions authorized per unit time. Finally these project show that our two-level control scheme is established and verify our investigative insights by simulation.

Green distributed algorithm for energy saving in IP wired networks using sleep scheduling

Energy saving has become a critical issue and a great challenge in the past few decades, and a great effort as well is being made to reduce consumed energy. The Internet forms a major source for energy consumption. Therefore, in this work we propose an algorithm for energy saving in distributed backbone networks, the reduced energy consumption (RedCon) algorithm. In this paper, we introduce a new version for saving energy on the Internet by switching off underutilized links and switching on idle links when the network is overloaded in a distributed manner over the network nodes based on LSA messages and without any knowledge of the traffic matrix. Our algorithm is more accurate and outperforms other algorithms with its time checks and advanced learning algorithm.

Design and Implementation of a Robust Convolutional Neural Network-Based Traffic Matrix Estimator for Cloud Networks

Recent research literature shows promising results by convolutional neural network- (CNN-) based approaches for estimation of traffic matrix of cloud networks using different architectures. Although conventionally, convolutional neural network-based approaches yield superior estimation; however, these rely on assumptions of availability of a large training dataset which is completely accurate and nonsparse. In real world, both these assumptions are problematic as training data size may be limited, and it is also prone to missing (or incomplete) measurements as well as may have measurement errors. Similarly, the 2-D training datasets derived from network topology based may be sparse. We investigate these challenges and develop a novel architecture which can cater for these challenges and deliver superior performance. Our approach shows promising results for traffic matrix estimation using convolutional neural network-based techniques in the presence of limited training data and outlier measurements.

Estimation of Traffic Matrix from Links Load using Genetic Algorithm

Traffic Matrix (TM) is a representation of all traffic flows in a network. It is helpful for traffic engineering and network management. It contains the traffic measurement for all parts of a network and thus for larger network it is difficult to measure precisely. Link load are easily obtainable but they fail to provide a complete TM representation. Also link load and TM relationship forms an under-determined system with infinite set of solutions. One of the well known traffic models Gravity model provides a rough estimation of the TM. We have proposed a Genetic algorithm (GA) based optimization method to further the solutions of the Gravity model. The Gravity model is applied as an initial solution and then GA model is applied taking the link load-TM relationship as a objective function. Results shows improvement over Gravity model.