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The Cognitive Packet Network (CPN) bases its routing decisions and flow control on the Random Neural Network (RNN) Reinforcement Learning algorithm; this paper proposes the addition of a Deep Learning (DL) Cluster management structure to the CPN for Quality of Service metrics (Delay Loss and Bandwidth), Cyber Security keys (User, Packet and Node) and Management decisions (QoS, Cyber and CEO). The RNN already models how neurons transmit information using positive and negative impulsive signals whereas the proposed additional Deep Learning structure emulates the way the brain learns and takes decisions; this paper presents a brain model as the combination of both learning algorithms, RNN and DL. The proposed model has been simulated under different network sizes and scenarios and it has been validated against the CPN itself without DL clusters. The simulation results are promising; the presented CPN with DL clusters as a mechanism to transmit, learn and make packet routing decisions is a step closer to emulate the way the brain transmits information, learns the environment and takes decisions. [ABSTRACT FROM AUTHOR]

This paper examines the use of a gradient-based algorithm for Quality of Service (QoS) and power minimisation in wired networks to result on reduced energy consumption. Two distinct schemes, conventional shortest-path routing and an autonomic algorithm energy aware routing algorithm (EARP) are investigated as the starting point for the gradient algorithm. Comparisons are conducted using the same network test-bed and identical network traffic under conditions where routers and link drivers are always kept on so as to meet the needs for network reliability in the presence of possible failures and unexpected overload. Since splitting traffic flows can increase jitter and the arrival of packets which are out of sequence, we also do not allow the same packet flow to be conveyed over multiple paths. In the experiments that we have conducted we observe that power consumed with the gradient-optimiser that is proposed in this paper is a few percent to 10% smaller than that consumed using shortest-path routing or EARP. Although the savings are small, they can be very significant for the Internet backbone as a whole over long periods of time, and further power savings may be obtained by judiciously putting to sleep equipment when it is under-utilised. [ABSTRACT FROM AUTHOR]

Although there is great interest in reducing energy consumption for all areas of human activity, many of the proposed approaches such as the smart home or the smart grid are actually prone to an increase in the use of Information and Communication Technologies (ICT) which itself is a big consumer of energy. It is remarkable that ICT's carbon imprint is of the order of 2% of the world total, comparable to the carbon imprint of air travel. Thus, it is imperative to address energy savings in ICT and, in particular, in data centres and networks. This paper follows up on our previous work that seeks novel ways to reduce the energy consumption in packet networks which constitute the backbone of the Internet and of the information society as a whole. Here we discuss the use of routing control as a means to reduce energy consumption while remaining aware of QoS considerations, and propose a method that uses a queueing theoretic analysis and optimization technique to distribute traffic so as to reduce a cost function that comprises both energy and QoS. A method using G-networks is developed to incorporate both the effect of user traffic and the overhead in QoS and energy consumption introduced by the control traffic that will be needed to carry out the re-routing decisions. For an N-node network, we show that this approach results in an algorithm which has O(N3) time complexity. Because this approach may be too costly in computational overhead and delays, we also propose another approach that uses load balancing and which would be much simpler to implement. [ABSTRACT FROM AUTHOR]

Sensor networks (SNs) consist of spatially distributed sensors which monitor an environment, and which are connected to some sinks or backbone system to which the sensor data is being forwarded. In many cases, the sensor nodes themselves can serve as intermediate nodes for data coming from other nodes, on the way to the sinks. Much of the traffic carried by SNs will originate from routine measurements or observations by sensors that monitor a particular situation, such as the temperature and humidity in a room or the infrared observation of the perimeter of a house, so that the volume of routine traffic resulting from such observations may be quite high. When important and unusual events occur, such as a sudden fire breaking out or the arrival of an intruder, it will be necessary to convey this new information very urgently through the network to a designated set of sink nodes where this information can be processed and dealt with. This paper addresses the important challenge by avoiding the routine background traffic from creating delays or bottlenecks that impede the rapid delivery of high priority traffic resulting from the unusual events. Specifically we propose a novel technique, the ‘Randomized Re-Routing Algorithm (RRR)’, which detects the presence of novel events in a distributed manner, and dynamically disperses the background traffic towards secondary paths in the network, while creating a ‘fast track path’ which provides better delay and better quality of service (QoS) for the high priority traffic which is carrying the new information. When the surge of new information has subsided, this is again detected by the nodes and the nodes progressively revert to best QoS or shortest-path routing for all the ongoing traffic. The proposed technique is evaluated using a mathematical model as well as simulations, and is also compared with a standard node by a node priority scheduling technique. [ABSTRACT FROM PUBLISHER]

Packet routing in networks requires knowledge about available paths, which can be either acquired dynamically while the traffic is being forwarded, or statically (in advance) based on prior information of a network's topology. This paper describes an experimental investigation of path discovery using genetic algorithms (GAs). We start with the quality-of-service (QoS)-driven routing protocol called ‘cognitive packet network’ (CPN), which uses smart packets (SPs) to dynamically select routes in a distributed autonomic manner based on a user's QoS requirements. We extend it by introducing a GA at the source routers, which modifies and filters the paths discovered by the CPN. The GA can combine the paths that were previously discovered to create new untested but valid source-to-destination paths, which are then selected on the basis of their ‘fitness.’ We present an implementation of this approach, where the GA runs in background mode so as not to overload the ingress routers. Measurements conducted on a network test bed indicate that when the background-traffic load of the network is light to medium, the GA can result in improved QoS. When the background-traffic load is high, it appears that the use of the GA may be detrimental to the QoS experienced by users as compared to CPN routing because the GA uses less timely state information in its decision making. [ABSTRACT FROM AUTHOR]

Size-based routing policies are known to perform well when the variance of the distribution of the job size is very high. We consider two size-based policies in this paper: Task Assignment with Guessing Size (TAGS) and Size Interval Task Assignment (SITA). The latter assumes that the size of jobs is known, whereas the former does not. Recently, it has been shown by our previous work that when the ratio of the largest to shortest job tends to infinity and the system load is fixed and low, the average waiting time of SITA is, at most, two times less than that of TAGS. In this article, we first analyze the ratio between the mean waiting time of TAGS and the mean waiting time of SITA in a non-asymptotic regime, and we show that for two servers, and when the job size distribution is Bounded Pareto with parameter α = 1 , this ratio is unbounded from above. We then consider a system with an arbitrary number of servers and we compare the mean waiting time of TAGS with that of Size Interval Task Assignment with Equal load (SITA-E), which is a SITA policy where the load of all the servers are equal. We show that in the light traffic regime, the performance ratio under consideration is unbounded from above when (i) the job size distribution is Bounded Pareto with parameter α = 1 and an arbitrary number of servers as well as (ii) for Bounded Pareto distributed job sizes with α ∈ (0 , 2) \ { 1 } and the number of servers tends to infinity. Finally, we use the result of our previous work to show how to design decentralized systems with quality of service constraints. [ABSTRACT FROM AUTHOR]