Evolutionary learning methods have been applied to a variety of different problems. In this paper, a new algorithm for active queue management based on an evolutionary learning model is proposed. This novel algorithm generates packet marks for the purpose of improving robustness and responsiveness of congestion control in the Internet routers, while maintaining a reasonable degree of queueing performance such as utilization, delay, and packet drops due to buffer overflow. Simulation results demonstrate the effectiveness of the proposed algorithm and compare the performance of various algorithms.

Network virtualization is viewed as a promising approach to facilitate the sharing of physical infrastructure among different kinds of users and applications. In this letter, we propose a topological consistency-based virtual network embedding (TC-VNE) over elastic optical networks (EONs). Based on the concept of topological consistency, we propose a new node ranking approach, named Sum-N-Rank, which contributes to the reduction of optical path length between preferred substrate nodes. In the simulation results, we found our work contributes to improve spectral efficiency and balance link load simultaneously without deteriorating blocking probability.

This paper studies the problem of real-time routing in a multi-autonomous robot enhanced network at uncertain and vulnerable tactical edge. Recent network protocols, such as opportunistic mobile network routing protocols, engaged social network in communication network that can increase the interoperability by using social mobility and opportunistic carry and forward routing algorithms. However, in practical harsh environment such as a battlefield, the uncertainty of social mobility and complexity of vulnerable environment due to unpredictable physical and cyber-attacks from enemy, would seriously affect the effectiveness and practicality of these emerging network protocols. This paper presents a GT-SaRE-MANET (Game Theoretic Situation-aware Robot Enhanced Mobile Ad-hoc Network) routing protocol that adopt the online reinforcement learning technique to supervise the mobility of multi-robots as well as handle the uncertainty and potential physical and cyber attack at tactical edge. Firstly, a set of game theoretic mission oriented metrics has been introduced to describe the interrelation among network quality, multi-robot mobility as well as potential attacking activities. Then, a distributed multi-agent game theoretic reinforcement learning algorithm has been developed. It will not only optimize GT-SaRE-MANET routing protocol and the mobility of multi-robots online, but also effectively avoid the physical and/or cyber-attacks from enemy by using the game theoretic mission oriented metrics. The effectiveness of proposed design has been demonstrated through computer aided simulations and hardware experiments.

In this letter, we evaluate the performance of several adaptive and non-adaptive AQM schemes for congestion control in a dynamic network environment with variable bandwidth links. The AQM schemes examined are RED, BLUE, Adaptive RED, REM, AVQ and PI controller. We compare their queueing performance and show that none of them can derive stable queue length and low packet drop rate simultaneously in networks where both input traffic and available output bandwidth are time varying. Adaptive and efficient algorithms should be designed and applied in order to improve the adaptiveness and robustness of congestion control in dynamic networks such as Internet.