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In this paper we propose a novel approach to deliver better delay-jitter performance in dynamic networks. Dynamic networks experience rapid and unpredictable fluctuations and hence, a certain amount of uncertainty about the delay-performance of various network elements is unavoidable. This uncertainty makes it difficult for network operators to guarantee a certain quality of service (in terms of delay and jitter) to users. The uncertainty about the state of the network is often overlooked to simplify problem formulation, but we capture it by modeling the delay on various links as general and potentially correlated random processes. Within this framework, a user will request a certain delay-jitter performance guarantee from the network. After verifying the feasibility of the request, the network will respond to the user by specifying a set of routes as well as the proportion of traffic which should be sent through each one to achieve the desired QoS. We propose to use mean-variance analysis as the basis for traffic distribution and route selection, and show that this technique can significantly reduce the end-to-end jitter because it accounts for the correlated nature of delay across different paths. The resulting traffic distribution is often non-uniform and the fractional flow on each path is the solution to a simple convex optimization problem. We conclude the paper by commenting on the potential application of this method to general transportation networks. 

Future networks have to accommodate an increase of 3-4 orders of magnitude in data rates with heterogeneous session sizes and potentially stricter time deadline requirements. The dynamic nature of scheduling of large transactions and the need for rapid actions by the Network Management and Control (NMC) system, require timely collection of network state information. Rough estimates of the size of detailed network states suggest a huge burden for network transport and computation resources. Thus, judicious sampling of network states is necessary for a cost-effective network management system. In this paper, we consider an NMC system where sensing and routing decisions are made with cognitive understanding of network states and short-term behavior of exogenous offered traffic. We study a small but realistic example of adaptive monitoring based on significant sampling techniques. This technique balances the need for updated state information against the updating cost and provides an algorithm that yields near optimum performance with significantly reduced burden of sampling, transport and computation. We show that our adaptive monitoring system can reduce the NMC overhead by a factor of 100 in one example. The spirit of cognitive NMC is to collect network states ONLY when they can improve the network performance. 

Future networks have to accommodate an increase of 3-4 orders of magnitude in data rates with very heterogeneous session sizes and sometimes with strict time deadline requirements. The dynamic nature of scheduling of large transactions and the need for rapid actions by the Network Management and Control (NMC) system, require timely collection of network state information. Rough estimates of the size of detailed network states suggest large volumes of data with refresh rates commensurate with the coherence time of the states (can be as fast as 100 ms), resulting in huge burden and cost for the network transport (300 Gbps/link) and computation resources. Thus, judicious sampling of network states is necessary for a cost-effective network management system. In this paper, we consider a construct of an NMC system where sensing and routing decisions are made with cognitive understanding of the network states and short-term behavior of exogenous offered traffic. We have studied a small but realistic example of adaptive monitoring based on significant sampling techniques. This technique balances the need for accurate and updated state information against the updating cost and provides an algorithm that yields near optimum performance with significantly reduced burden of sampling, transport and computation. We show that our adaptive monitoring system can reduce the NMC overhead by a factor of 100 in one example. The essential spirit of the cognitive NMC is that it collects network states ONLY when they matter to the network performance.