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THE RANDOM NEURAL NETWORK FOR COGNITIVE TRAFFIC ROUTING AND TASK ALLOCATION IN NETWORKS AND THE CLOUD

Published online by Cambridge University Press:  22 May 2017

Lan Wang
Lan Wang*
Affiliation:
Department of Electrical and Electronic Engineering, Imperial College, London SW7 2BT, UK E-mail: [email protected]
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Abstract

G-Network queueing network models, and in particular the random neural network (RNN), are useful tools for decision making in complex systems, due to their ability to learn from measurements in real time, and in turn provide real-time decisions regarding resource and task allocation. In particular, the RNN has led to the design of the cognitive packet network (CPN) decision tool for the routing of packets in the Internet, and for task allocation in the Cloud. Thus in this paper, we present recent research on how to dynamically create the means for quality of service (QoS) to end users of the Internet and in the Cloud. The approach is based on adapting the decisions so as to benefit users as the conditions in the Internet and in Cloud servers vary due to changing traffic and workload. We present an overview of the algorithms that were designed based on the RNN, and also detail the experimental results that were obtained in three areas: (i) traffic routing for real-time applications, which have strict QoS constraints; (ii) routing approaches, which operate at the overlay level without affecting the Internet infrastructure; and (iii) the routing of tasks across servers in the Cloud through the Internet.

Keywords

cloud computingcognitive packet networkG-networkoverlay networkrandom neural networkreal-time applicationreinforcement learningroutingtask allocation
Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 31 , Special Issue 4: G-Networks and their Applications , October 2017 , pp. 540 - 560
Copyright
Copyright © Cambridge University Press 2017 

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