Multiple Layer Resource Optimization for Optical as a Service
draft-multiple-layer-resource-optimization-01

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Cross Stratum Optimization Research Group                        H. Yang
Internet-Draft                                                   K. Zhan
Intended status: Informational                                     A. Yu
Expires: November 6, 2019                                         Q. Yao
                                                                J. Zhang
                      Beijing University of Posts and Telecommunications
                                                             May 5, 2019

     Multiple Layer Resource Optimization for Optical as a Service
             draft-multiple-layer-resource-optimization-01

Abstract

   We have established a neural network model optimized by adaptive
   artificial fish swarm algorithm.  Then we propose a novel multi-path
   pre-reserved resource allocation strategy to increase resource
   utilization.  The results prove the effectiveness of our method.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
     1.1.  Conventions Used in This Document . . . . . . . . . . . .   3
   2.  PREDICTION STRATEGY . . . . . . . . . . . . . . . . . . . . .   3
     2.1.  Artificial neural network model . . . . . . . . . . . . .   3
     2.2.  Adaptive artificial fish swarm artificial neural networks
           (AAFS-ANN ) . . . . . . . . . . . . . . . . . . . . . . .   4
   3.  MULTI-PATH PRE-RESERVED RESOURCE ALLOCATION . . . . . . . . .   4
     3.1.  Reconfiguration time calculation  . . . . . . . . . . . .   6
     3.2.  Multi-path pre-reserved resource allocation(MP-RA)  . . .   6
   4.  Experimental evaluation and results analysis  . . . . . . . .   7
   5.  CONCLUSION  . . . . . . . . . . . . . . . . . . . . . . . . .   9
   6.  ACKNOWLEDGMENT  . . . . . . . . . . . . . . . . . . . . . . .   9
   7.  References  . . . . . . . . . . . . . . . . . . . . . . . . .   9
     7.1.  Normative References  . . . . . . . . . . . . . . . . . .   9
     7.2.  Informative References  . . . . . . . . . . . . . . . . .   9
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  10

1.  Introduction

   With the rapid growth of cloud computing, 5G services, and the
   periodicity of people's activities, traffic load has exhibited
   periodicity in both time and space domains, namely tidal traffic [1].
   The number of people using optical metropolitan networks is enormous
   and unevenly distributed.  In addiction, the separation of work areas
   and residential areas is an important cause of tidal traffic.
   Generally, tidal traffic will reduce the performance of networks
   during to following two reasons: firstly, the network traffic will be
   blocked due to the sharp increase in traffic in the high-traffic
   area; secondly, network nodes may be idle and waste resources in the
   low-traffic areas.  The static configuration resources will intensify
   both network and service congestion during traffic peak hours, as
   well as low resource utilization during low-traffic times and
   regions.  In the future, global mobile Internet traffic will increase
   by 10 times [2], urbanization is rapidly advancing, the scope and
   severity of space and time domains affected by tidal traffic are
   increasing as communication need and network technologies developing.
   Tidal traffic will further affect the optical access network and the
   optical core network, making it essential issue for network
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