Multiple Layer Resource Optimization for Optical as a Service
draft-multiple-layer-resource-optimization-04
Cross Stratum Optimization Research Group H. Yang
Internet-Draft K. Zhan
Intended status: Informational A. Yu
Expires: April 29, 2021 Q. Yao
J. Zhang
Beijing University of Posts and Telecommunications
October 26, 2020
Multiple Layer Resource Optimization for Optical as a Service
draft-multiple-layer-resource-optimization-04
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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