< draft-multiple-layer-resource-optimization-00.txt   draft-multiple-layer-resource-optimization-01.txt >
Cross Stratum Optimization Research Group H. Yang Cross Stratum Optimization Research Group H. Yang
Internet-Draft K. Zhan Internet-Draft K. Zhan
Intended status: Informational A. Yu Intended status: Informational A. Yu
Expires: May 9, 2019 Q. Yao Expires: November 6, 2019 Q. Yao
J. Zhang J. Zhang
Beijing University of Posts and Telecommunications Beijing University of Posts and Telecommunications
November 5, 2018 May 5, 2019
Multiple Layer Resource Optimization for Optical as a Service Multiple Layer Resource Optimization for Optical as a Service
draft-multiple-layer-resource-optimization-00 draft-multiple-layer-resource-optimization-01
Abstract Abstract
Human activities are highly predictable, so are the network traffic. We have established a neural network model optimized by adaptive
It shows a tidal phenomenon in traffic. Current resource allocation artificial fish swarm algorithm. Then we propose a novel multi-path
methods cannot adapt to tidal traffic, resulting in low resource pre-reserved resource allocation strategy to increase resource
utilization. To predicted tidal traffic, we have established a utilization. The results prove the effectiveness of our method.
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.
Status of This Memo Status of This Memo
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Table of Contents Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1. Conventions Used in This Document . . . . . . . . . . . . 3 1.1. Conventions Used in This Document . . . . . . . . . . . . 3
2. PREDICTION STRATEGY . . . . . . . . . . . . . . . . . . . . . 3 2. PREDICTION STRATEGY . . . . . . . . . . . . . . . . . . . . . 3
2.1. Artificial neural network model . . . . . . . . . . . . . 3 2.1. Artificial neural network model . . . . . . . . . . . . . 3
2.2. Adaptive artificial fish swarm artificial neural networks 2.2. Adaptive artificial fish swarm artificial neural networks
(AAFS-ANN ) . . . . . . . . . . . . . . . . . . . . . . . 4 (AAFS-ANN ) . . . . . . . . . . . . . . . . . . . . . . . 4
3. MULTI-PATH PRE-RESERVED RESOURCE ALLOCATION . . . . . . . . . 5 3. MULTI-PATH PRE-RESERVED RESOURCE ALLOCATION . . . . . . . . . 4
3.1. Reconfiguration time calculation . . . . . . . . . . . . 6 3.1. Reconfiguration time calculation . . . . . . . . . . . . 6
3.2. Multi-path pre-reserved resource allocation(MP-RA) . . . 6 3.2. Multi-path pre-reserved resource allocation(MP-RA) . . . 6
4. Experimental evaluation and results analysis . . . . . . . . 7 4. Experimental evaluation and results analysis . . . . . . . . 7
5. CONCLUSION . . . . . . . . . . . . . . . . . . . . . . . . . 9 5. CONCLUSION . . . . . . . . . . . . . . . . . . . . . . . . . 9
6. ACKNOWLEDGMENT . . . . . . . . . . . . . . . . . . . . . . . 9 6. ACKNOWLEDGMENT . . . . . . . . . . . . . . . . . . . . . . . 9
7. References . . . . . . . . . . . . . . . . . . . . . . . . . 9 7. References . . . . . . . . . . . . . . . . . . . . . . . . . 9
7.1. Normative References . . . . . . . . . . . . . . . . . . 9 7.1. Normative References . . . . . . . . . . . . . . . . . . 9
7.2. Informative References . . . . . . . . . . . . . . . . . 9 7.2. Informative References . . . . . . . . . . . . . . . . . 9
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 10 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 10
skipping to change at page 5, line 27 skipping to change at page 5, line 21
| \ \ | | \ \ |
| \ \ | | \ \ |
| \ \ | | \ \ |
| --- --- | | --- --- |
| | C |---------------| D | | | | C |---------------| D | |
| --- --- | | --- --- |
------------------------------------- -------------------------------------
Fig.1(a) Sample network Fig.1(a) Sample network
^ |------------------------------------
| T4 | | | | | | | | | |
|------------------------------------ |------------------------------------
T4 | | | | | | | | | | T3 | | * | * | | | | | | |
|------------------------------------ |------------------------------------
T3 | | $ | $ | | | | | | | T2 | | * | * | | | | | | |
|------------------------------------ |------------------------------------
T2 | | $ | $ | | | | | | | T1 | | | | | | | | | |
|------------------------------------ |------------------------------------
T1 | | | | | | | | | | T0 | | | | | | | | | |
|------------------------------------ -------------------------------------
T0 | | | | | | | | | | S0 S1 S2 S3 S4
---------------------------------------->
S0 S1 S2 S3 S4
Fig.1(b) Requested resources Fig.1(b) Requested resources
^ |------------------------------------
| T4 | | | | | | | | | |
|------------------------------------ |------------------------------------
T4 | | | | | | | | | | T3 | # | * | # | # | # | | | | |
|------------------------------------ |------------------------------------
T3 | # | $ | # | # | # | | | | | T2 | # | * | # | # | # | | | | |
|------------------------------------ |------------------------------------
T2 | # | $ | # | # | # | | | | | T1 | # | | | # | # | | | | |
|------------------------------------ |------------------------------------
T1 | # | | | # | # | | | | | T0 | # | | | # | # | | | | |
|------------------------------------ ------------------------------------
T0 | # | | | # | # | | | | | S0 S1 S2 S3 S4
---------------------------------------->
S0 S1 S2 S3 S4
Fig.1(c) Requested resources Fig.1(c) Requested resources
^ |------------------------------------
| T4 | | | | | | | | | |
|------------------------------------ |------------------------------------
T4 | | | | | | | | | | T3 | # | # | * | | # | # | | | |
|------------------------------------ |------------------------------------
T3 | # | # | $ | | # | # | | | | T2 | # | # | * | # | # | # | | | |
|------------------------------------ |------------------------------------
T2 | # | # | $ | # | # | # | | | | T1 | # | | | # | | | | | |
|------------------------------------ |------------------------------------
T1 | # | | | # | | | | | | T0 | # | | | | | | | | |
|------------------------------------ ------------------------------------
T0 | # | | | | | | | | | S0 S1 S2 S3 S4
---------------------------------------->
S0 S1 S2 S3 S4
Fig.1(d) Requested resources Fig.1(d) Requested resources
3.1. Reconfiguration time calculation 3.1. Reconfiguration time calculation
Frequent reconfiguration can result in service interruption and Frequent reconfiguration can result in service interruption and
unstable of distributed routing algorithm, so we need to predict the unstable of distributed routing algorithm, so we need to predict the
next 24-hour traffic demand D^24 for the next configuration time next 24-hour traffic demand D^24 for the next configuration time
point calculation. Algorithm 1 is the calculation process of the point calculation. Algorithm 1 is the calculation process of the
reconfiguration time point. reconfiguration time point.
skipping to change at page 11, line 20 skipping to change at page 11, line 20
Email: yqy89716@bupt.edu.cn Email: yqy89716@bupt.edu.cn
URI: http://www.bupt.edu.cn/ URI: http://www.bupt.edu.cn/
Jie Zhang Jie Zhang
Beijing University of Posts and Telecommunications Beijing University of Posts and Telecommunications
No.10,Xitucheng Road,Haidian District No.10,Xitucheng Road,Haidian District
Beijing 100876 Beijing 100876
P.R.China P.R.China
Phone: +8613911060930 Phone: +8613911060930
Email: lgr24@bupt.edu.cn Email: lgr@bupt.edu.cn
URI: http://www.bupt.edu.cn/ URI: http://www.bupt.edu.cn/
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