面向云数据中心的多业务差异化流量管理优化策略

doi:10.11959/j.issn.1000-436x.2019215

通信学报 ›› 2019, Vol. 40 ›› Issue (11): 45-56.doi: 10.11959/j.issn.1000-436x.2019215

面向云数据中心的多业务差异化流量管理优化策略

王耀民,王霞,董易,高莲,张松海,施心陵

云南大学信息学院，云南昆明 650504

修回日期:2019-08-26 出版日期:2019-11-25 发布日期:2019-12-06
作者简介:王耀民（1986- ），男，云南昆明人，云南大学博士生，主要研究方向为云计算、数据中心、软件定义网络、智能优化算法。|王霞（1985- ），女，云南保山人，云南大学博士生，主要研究方向为信号系统、智能优化算法。|董易（1985- ），男，云南保山人，博士，云南大学助理研究员，主要研究方向为信号系统、智能优化算法。|高莲（1981- ），女，云南玉溪人，博士，云南大学讲师，主要研究方向为信号处理与信息系统、生物信息工程。|张松海（1991- ），男，云南昆明人，云南大学硕士生，主要研究方向为智能优化算法。|施心陵（1956- ），男，云南昆明人，博士，云南大学教授、博士生导师，主要研究方向为信号处理与信息系统、智能优化算法。
基金资助:
国家自然科学基金资助项目(81771928);国家自然科学基金资助项目(61561049)

Multi-service differentiated traffic management optimization strategy in cloud data center

Yaomin WANG,Xia WANG,Yi DONG,Lian GAO,Songhai ZHANG,Xinling SHI

School of Information,Yunnan University,Kunming 650504,China

Revised:2019-08-26 Online:2019-11-25 Published:2019-12-06
Supported by:
The National Natural Science Foundation of China(81771928);The National Natural Science Foundation of China(61561049)

摘要/Abstract

摘要：

为应对运营商云数据中心多业务差异化流量管理要求，提升网络性能和业务体验，构建符合运营商云数据中心运营要求的多业务差异化流量管理模型（MSD），针对MSD模型改进斐波那契树优化（FTO）算法，提出运营商SDN云数据中心流量管理的MSD-FTO策略。实验结果表明，该策略具备良好的全局优化能力和多模自适应特性，通过算法全局局部交替迭代寻优得到多个符合条件的差异化流量管理方案，可解决运营商云数据中心多业务差异化流量管理问题，有效提升云数据中心的网络性能和业务体验。

关键词: 云数据中心, 多业务差异化, 流量管理, 斐波那契树优化算法, 多模自适应优化

Abstract:

In order to cope with the traffic management for multi-service differentiated in cloud data centers,improving network performance and service experience,the multi-service differentiated (MSD) traffic management model was designed that can suit operational requirements in cloud data center.Fibonacci tree optimization (FTO) algorithm was improved according to the MSD model.MSD-FTO traffic management strategy was proposed in SDN cloud data center.Simulation results show that the strategy takes advantage of FTO global optimization ability and multi-modal adaptive performance.Through the global local alternating optimization of the algorithm,differentiation traffic management schemes are obtained as needed,the problem of multi-services differentiated traffic management is solved in operator cloud data center that improve network performance and service experience in cloud data center effectively.

Key words: cloud data center, MSD, traffic management, FTO, multi-modal adaptive optimization

中图分类号:

TP393

王耀民,王霞,董易,高莲,张松海,施心陵. 面向云数据中心的多业务差异化流量管理优化策略[J]. 通信学报, 2019, 40(11): 45-56.

Yaomin WANG,Xia WANG,Yi DONG,Lian GAO,Songhai ZHANG,Xinling SHI. Multi-service differentiated traffic management optimization strategy in cloud data center[J]. Journal on Communications, 2019, 40(11): 45-56.

图/表 19

图1

图2

图3

图4

表1

符号定义"

符号	描述
c_ij	链路(i,j)性能开销
α	时延权重
β	分组丢失权重
d_ij	链路(i,j)时延
p_ij	链路(i,j)分组丢失
b_ij	链路(i,j)的可用带宽
$P_{\max}^{k}$	业务k分组丢失阈值
$D_{\max}^{k}$	业务k时延阈值

表1

图5

图6

图7

图8

图9

表2

多峰函数"

函数	函数表达式
f₁	$- e^{- 2 \ln (2) {(x - 0.0667)}^{2}} \sin^{6} (5 π x)$
f₂	${\sum_{i = 1}^{5} i \cos [(i + 1) x + i]}^{2}$
f₃	$[1 - {\| \frac{\sin [π (x_{1} - 2)] \sin [π (x_{2} - 2)]}{π^{2} (x_{1} - 2) (x_{2} - 2)} \|}^{5}] [2 + {(x_{1} - 7)}^{2} + {(x_{2} - 7)}^{2}]$
f₄	$\sum_{i = 1}^{n} \sin (x_{i})$

表2

表3

图10

图11

图12

表4

表5

图13

图14

参考文献 27

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算法	方案1	方案2	方案3	多模特性
GA	62.15	—	—	×
IGA	58.43	—	—	×
FTO	52.76	55.62	61.32	√

业务	链路带宽/(Mbit·s^-1)	网络时延/ms	分组丢失率	N	num
4K	(30,100)随机取值	50	<1％	300	3
IPTV	(10,20)随机取值	100	<2％	300	3
Web	(2,10)随机取值	120	<5％	300	3

面向云数据中心的多业务差异化流量管理优化策略

Multi-service differentiated traffic management optimization strategy in cloud data center

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图/表 19

参考文献 27

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函数	定义域	num	N
f₁	x∈[0,1]	5	20
f₂	x∈[-10,10]	21	20
f₃	x₁,x₂∈[0,15]	3	30
f₄	x₁,x₂∈[-10,10]	13	30

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