基于Kubernetes的多云网络成本优化模型

高明; 刘铭; 陈泱婷; 王伟明

doi:10.11959/j.issn.1000-0801.2023028

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基于Kubernetes的多云网络成本优化模型

研究与开发 | 更新时间：2024-06-05

- 基于Kubernetes的多云网络成本优化模型
- Cost optimization model for multi-cloud network based on Kubernetes
- 电信科学 2023年39卷第2期页码：71-82
- 作者机构：
- 作者简介：
  
  [ "高明（1979- ），男，博士，浙江工商大学信息与电子工程学院副教授、网络系主任，主要研究方向为新型网络体系架构和工业互联网" ]
  [ "刘铭（1997- ），男，浙江工商大学信息与电子工程学院硕士生，主要研究方向为新型网络体系架构和云原生网络" ]
  [ "陈泱婷（1998- ），女，浙江工商大学信息与电子工程学院硕士生，主要研究方向为软件定义网络" ]
  [ "王伟明（1964- ），男，博士，浙江工商大学信息与电子工程学院教授，主要研究方向为新一代网络体系结构和开放可编程网络" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61871468);浙江省基础公益研究计划项目;The Basic Public Welfare Research Program of Zhejiang Province(LGG20F010015);浙江省新型网络标准与应用技术重点实验室项目(2013E10012)
- DOI：10.11959/j.issn.1000-0801.2023028
  中图分类号： TP393
- 网络出版日期：2023-02，
  
  纸质出版日期：2023-02-20
- 稿件说明：
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高明, 刘铭, 陈泱婷, 等. 基于Kubernetes的多云网络成本优化模型[J]. 电信科学, 2023,39(2):71-82.

Ming GAO, Ming LIU, Yangting CHEN, et al. Cost optimization model for multi-cloud network based on Kubernetes[J]. Telecommunications science, 2023, 39(2): 71-82.
高明, 刘铭, 陈泱婷, 等. 基于Kubernetes的多云网络成本优化模型[J]. 电信科学, 2023,39(2):71-82. DOI： 10.11959/j.issn.1000-0801.2023028.

Ming GAO, Ming LIU, Yangting CHEN, et al. Cost optimization model for multi-cloud network based on Kubernetes[J]. Telecommunications science, 2023, 39(2): 71-82. DOI： 10.11959/j.issn.1000-0801.2023028.

摘要

以 Kubernetes 为代表的云原生编排系统在多云环境中被云租户广泛使用，随之而来的网络观测性问题愈发突出，跨云跨地区的网络流量成本尤为突出。在Kubernetes中引入扩展的伯克利数据包过滤器（extended Berkeley packet filter，eBPF）技术采集操作系统内核态的网络数据特征解决网络观测问题，随后将网络数据特征建模为二次分配问题（quadratic assignment problem，QAP），使用启发式搜索与随机搜索组合的方法在实时计算的场景下求得最佳近优解。此模型在网络资源成本优化中优于 Kubernetes 原生调度器中仅基于计算资源的调度策略，在可控范围内增加了调度链路的复杂度，有效降低了多云多地区部署环境中的网络资源成本。

Abstract

The cloud-native scheduling system

represented by Kubernetes

is widely used by cloud tenants in a multi-cloud environment.The problem of network observation becomes more and more serious

especially the cost of network traffic across cloud and region.In Kubernetes

the eBPF technology was introduced to collect the network data features of kernel state of operating system to solve the network observation problem

and then the network data features were modeled as QAP

a combination of heuristic and stochastic optimization was used to obtain the best near optimal solution in a real-time computing scenario.This model is superior to the Kubernetes native scheduler in the cost optimization of network resources

which is based on the scheduling strategy of computing resources only

and increases the complexity of scheduling links in a controllable range

effectively reduces the cost of network resources in a multi-cloud area deployment environment.

关键词

Keywords

references

中国信息通信研究院 . 云原生发展白皮书（2020 年） [R ] . 2020 .

China Academy for Information and Communications Technology . Cloud native development white paper (2020) [R ] . 2020 .

RIOS J , JHA S , SHWARTZ L . Localizing and explaining faults in micro services using distributed tracing [C ] // Proceedings of 2022 IEEE 15th International Conference on Cloud Computing (CLOUD) . Piscataway:IEEE Press , 2022 : 489 - 499 .

eBFP:extended berkeley packet filter [EB ] . 2022 .

BE A , MG B , ZZ A . Evaluating and reducing cloud waste and cost—a data-driven case study from Azure workloads [J ] . Sustainable Computing:Informatics and Systems , 2022 ,35:100708.

LI W , LI G J , YU X F . A fast traffic classification method based on SDN network [M ] . Electronics,Communications and Networks IV.U . S.A : CRC Press , 2015 : 223 - 229 .

WANG F , LIU B , ZHANG L J , et al . Dynamic routing and spectrum assignment based on multilayer virtual topology and ant colony optimization in elastic software-defined optical networks [J ] . Optical Engineering , 2017 , 56 ( 7 ): 076111 .

PARSAEI M R , MOHAMMADI R , JAVIDAN R . A new adaptive traffic engineering method for telesurgery using ACO algorithm over Software Defined Networks [J ] . LaRechercheEur opéenneEn Télémédecine , 2017 , 6 ( 3/4 ): 173 - 180 .

WANG J C , DE LAAT C , ZHAO Z M . QoS-aware virtual SDN network planning [C ] // Proceedings of 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM) . Piscataway:IEEE Press , 2017 : 644 - 647 .

CAO J Y , ZHANG Y , AN W , et al . VNF placement in hybrid NFV environment:modeling and genetic algorithms [C ] // Proceedings of 2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS) . Piscataway:IEEE Press , 2017 : 769 - 777 .

CARPIO F , DHAHRI S , JUKAN A . VNF placement with replication for Loac balancing in NFV networks [C ] // Proceedings of 2017 IEEE International Conference on Communications (ICC) . Piscataway:IEEE Press , 2017 : 1 - 6 .

RANKOTHGE W , MA J F , LE F , et al . Towards making network function virtualization a cloud computing service [C ] // Proceedings of 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM) . Piscataway:IEEE Press , 2015 : 89 - 97 .

YI B , WANG X , LI K , et al . A comprehensive survey of network function virtualization [J ] . Computer Networks , 2018 ( 133 ): 212 - 262 .

MIANO S , RISSO F , BERNAL M V , et al . A framework for eBPF-based network functions in an era of micro services [J ] . IEEE Transactions on Network and Service Management , 2021 , 18 ( 1 ): 133 - 151 .

MAYER A , LORETI P , BRACCIALE L , et al . Performance Monitoring with Hˆ2:hybrid Kernel/eBPF data plane for SRv6 based Hybrid SDN [J ] . Computer Networks , 2021 ( 185 ): 107705 .

DRORI I , KHARKAR A , SICKINGER W R , et al . Learning to solve combinatorial optimization problems on real-world graphs in linear time [C ] // Proceedings of 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA) . Piscataway:IEEE Press , 2021 : 19 - 24 .

VESSELINOVA N , STEINERT R , PEREZ-RAMIREZ D F , , et al . Learning combinatorial optimization on graphs:a survey with applications to networking [J ] . IEEE Access , 2020 ( 8 ): 120388 - 120416 .

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