On minimizing flow monitoring costs in large‐scale software‐defined network networks

Author:

Yahyaoui Haythem1,Zhani Mohamed Faten12,Bouachir Ouns3,Aloqaily Moayad4

Affiliation:

1. École de Technologie Supérieure Montreal, Quebec Canada

2. ISITCom Université de Sousse Sousse Tunisia

3. Zayed University Dubai UAE

4. Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE

Abstract

SummaryRecent years have witnessed the rise of novel network applications such as telesurgery, telepresence, and holoportation. As such applications have stringent performance requirements, timely and accurate traffic monitoring becomes of paramount importance to be able to react in a timely and efficient manner, and swiftly adjust the network configuration to achieve the sought‐after requirements. However, existing monitoring schemes are either incurring high cost (e.g., high bandwidth consumption) due to the large number of monitoring messages or inefficient when they incur high reporting delay (i.e., the time needed for a monitoring message to reach the controller) making the collected statistics obsolete. In this paper, we address this problem and propose monitoring mechanisms for software defined networks that minimize the monitoring cost while satisfying an upper bound on the reporting delay of the statistics. Our solutions allow to carefully select the switch that should report the statistics about each flow crossing the network taking into consideration the available bandwidth and the capacity of the switch (i.e., the maximum number of flows that it can monitor). In particular, we formulate the switch‐to‐flow selection problem as an integer linear program and propose two heuristic algorithms to cope with large‐scale instances of the problem. We consider the scenario where a single controller is collecting statistics and another where statistics are collected by multiple controllers. Simulation results show that the proposed algorithms provide near‐optimal solutions with minimal computation time and outperform existing monitoring strategies in terms of monitoring cost and reporting delay.

Publisher

Wiley

Subject

Computer Networks and Communications,Computer Science Applications

Reference17 articles.

1. Network management 2030: operations and control of network 2030 services;Clemm A;J Netw Syst Manag,2020

2. FlexNGIA: A flexible Internet architecture for the next‐generation tactile Internet;Zhani MF;J Netw Syst Manag,2020

3. YahyaouiH AidiS ZhaniMF.On using flow classification to optimize traffic routing in SDN networks. In: IEEE annual consumer communications & networking conference (CCNC);2020:1‐6.

4. Markets Markets.The expected growth in the network monitoring market:marketsandmarkets.https://www.marketsandmarkets.com/Market-Reports/network-monitoring-market-51888593.html Accessed on 2022‐04‐19;2017.

5. YuC LumezanuC ZhangY SinghV JiangG MadhyasthaHV.Flowsense: monitoring network utilization with zero measurement cost. In: International conference on passive and active network measurement Springer;2013:31‐41.

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Transforming Network Management: Intent-Based Flexible Control Empowered by Efficient Flow-Centric Visibility;Future Internet;2024-06-25

2. A Low-overhead Network Monitoring for SDN-Based Edge Computing;2023 IEEE Symposium on Computers and Communications (ISCC);2023-07-09

3. Sustainable fixed wireless access with blockchain secured software defined network;Pervasive and Mobile Computing;2023-05

4. Network Management devices in an SDN environment;2023 24th International Conference on Control Systems and Computer Science (CSCS);2023-05

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3