Enhanced IPFIX flow processing mechanism for overlay network monitoring

• Main Author: Khurram, Shahzada
• Format: Thesis
• Language: English; English; English
• Published: 2019
• Subjects: T58.5-58.64 Information technology; TK7885-7895 Computer engineering. Computer hardware
• Online Access: https://etd.uum.edu.my/8920/1/s95688_01.pdf; https://etd.uum.edu.my/8920/2/s95688_02.pdf; https://etd.uum.edu.my/8920/3/s95688_references.docx; https://etd.uum.edu.my/8920/

Cloud computing is an emerging technology. People are adopting cloud at a faster rate, due to this cloud network traffic is increasing at a pace which is challenging to manage. Monitoring tool is an essential aspect of cloud computing and becomes more apparent with the acquired of cloud services. Overlay network provides new path to converge network and run as an independent virtual network on top of physical network. Currently, cloud overlay network technologies in cloud infrastructure have visibility gaps, which mean cloud provider and consumers miss out the major performance issues for troubleshooting of overlay network traffic. Hence, to keep a close watch on network and catch potential problems, a network monitoring tool required, to track and report more in-depth for not only see the hidden traffic but also presents the related information of cloud overlay network technologies specifically suited to the modern cloud-scale data center. Therefore, this study proposes an enhanced IP Flow Information Export (IPFIX) mechanism for cloud overlay network monitoring by adopting flexible flow based technique. Furthermore, the solution provided in this research consist of diverse mechanisms: enhanced packet filtering mechanisms using property match filtering technique and hash-based filtering technique. Virtual Extensible Local Area Network (VXLAN) based flow classification mechanisms using 6-tuple flow pattern and adoptable flow patterns. IPFIX message template mechanisms, which is comprise set of fields for data records within the IPFIX flow processing system. The findings demonstrate that the proposed mechanism can capture multi-tenant overlay network traffic to identify, track, analyze and continuously monitor the performance of cloud overlay network services. The proposed mechanisms are resource efficient where the combination of VFMFM+6tuple+VXLAN Message consume 4.63% less CPU, while the combination of VHFM+AFCM+AFCM Message consume 11.45% less CPU than Standard IPFIX. The contributions of this study would help cloud network operators and end-users to quickly and proactively resolve any overlay network based on performance issues with end-to end visibility and actionable insights.