Abstract
Heavy-Hitters (HHs) are large-volume flows that consume considerably more network resources than other flows combined. In SDN-based DCNs (SDDCNs), HHs cause non-trivial delays for small-volume flows known as non-HHs that are delay-sensitive. Uncontrolled forwarding of HHs leads to network congestion and overall network performance degradation. A pivotal task for controlling HHs is their identification. The existing methods to identify HHs are threshold-based. However, such methods lack a smart system that efficiently identifies HH according to the network behaviour. In this paper, we introduce a novel approach to overcome this lack and investigate the feasibility of using Knowledge-Defined Networking (KDN) in HH identification. KDN by using Machine Learning (ML), allows integrating behavioural models to detect patterns, like HHs, in SDN traffic. Our KDN-based approach includes mainly three modules: HH Data Acquisition Module (HH-DAM), Data ANalyser Module (HH-DANM), and APplication Module (HH-APM). In HH-DAM, we present the flowRecorder tool for organizing packets into flows records. In HH-DANM, we perform a cluster-based analysis to determine an optimal threshold for separating HHs and non-HHs. Finally, in HH-APM, we propose the use of MiceDCER for routing non-HHs efficiently. The per-module evaluation results corroborate the usefulness and feasibility of our approach for identifying HHs.
Highlights
Today’s data centres networks (DCNs) have become an efficient and promising infrastructure to support a wide range of technologies, network services and applications such as multimedia content delivery, search engines, e-mail, map-reduce computation, and virtual machine migration [1]
We propose a novel HH flow identification approach aiming at overcoming the lack above-mentioned and investigating the feasibility of using the Knowledge-Defined Networking (KDN) concept in the HH identification
In HH-APplication Module (APM), we propose the use of MiceDCER to efficiently route non-HHs
Summary
Today’s data centres networks (DCNs) have become an efficient and promising infrastructure to support a wide range of technologies, network services and applications such as multimedia content delivery, search engines, e-mail, map-reduce computation, and virtual machine migration [1]. It is noteworthy that all approaches afore-cited perform a threshold-based HH identification. They lack a smart system that efficiently identifies HH according to the network behaviour; it means they are unaware of traffic in the network. In HH-DAM, we present the flowRecorder tool [25] that is used for generating and organizing packets into flows records Using this tool, we generated a dataset from publicly accessible university DCN traffic traces. In HH-APM, we propose the use of MiceDCER to efficiently route non-HHs. MiceDCER results show that it can reduce the number of routing rules by installing wildcard rules based on the information carried by the Address Resolution Protocol (ARP).
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