Packet routing analyses using probabilistic data structures in Multi-Tenant Networks based on programmable devices
Fecha
2019-09-04Autor
Teles Martins, Regis Francisco
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Given the network traffic growth, due to applications that heavily use computational cloud
infrastructure, the need for improving the monitoring traffic techniques has increased. For
traffic engineering, it is essential to gain total visibility into the traffic flowing across the
network. The most used methods for traffic monitoring in the industry are those based
on dedicated monitoring protocols as SNMP (Simple Network Management Protocol),
NetFlow, SFlow, among others.
With processing capacity evolution of forwarding devices, new techniques have been
proposed. The use of sketches has become widely popular for traffic monitoring tasks.
Sketches are compact data structures capable of summarizing and store information
about the state of packets. Using sketches, it is possible to monitor a network traffic,
understanding the path travelled by each packet and which devices were responsible for
the packet forwarding.
Analyzing traffic over the network is a challenge that changes the traditional monitoring
approach. The current performance indicator metrics provided by network devices are not
enough to analyze and create insights for the network traffic as a whole. We need a way
to produce key performance indicators that can be correlated across different network
devices on the same network. This new approach opens opportunities for researching
and developing novel techniques to obtain a holistic network traffic visibility, to support
decisions in traffic engineering, to detect traffic anomalies and other applications.
Using a single sketch named BitMatrix, proposed in this work, it is possible to monitor
network traffic, understand the path travelled per packet and which devices forwarded this
packet along its path. In this context, this probabilistic structure was adopted to identify
the path used to forward a packet in a multi-tenant network in two different scenarios: a)
in an emulated network, using P4 routers and, b) in a simulated network, processing real
traffic traces, using a Python framework. As a result, overloaded routers, links and paths
and heavy user tenants were identified.
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