@INPROCEEDINGS{Angi2210:Howdah,
AUTHOR="Antonino Angi and Alessio Sacco and Flavio Esposito and Guido Marchetto and
Alexander Clemm",
TITLE="Howdah: Load Profiling via {In-Band} Flow Classification and {P4}",
BOOKTITLE="18th International Conference on Network and Service Management (CNSM)
(CNSM 2022)",
ADDRESS="Thessaloniki, Greece",
DAYS="30",
MONTH=oct,
YEAR=2022,
ABSTRACT="The challenges of managing datacenter traffic increase with the complexity
and variety of new Internet and Web applications. Efficient network
management systems are often required to thwart delays and minimize
failures. In this regard, it appears helpful to identify in advance the
different classes of flows that (co)exist in the network, characterizing
them into different types according to the different latency/bandwidth
requirements. In this paper, we propose Howdah, a traffic identification
and profiling mechanism that uses Machine Learning and a congestion-aware
forwarding strategy to offer adaptation to different traffic classes with
the support of programmable data-planes. With Howdah, sender and gateway
elements inject in-band traffic information obtained using supervised
learning. When a switch or a router receives a packet, it exploits such
host-based traffic classification to adapt to a desirable traffic profile,
for example, balancing the load. We compare our solutions against recent
traffic engineering solutions and show the efficacy of cooperation between
host traffic classification and P4-based switch forwarding policies,
reducing packet transmission time in datacenter scenarios."
}