Load Profiling via In-Band Flow Classification and P4 With Howdah

Data center traffic management challenges increase with the complexity and variety of new Internet and Web applications. Efficient network management systems are often needed to thwart delays and minimize failures. In this regard, it seems helpful to identify in advance the different classes of flows that (co)exist in the network, characterizing them into different types based on different latency/bandwidth requirements. In this paper, we propose Howdah, a traffic identification and profiling mechanism that uses Machine Learning and a load-aware forwarding strategy to offer adaptation to different classes of traffic with the support of programmable data planes. With Howdah, the sender and gateway elements inject in-band traffic information obtained by a supervised learning algorithm. When a switch or router receives a packet, it exploits this host-based traffic classification to adapt to a desirable traffic profile, for example, to balance the traffic load. We compare our solution against recent traffic engineering proposals and demonstrate the effectiveness of the cooperation between host traffic classification and P4-based switch forwarding policies, reducing packet transmission time in data center scenarios.

I. Introduction

In the last two decades, data centers have changed their topology in response to the increasing demands of networked applications that continue to require more data at a faster speed while requiring lower latency. Because of these requirements, new data center architectures have been proposed, focusing on ingress and egress traffic optimizations but also on better orchestration of data center internal traffic. Data center topologies have also evolved to represent multi-rooted leaf-spine or, more often, fat-trees. Such topologies have in common the presence of multiple source-destination paths to handle the high traffic volume, which can lead to the necessity of having routing strategies that deal with different traffic loads in the network, aimed at avoiding congestion, lowering delays, and still high performance.