Packet Flow Capacity Autonomous Operation Based on Reinforcement Learning

As the dynamicity of the traffic increases, the need for self-network operation becomes more evident. One of the solutions that might bring cost savings to network operators is the dynamic capacity management of large packet flows, especially in the context of packet over optical networks. Machine Learning, particularly Reinforcement Learning, seems to be an enabler for autonomicity as a result of its inherent capacity to learn from experience. However, precisely because of that, RL methods might not be able to provide the required performance (e.g., delay, packet loss, and capacity overprovisioning) when managing the capacity of packet flows, until they learn the optimal policy. In view of that, we propose a management lifecycle with three phases: (i) a self-tuned threshold-based approach operating just after the packet flow is set up and until enough data on the traffic characteristics are available; (ii) an RL operation based on models pre-trained with a generic traffic profile; and (iii) an RL operation with models trained for real traffic. Exhaustive simulation results confirm the poor performance of RL algorithms until the optimal policy is learnt and when traffic characteristics change over time, which prevents deploying such methods in operators’ networks. In contrast, the proposed lifecycle outperforms benchmarking approaches, achieving noticeable performance from the beginning of operation while showing robustness against traffic changes.

BB-Gen: A Packet Crafter for Data Plane Evaluation

With the advent of research on fast path packet processing, traffic generator tools witnessed many entrants with features ranging from supporting list of protocols, analyzing network traffic to measuring throughput and latency of packets. While approaching towards feature completeness, the tools are becoming more complex every time making it difficult to port, manage, and use. BBGen with a sole focus on simplicity complements other traffic generators instead of trying to replace them. BB-Gen is a simple CLI-based packet crafter to generate packet flows formatted as PCAP files. The tool supports different standard protocols and creates the necessary traces for network function configuration and testing. It allows creating PCAPs for worst and best case scenarios with all unique flows or following flow distributions published elsewhere. In this demo, we feature BB-Gen as used by the MACSAD development team to test P4-based software switch pipelines.

Advancing Network Flow Information Using Collaborative Filtering

Collaborative Filtering algorithms are frequently employed in e-commerce. However, this kind of algorithms can also be useful in other domains. In an information system thousands of bytes are sent through the network every second. Analyzing this data can require too much time and many resources, but it is necessary for ensuring the right operation of the network. Results are used for profiling, security analysis, traffic engineering and many other purposes. Nowadays, as a complement to a deep inspection of the data, it is more and more common to monitor packet flows, since it consumes less resources and it allows to react faster to any network situation. In a typical ow monitoring system, flows are exported to a collector, which stores the information before being analyzed. However, many collectors work based on time slots, so they do not analyze the flows when they are just received, generating a delay. In this work we demonstrate how Collaborative Filtering algorithms can be applied to this new domain. In particular, using information about past flows, these algorithms can anticipate future flows before being captured. This way, time required for detecting and responding to different network situations is reduced.