35 research outputs found
STDPG: A Spatio-Temporal Deterministic Policy Gradient Agent for Dynamic Routing in SDN
Dynamic routing in software-defined networking (SDN) can be viewed as a
centralized decision-making problem. Most of the existing deep reinforcement
learning (DRL) agents can address it, thanks to the deep neural network
(DNN)incorporated. However, fully-connected feed-forward neural network (FFNN)
is usually adopted, where spatial correlation and temporal variation of traffic
flows are ignored. This drawback usually leads to significantly high
computational complexity due to large number of training parameters. To
overcome this problem, we propose a novel model-free framework for dynamic
routing in SDN, which is referred to as spatio-temporal deterministic policy
gradient (STDPG) agent. Both the actor and critic networks are based on
identical DNN structure, where a combination of convolutional neural network
(CNN) and long short-term memory network (LSTM) with temporal attention
mechanism, CNN-LSTM-TAM, is devised. By efficiently exploiting spatial and
temporal features, CNNLSTM-TAM helps the STDPG agent learn better from the
experience transitions. Furthermore, we employ the prioritized experience
replay (PER) method to accelerate the convergence of model training. The
experimental results show that STDPG can automatically adapt for current
network environment and achieve robust convergence. Compared with a number
state-ofthe-art DRL agents, STDPG achieves better routing solutions in terms of
the average end-to-end delay.Comment: 6 pages,5 figures,accepted by IEEE ICC 202
Discovery of Flow Splitting Ratios in ISP Networks with Measurement Noise
Network telemetry and analytics is essential for providing highly dependable services in modern computer networks. In particular, network flow analytics for ISP networks allows operators to inspect and reason about traffic patterns in their networks in order to react to anomalies. High performance network analytics systems are designed with scalability in mind, and can consequently only observe partial information about the network traffic. Still, they need to provide a holistic view of the traffic, including the distribution of different traffic flows on each link. It is impractical to monitor such fine-grained telemetry, and in large, heterogeneous networks it is often too complex and error-prone, if not impossible, to access and maintain all technical specifications and router-specific configurations needed to determine e.g. the load balancing weights used when traffic is split onto multiple paths. The ratios by which flows are split on the possible paths must be derived indirectly from the measured flow demands and link utilizations. Motivated by a case study provided by a major European ISP, we suggest an efficient method to estimate the flow splitting ratios. Our approach, based on quadratic linear programming, is scalable and robust to the measurement noise found in a typical network analytics deployment. Finally, we implement an automated tool for estimating the flow splitting ratios and document its applicability on real data from the ISP