As deep reinforcement learning (RL) showcases its strengths in networking and systems, its pitfalls also come to the public's attention-when trained to handle a wide range of network workloads and previously unseen deployment environments, RL policies often manifest suboptimal performance and poor generalizability.To tackle these problems, we present Genet, a new training framework for learning better RL-based network adaptation algorithms. Genet is built on the concept of curriculum learning, which has proved effective against similar issues in other domains where RL is extensively employed. At a high level, curriculum learning gradually presents more difficult environments to the training, rather than choosing them randomly, so that the current RL model can make meaningful progress in training. However, applying curriculum learning in networking is challenging because it remains unknown how to measure the "difficulty" of a network environment.Instead of relying on handcrafted heuristics to determine the environment's difficulty level, our insight is to utilize traditional rule-based (non-RL) baselines: If the current RL model performs significantly worse in a network environment than the baselines, then the model's potential to improve when further trained in this environment is substantial. Therefore, Genet automatically searches for the environments where the current model falls significantly behind a traditional baseline scheme and iteratively promotes these environments as the training progresses. Through evaluating Genet on three use cases-adaptive video streaming, congestion control, and load balancing, we show that Genet produces RL policies which outperform both regularly trained RL policies and traditional baselines in each context, not only under synthetic workloads but also in real environments.
Edge video analytics is becoming the solution to many safety and management tasks. Its wide deployment, however, must first address the tension between inference accuracy and resource (compute/network) cost. This has led to the development of video analytics pipelines (VAPs), which reduce resource cost by combining DNN compression/speedup techniques with video processing heuristics. Our measurement study, however, shows that today's methods for evaluating VAPs are incomplete, often producing premature conclusions or ambiguous results. This is because each VAP's performance varies substantially across videos and time, and is sensitive to different subsets of video content characteristics.We argue that accurate VAP evaluation must first characterize the complex interaction between VAPs and video characteristics, which we refer to as VAP performance clarity. We design and implement Yoda, the first VAP benchmark to achieve performance clarity. Using primitive-based profiling and a carefully curated benchmark video set, Yoda builds a performance clarity profile for each VAP to precisely define its accuracy/cost tradeoff and its relationship with video characteristics. We show that Yoda substantially improves VAP evaluations by (1) providing a comprehensive, transparent assessment of VAP performance and its dependencies on video characteristics; (2) explicitly identifying fine-grained VAP behaviors that were previously hidden by large performance variance; and (3) revealing strengths/weaknesses among different VAPs and new design opportunities.
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