We present Presto, the first TCP stack that delivers ASIC-class performance and energy efficiency on programmable Reconfigurable Match-Action Table (RMT) pipelines, providing flexibility while retaining standard TCP semantics and POSIX socket compatibility. The key challenge in designing Presto is reconciling TCP's complex, dependent state updates with RMT's unidirectional, lock-step execution model. To overcome this challenge, Presto introduces three novel techniques: optimistic concurrency (speculative updates validated downstream), pseudo-segment injection (circular dependency resolution without stalls), and bump-in-the-wire processing (singlepass segment handling). Together, these enable TCP retransmission, reassembly, flow, and congestion control, as a pipeline of simple match-action operations.

In cellular communication systems, radio resources are allocated to users by the MAC scheduler, that typically runs at the base station (BS). The task of the scheduler is to meet the quality of service (QoS) requirements of each data flow while maximizing the system throughput and achieving a desired level of fairness amongst users. Traditional schedulers use handcrafted metrics and are meticulously tuned to achieve a delicate balance between multiple, often conflicting objectives. Diverse QoS requirements of 5G networks further complicate traditional schedulers. In this paper, we propose a novel reinforcement learning based scheduler that learns an allocation policy to simultaneously optimize multiple objectives. Our approach allows network operators to customize their requirements, by assigning priority values to QoS classes. In addition, we adopt a flexible neural-network architecture that can easily adapt to varying number of flows, drastically simplifying training, thus rendering it viable for practical implementation in constrained systems. We demonstrate, via simulations, that our algorithm outperforms conventional heuristics such as M-LWDF, EXP-RULE and LOGRULE and is robust to changes in radio environment and traffic patterns.