Multi-access networks are increasingly important for reliable end-to-end connectivity and enhanced throughput performance. A scalable multi-access solution is required to roll out multi-access networks at scale. However, existing CPU-based solutions can no longer scale sustainably, as network traffic has outgrown the CPU performance growth. Consequently, hardware accelerators offer a compelling alternative. This paper introduces JUNCTION, a scalable multi-access solution designed using programmable switches. JUNCTION features a multipath protocol tailored to the hardware constraints and optimized for efficient memory utilization, enabling it to handle a large number of multipath sessions. We validate JUNCTION on a 5G-WiFi multi-access testbed. Our analysis demonstrates that it can scale an order of magnitude better than existing solutions.

Packet loss due to link corruption is a major problem in large warehouse-scale datacenters. The current state-of-the-art approach of disabling corrupting links is not adequate because, in practice, all the corrupting links cannot be disabled due to capacity constraints. In this paper, we show that, it is feasible to implement link-local retransmission at sub-RTT timescales to completely mask corruption packet losses from the transport endpoints. Our system, LinkGuardian, employs a range of techniques to (i) keep the packet buffer requirement low, (ii) recover from tail packet losses without employing timeouts, and (iii) preserve packet ordering. We implement LinkGuardian on the Intel Tofino switch and show that for a 100G link with a loss rate of 10−3, LinkGuardian can reduce the loss rate by up to 6 orders of magnitude while incurring only 8% reduction in effective link speed. By eliminating tail packet losses, LinkGuardian improves the 99.9th percentile flow completion time (FCT) for TCP and RDMA by 51x and 66x respectively. Finally, we also show that in the context of datacenter networks, simple out-of-order retransmission is often sufficient to significantly mitigate the impact of corruption packet loss for short TCP flows.

5G networks are envisioned to support various emerging use cases, such as telemedicine, remote construction, autonomous driving, industrial automation, drone control, and immersive entertainment. These applications demand low latency, high reliability, and in some cases require ultra-high-bandwidths. Specifically, these applications require 5G networks to provide 1ms end-to-end latency with 99.99% reliability [12] for ultra-reliable low-latency communications (URLLC). Various studies [11, 19, 26] have shown that the radio access network (RAN) remains the bottleneck in realizing low-latency communications.

Packet corruption loss is a serious problem in datacenter networks. A large-scale study by Microsoft reported that the number of packets lost due to corruption is comparable to those lost due to congestion. Previous attempts to mitigate the impact of packet corruption loss seek to avoid the faulty links by routing around them, at the cost of reduced link capacities and disruption to the rest of the network.

5G networks are gaining pace in development and deployment in recent years. One of 5G's key objective is to support a variety of use cases with different Service Level Objectives (SLOs). Slicing is a key part of 5G that allows operators to provide a tailored set of resources to different use cases in order to meet their SLOs. Existing works focus on slicing in the frontend or the C-RAN. However, slicing is missing in the fronthaul network that connects the frontend to the C-RAN. This leads to over-provisioning in the fronthaul and the C-RAN, and also limits the scalability of the network.

Slicing is a critical technology in 5G, as it allows operators to slice a physical network into multiple virtual networks, each dedicated to a different use case/Mobile Virtual Network Operator (MVNO) [2]. Network slicing enables network operators to deploy a tailored set of resources for specific use cases or MVNO. For example, high performance reliable hardware is required only for ultra-reliable low-latency (uRLLC) use cases such as autonomous vehicle networks. Such tailoring of services reduces costs for network operators. Further, 5G systems can now be deployed more quickly due to virtualization provided by slicing, thereby enabling faster time-to-market. To this end, there exists a large body of work that introduces slicing in different parts of the cellular network (see Fig. 1). PRAN [12] and FlexRAN [13] provide slicing in the Radio Access Network (RAN) while Orion [14] provides slicing for the frontend (wireless spectrum). The fronthaul connects the frontend base station to the RAN and carries digitized radio signals between the two parts of the cellular network. However, to the best of our knowledge, there exists no work on slicing in the fronthaul. This severely limits the benefits of slicing in the RAN and the frontend (see §1.1).

In order to provide greater network capacity, the use of small base stations such as Femtocells has increased to allow higher spectrum reuse. In these Femtocells, base station designers have started to explore the use of general purpose multi-core architectures to provide greater flexibility. Multi-core architectures allow power-performance trade-off possibilities through techniques such as Dynamic Voltage Frequency Scaling (DVFS) and Power Gating. In this work, we propose a power management framework based on reinforcement learning called PR3, which uses both DVFS and Power Gating. Our approach is unique as it introduces a feedback from the network scheduler and baseband processor to the Power Governor, so that information about both the network and computation workloads are included in the decision making. Evaluation on a hardware platform (Odroid XU3) running PHY LTE uplink baseband processing benchmark, shows that PR3performs well in terms of both power and latency. It is able to save upto 50% power while maintaining low processing latency. PR3is also adaptive, making it effective over a wide range of traffic loads.