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.

Remote Direct Memory Access (RDMA) is widely used in high-performance computing (HPC) and data center networks. In this paper, we first show that RDMA does not work well with existing load balancing algorithms because of its traffic flow characteristics and assumption of in-order packet delivery. We then propose ConWeave, a load balancing framework designed for RDMA. The key idea of ConWeave is that with the right design, it is possible to perform fine granularity rerouting and mask the effect of out-of-order packet arrivals transparently in the network datapath using a programmable switch. We have implemented ConWeave on a Tofino2 switch. Evaluations show that ConWeave can achieve up to 42.3% and 66.8% improvement for average and 99-percentile FCT, respectively compared to the state-of-the-art load balancing algorithms.

Application offloads on modern high-speed programmable switches have been proposed in a variety of systems (e.g., key–value store systems and network middleboxes) so as to efficiently scale up the traditional server-oriented deployments. However, they largely achieve sub-optimal offloading efficiency due to the lack of (1) capability to perform control actions at sufficient rates, and (2) adaptability to workload changes. In this paper, we scrutinize the common stumbling blocks of existing frameworks with performance evaluations on real workloads. We present DySO (Dynamic State Offloading), a framework which enables expeditious on-demand control actions and self-tuning of management rules. DySO’s key insight is to perform control actions via a data-path instead of the switch control channel which is the bottleneck to read/write states into data plane. Our software simulations show up to 100% performance improvement compared to existing systems for various real world traces. On top of that, we implement and evaluate DySO on a commodity programmable switch, showing two orders of magnitude faster responsiveness to sudden workload changes compared to the existing systems.

We present the design and prototyping of a framework to support multicast for remote direct memory accesses (RDMA), specifically the one-sided WRITE operation. We use P4 programmable hardware to augment fixed-function RDMA transport hardware found on commodity NICs to enable one-sided RDMA multicast with zero-CPU overhead. Finally, we outline the potential challenges and future directions in realizing the framework for large-scale data center deployments.

Sketches have successfully provided accurate and fine-grained measurements (e.g., flow size and heavy hitters) which are imperative for network management. In particular, Count-Min (CM) sketch is widely utilized in many applications due to its simple design and ease of implementation. There have been many efforts to build monitoring frameworks based on Count-Min sketch. However, these frameworks either support very specific measurement tasks or they cannot be implemented on high-speed programmable hardware (PISA).