Collective communication operations in AI and HPC workloads generate heavy network traffic. Offloading these operations to network switches reduces latency, but performing arithmetic and replication at line rate is difficult, especially as port counts and link speeds grow. Existing in-network approaches rely on accumulation buffers that not only limit throughput but also require complex state management to handle stragglers and congestion. We present CCSwitch, a modular switching fabric built from 4×4 non-blocking Collective Engines (CEs). Each CE combines spatial and temporal parallelism to perform reductions without accumulation buffers. CEs compose into k-ary n-tree topologies, scaling to 32- and 256-port switches while preserving non-blocking throughput. Source routing and flit-level synchronization keep per-switch state minimal. Our FPGA implementation shows that CCSwitch's quaternary-tree reduction fabric uses up to 23% fewer LUTs and 12–30% fewer flip-flops than a comparable Clos-based design at equal throughput. Enabling the full feature set—source routing, replication, and time-multiplexed VCs—uses 1.4–1.8× more LUTs than the circuit-switched baseline, well below the 3–5× overhead typical of packet-switched NoC routers, while supporting concurrent collectives on shared links.

The rapid adoption of large language models (LLMs) has increased the need for efficient multi-tenant inference systems that maximize GPU utilization. However, existing frameworks struggle to scale due to the high memory demands of model weights and key-value (KV) caches. We present DynamoServe, a multi-tenant LLM serving framework that addresses these challenges through three key innovations: (1) leveraging stranded GPU memory to offload model weights and KV caches, (2) mitigating resource fragmentation in multi-workload environments, and (3) improving memory locality through coordinated data placement and demand-driven weight migration across GPUs. Together, these techniques enable high-throughput, low-latency inference. Experiments on state-of-the-art models show that DynamoServe significantly improves memory efficiency without sacrificing latency.

The need for fault tolerance and scalability is leading to the development of distributed SDN operating systems and applications. But how can you develop such systems and applications reliably without access to an expensive testbed? We continue to observe SDN development practices using full system virtualization or heavyweight containers, increasing complexity and overhead while decreasing usability. We demonstrate a simpler and more efficient approach: using Mininet's cluster mode to easily deploy a virtual testbed of lightweight containers on a single machine, an ad hoc cluster, or a dedicated hardware testbed. By adding an open source, distributed network operating system such as ONOS, we can create a flexible and scalable open source development platform for distributed SDN system and application software development.

In an ideal world, all research papers would be runnable: simply click to replicate all results, using the same setup as the authors. One approach to enable runnable network systems papers is Container-Based Emulation (CBE), where an environment of virtual hosts, switches, and links runs on a modern multicore server, using real application and kernel code with software-emulated network elements. CBE combines many of the best features of software simulators and hardware testbeds, but its performance fidelity is unproven.

Mininet is a system for rapidly prototyping large networks on the constrained resources of a single laptop. The lightweight approach of using OS-level virtualization features, including processes and network namespaces, allows it to scale to hundreds of nodes. Experiences with our initial implementation suggest that the ability to run, poke, and debug in real time represents a qualitative change in workflow. We share supporting case studies culled from over 100 users, at 18 institutions, who have developed Software-Defined Networks (SDN). Ultimately, we think the greatest value of Mininet will be supporting collaborative network research, by enabling self-contained SDN prototypes which anyone with a PC can download, run, evaluate, explore, tweak, and build upon.