Roaming Edge vNFs using Glasgow Network Functions

While the network edge is becoming more important for the provision of customized services in next generation mobile networks, current NFV architectures are unsuitable to meet the increasing future demand. They rely on commodity servers with resource-hungry Virtual Machines that are unable to provide the high network function density and mobility requirements necessary for upcoming wide-area and 5G networks. In this demo, we showcase Glasgow Network Functions (GNF), a virtualization framework suitable for next generation mobile networks that exploits lightweight network functions (NFs) deployed at the edge and transparently following users' devices as they roam between cells

RackBlox: A Software-Defined Rack-Scale Storage System with Network-Storage Co-Design

Software-defined networking (SDN) and software-defined flash (SDF) have been serving as the backbone of modern data centers. They are managed separately to handle I/O requests. At first glance, this is a reasonable design by following the rack-scale hierarchical design principles. However, it suffers from suboptimal end-to-end performance, due to the lack of coordination between SDN and SDF. In this paper, we co-design the SDN and SDF stack by redefining the functions of their control plane and data plane, and splitting up them within a new architecture named RackBlox. RackBlox decouples the storage management functions of flash-based solid-state drives (SSDs), and allow the SDN to track and manage the states of SSDs in a rack. Therefore, we can enable the state sharing between SDN and SDF, and facilitate global storage resource management. RackBlox has three major components: (1) coordinated I/O scheduling, in which it dynamically adjusts the I/O scheduling in the storage stack with the measured and predicted network latency, such that it can coordinate the effort of I/O scheduling across the network and storage stack for achieving predictable end-to-end performance; (2) coordinated garbage collection (GC), in which it will coordinate the GC activities across the SSDs in a rack to minimize their impact on incoming I/O requests; (3) rack-scale wear leveling, in which it enables global wear leveling among SSDs in a rack by periodically swapping data, for achieving improved device lifetime for the entire rack. We implement RackBlox using programmable SSDs and switch. Our experiments demonstrate that RackBlox can reduce the tail latency of I/O requests by up to 5.8x over state-of-the-art rack-scale storage systems.Comment: 14 pages. Published in published in ACM SIGOPS 29th Symposium on Operating Systems Principles (SOSP'23

A one-pass clustering based sketch method for network monitoring

Network monitoring solutions need to cope with increasing network traffic volumes, as a result, sketch-based monitoring methods have been extensively studied to trade accuracy for memory scalability and storage reduction. However, sketches are sensitive to skewness in network flow distributions due to hash collisions, and need complicated performance optimization to adapt to line-rate packet streams. We provide Jellyfish, an efficient sketch method that performs one-pass clustering over the network stream. One-pass clustering is realized by adapting the monitoring granularity from the whole network flow to fragments called subflows, which not only reduces the ingestion rate but also provides an efficient intermediate representation for the input to the sketch. Jellyfish provides the network-flow level query interface by reconstructing the network-flow level counters by merging subflow records from the same network flow. We provide probabilistic analysis of the expected accuracy of both existing sketch methods and Jellyfish. Real-world trace-driven experiments show that Jellyfish reduces the average estimation errors by up to six orders of magnitude for per-flow queries, by six orders of magnitude for entropy queries, and up to ten times for heavy-hitter queries.This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61972409; in part by Hong Kong Research Grants Council (RGC) under Grant TRS T41-603/20-R, Grant GRF-16213621, and Grant ITF ACCESS; in part by the Spanish I+D+i project TRAINER-A, funded by MCIN/AEI/10.13039/501100011033, under Grant PID2020-118011GB-C21; and in part by the Catalan Institution for Research and Advanced Studies (ICREA Academia).Peer ReviewedPostprint (author's final draft

A Comprehensive Study on Off-path SmartNIC

SmartNIC has recently emerged as an attractive device to accelerate distributed systems. However, there has been no comprehensive characterization of SmartNIC especially on the network part. This paper presents the first comprehensive study of off-path SmartNIC. Our experimental study uncovers the key performance characteristics of the communication among the client, SmartNIC SoC, and the host. We find without considering SmartNIC hardware architecture, communications with it can cause up to 48% bandwidth degradation due to performance anomalies. We also propose implications to address the anomalies.Comment: This is the short version. Full version will appear at OSDI2

Towards Scalable Network Traffic Measurement With Sketches

Driven by the ever-increasing data volume through the Internet, the per-port speed of network devices reached 400 Gbps, and high-end switches are capable of processing 25.6 Tbps of network traffic. To improve the efficiency and security of the network, network traffic measurement becomes more important than ever. For fast and accurate traffic measurement, managing an accurate working set of active flows (WSAF) at line rates is a key challenge. WSAF is usually located in high-speed but expensive memories, such as TCAM or SRAM, and thus their capacity is quite limited. To scale up the per-flow measurement, we pursue three thrusts. In the first thrust, we propose to use In-DRAM WSAF and put a compact data structure (i.e., sketch) called FlowRegulator before WSAF to compensate for DRAM\u27s slow access time. Per our results, FlowRegulator can substantially reduce massive influxes to WSAF without compromising measurement accuracy. In the second thrust, we integrate our sketch into a network system and propose an SDN-based WLAN monitoring and management framework called RFlow+, which can overcome the limitations of existing traffic measurement solutions (e.g., OpenFlow and sFlow), such as a limited view, incomplete flow statistics, and poor trade-off between measurement accuracy and CPU/network overheads. In the third thrust, we introduce a novel sampling scheme to deal with the poor trade-off that is provided by the standard simple random sampling (SRS). Even though SRS has been widely used in practice because of its simplicity, it provides non-uniform sampling rates for different flows, because it samples packets over an aggregated data flow. Starting with a simple idea that independent per-flow packet sampling provides the most accurate estimation of each flow, we introduce a new concept of per-flow systematic sampling, aiming to provide the same sampling rate across all flows. In addition, we provide a concrete sampling method called SketchFlow, which approximates the idea of the per-flow systematic sampling using a sketch saturation event

RF-Transformer: A Unified Backscatter Radio Hardware Abstraction

This paper presents RF-Transformer, a unified backscatter radio hardware abstraction that allows a low-power IoT device to directly communicate with heterogeneous wireless receivers at the minimum power consumption. Unlike existing backscatter systems that are tailored to a specific wireless communication protocol, RF-Transformer provides a programmable interface to the micro-controller, allowing IoT devices to synthesize different types of protocol-compliant backscatter signals sharing radically different PHY-layer designs. To show the efficacy of our design, we implement a PCB prototype of RF-Transformer on 2.4 GHz ISM band and showcase its capability on generating standard ZigBee, Bluetooth, LoRa, and Wi-Fi 802.11b/g/n/ac packets. Our extensive field studies show that RF-Transformer achieves 23.8 Mbps, 247.1 Kbps, 986.5 Kbps, and 27.3 Kbps throughput when generating standard Wi-Fi, ZigBee, Bluetooth, and LoRa signals while consuming 7.6-74.2 less power than their active counterparts. Our ASIC simulation based on the 65-nm CMOS process shows that the power gain of RF-Transformer can further grow to 92-678. We further integrate RF-Transformer with pressure sensors and present a case study on detecting foot traffic density in hallways. Our 7-day case studies demonstrate RFTransformer can reliably transmit sensor data to a commodity gateway by synthesizing LoRa packets on top of Wi-Fi signals. Our experimental results also verify the compatibility of RF-Transformer with commodity receivers. Code and hardware schematics can be found at: https://github.com/LeFsCC/RF-Transformer

FatPaths: Routing in Supercomputers and Data Centers when Shortest Paths Fall Short

We introduce FatPaths: a simple, generic, and robust routing architecture that enables state-of-the-art low-diameter topologies such as Slim Fly to achieve unprecedented performance. FatPaths targets Ethernet stacks in both HPC supercomputers as well as cloud data centers and clusters. FatPaths exposes and exploits the rich ("fat") diversity of both minimal and non-minimal paths for high-performance multi-pathing. Moreover, FatPaths uses a redesigned "purified" transport layer that removes virtually all TCP performance issues (e.g., the slow start), and incorporates flowlet switching, a technique used to prevent packet reordering in TCP networks, to enable very simple and effective load balancing. Our design enables recent low-diameter topologies to outperform powerful Clos designs, achieving 15% higher net throughput at 2x lower latency for comparable cost. FatPaths will significantly accelerate Ethernet clusters that form more than 50% of the Top500 list and it may become a standard routing scheme for modern topologies