Software Data Planes Research Articles

A Forwarding Latency Optimization Method for Software Data Plane Based on Spin-Polling

Modern software data planes use spin-polling and batch processing mechanisms to significantly improve maximum throughput and forwarding latency. The user-level IO queue-based spin polling mechanism has a higher response speed than the traditional interrupt mechanism. The batch mechanism enables the software data plane to achieve higher throughput by amortizing the IO overhead over multiple packets. However, the software data plane under the spin-polling mechanism keeps running at full speed regardless of the input traffic rate, resulting in significant performance waste. At the same time, we find that the batch processing mechanism does not cope well with different input traffic, mainly reflected in the forwarding latency. The purpose of this paper is to optimize the forwarding latency by leveraging the wasted performance. We propose a forwarding latency optimization scheme for the software data plane based on the spin polling mechanism in this paper. First, we calculate the CPU utilization of the software data plane according to the number of cycles the CPU spends on the valuable task. Then, our scheme controls the Tx queues and dynamically adjusts the output batch size based on the CPU utilization to optimize the forwarding latency of the software data plane. Compared with the original software data plane, the evaluation result shows that the forwarding latency can be reduced by 3.56% to 45% (in a single queue evaluation) and 4.35% to 55.54% (in a multiple queue evaluation).

Programming socket-independent network functions with nethuns

Software data planes running on commodity servers are very popular in real deployments. However, to attain top class performance, the software approach requires the adoption of accelerated network I/O frameworks, each of them characterized by its own programming model and API. As a result, network applications are often closely tied to the underlying technology, with obvious issues of portability over different systems. This is especially true in cloud scenarios where different I/O frameworks could be installed depending on the configuration of the physical servers in the infrastructure. The nethuns library proposes a unified programming abstraction to access and manage network operations over different I/O frameworks. The library is freely available to the community under the BSD license and currently supports AF_XDP and netmap for fast packet handling along with the classic AF_PACKET and the pcap library. Network applications based on nethuns need only to be re-compiled to run over a different network API. The experiments prove that the overhead introduced by nethuns is negligible, hence making it a convenient programming platform that eases the coding process while guaranteeing high performance and portability. As proofs of concept, a handy traffic generator as well as the popular Open vSwitch application have been successfully ported and tested over nethuns.

Accuracy-Aware Adaptive Traffic Monitoring for Software Dataplanes

Network operators have recently been developing multi-Gbps traffic monitoring tools on commodity hardware, as part of the packet-processing pipelines realizing software dataplanes. These solutions allow the execution of sophisticated per-packet monitoring using the processing power available on servers. Although advances in packet capture have enabled the interception of packets at high rates, bottlenecks can still arise in the monitoring process as a result of concurrent access to shared processor resources, variations of the traffic skew, and unbalanced packet-rate spikes. In this paper we present an adaptive monitoring framework, MONA, which is resilient to bottlenecks while maintaining the accuracy of monitoring reports above a user-specified threshold. MONA dynamically reduces the measurement task sets under adverse conditions, and reconfigures them to recover potential accuracy degradations. To quantify the monitoring accuracy at run time, MONA adopts a novel task-independent technique that generates accuracy estimates according to recently observed traffic characteristics. With a prototype implementation based on a generic packet-processing pipeline, and using well-known measurements tasks, we show that MONA achieves lossless traffic monitoring for a wide range of conditions, significantly enhances the level of monitoring accuracy, and performs adaptations at the time scale of milliseconds with limited overhead.

A Scalable VPN Gateway for Multi-Tenant Cloud Services

Major cloud providers offer networks of virtual machines with private IP addresses as a service on the cloud. To isolate the address space of different customers, customers are required to tunnel their traffic to a Virtual Private Network (VPN) gateway, which is typically a middlebox inside the cloud that internally tunnels each packet to the correct destination. To improve performance, an increasing number of enterprises connect directly to the cloud provider's network at the edge, to a device we call the provider's edge (PE). PE is a chokepoint for customer's traffic to the cloud, and therefore a natural candidate for implementing network functions concerning customers' virtual networks, including the VPN gateway, to avoid a detour to middleboxes inside the cloud. At the scale of today's cloud providers, VPN gateways need to maintain information for around a million internal tunnels. We argue that no single commodity device can handle these many tunnels while providing a high enough port density to connect to hundreds of cloud customers at the edge. Thus, in this paper, we propose a hybrid architecture for the PE, consisting of a commodity switch, connected to a commodity server which uses Data-Plane Development Kit (DPDK) for fast packet processing. This architecture enables a variety of network functions at the edge by offering the benefits of both hardware and software data planes. We implement a scalable VPN gateway on our proposed PE and show that it matches the scale requirements of today's cloud providers while processing packets close to line rate.

Reconfigurable Data Planes for Scalable Network Virtualization

Network virtualization presents a powerful approach to share physical network infrastructure among multiple virtual networks. Recent advances in network virtualization advocate the use of field-programmable gate arrays (FPGAs) as flexible high performance alternatives to conventional host virtualization techniques. However, the limited on-chip logic and memory resources in FPGAs severely restrict the scalability of the virtualization platform and necessitate the implementation of efficient forwarding structures in hardware. The research described in this manuscript explores the implementation of a scalable heterogeneous network virtualization platform that integrates virtual data planes implemented in FPGAs with software data planes created using host virtualization techniques. The system exploits data plane heterogeneity to cater to the dynamic service requirements of virtual networks by migrating networks between software and hardware data planes. We demonstrate data plane migration as an effective technique to limit the impact of traffic on unmodified data planes during FPGA reconfiguration. Our system implements forwarding tables in a shared fashion using inexpensive off-chip memories and supports both Internet Protocol (IP) and non-IP-based data planes. Experimental results show that FPGA-based data planes can offer two orders of magnitude better throughput than their software counterparts, and FPGA reconfiguration can facilitate data plane customization within 12 seconds. An integrated system that supports up to 15 virtual networks has been validated on the NetFPGA platform.

Virtual routers on the move

The complexity of network management is widely recognized as one of the biggest challenges facing the Internet today. Point solutions for individual problems further increase system complexity while not addressing the underlying causes. In this paper, we argue that many network-management problems stem from the same root cause---the need to maintain consistency between the physical and logical configuration of the routers. Hence, we propose VROOM (Virtual ROuters On the Move), a new network-management primitive that avoids unnecessary changes to the logical topology by allowing (virtual) routers to freely move from one physical node to another. In addition to simplifying existing network-management tasks like planned maintenance and service deployment, VROOM can also help tackle emerging challenges such as reducing energy consumption. We present the design, implementation, and evaluation of novel migration techniques for virtual routers with either hardware or software data planes. Our evaluation shows that VROOM is transparent to routing protocols and results in no performance impact on the data traffic when a hardware-based data plane is used.