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A Transparent Highway for inter-Virtual Network Function Communication with Open vSwitch
Abstract
This paper presents a software architecture that can dynamically and transparently establish direct communication paths between DPDK-based virtual network functions executed in virtual machines, by recognizing new point-to-point connections in traffic steering rules. We demonstrate the huge advantages of this architecture in terms of performance and the possibility to implement it with localized modifications in Open vSwitch and DPDK, without touching the VNFs.
This chapter provides a self‐contained overview of the vertical requirements as well as the technical and architectural concepts that is critical in delivering ultra‐reliable and low‐latency communications (URLLC) services. It presents a number of popular URLLC use cases. The chapter focuses on the new 5G network architecture and how it can be interfaced with URLLC applications. It explains the evolution of software defined networking and network function virtualization concepts, 5G core network and notions such as application specific slicing. A cloud‐native architecture divides the tightly integrated and virtualized network functions‐specific software stack into more scalable, reusable and efficiently deployable software components. The implications of cloud native network functions and architecture on provisioning URLLC services should be carefully considered. The 5G system service‐based architecture and extensions for vertical and URLLC applications are under discussion in 3GPP standards.
The Network Functions Virtualization (NFV) is an emerging solution that improves the flexibility, efficiency, and manageability of networks by leveraging virtualization and cloud computing technologies to run network appliances in software. The implementation of NFV presents issues such as the introduction of new software components, bottleneck performance, and monitoring hidden traffic. A considerable amount of traffic in NFV is invisible using traditional monitoring strategies because it does not hit a physical link. The implementation of autonomic management and supervised algorithms from Machine Learning (ML) become a key strategy to manage this hidden traffic. In this work, we focus on analyzing the traffic features of an NFV-based network while performing a benchmarking of the behavior of supervised ML algorithms in the IP traffic classification regarding their efficiency; considering that the efficiency of an algorithm depends on the trade-off between the response time and the precision. Our results demonstrate the NaiveBayes algorithm as the best traffic classifier. NaiveBayes reaches values of 99.9% with precision in 1.1sec.
Supporting network I/O at high packet rates in virtual machines is fundamental for the deployment of Cloud data centers and Network Function Virtualization. Historically, SR-IOV and hardware passthrough were thought as the only viable solution to reduce the high cost of virtualization. In previous work [15] we showed how even plain device emulation can achieve VM-to-VM speeds of millions of packets per second (Mpps), though still at least 3 times slower than bare metal. In this paper, to fill this gap, we present ptnetmap, a virtual passthrough network device (based on the netmap framework). ptnetmap allows VMs to connect to any netmap port (physical devices, software switches, netmap pipes), conserving the speed and isolation of the native netmap system, and removing the constraints of hardware passthrough. Our work includes two key features not present in previous proposals: we provide a high speed path also to untrusted VMs, and do not require dedicated polling cores/threads, which is fundamental to achieve an efficient use of resources. Besides these features, our speed is also beyond previously published values. Running on top of ptnetmap, VMs can saturate a 10 Gbit link at 14.88 Mpps, talk at over 20 Mpps to untrusted VMs, and over 70 Mpps to trusted VMs. ptnetmap extends the netmap framework, and currently supports Linux and FreeBSD guests, and QEMU/KVM host. Support for bhyve/FreeBSD host is under development.