IX: A Protected Dataplane Operating System for High Throughput and Low Latency

Edouard Bugnion, Ana Klimovic, Christos Kozyrakis, Georgios Prekas
USENIX, 2014
Conference paper

Abstract

The conventional wisdom is that aggressive networking requirements, such as high packet rates for small messages and microsecond-scale tail latency, are best addressed outside the kernel, in a user-level networking stack. We present IX, a dataplane operating system that provides high I/O performance, while maintaining the key advantage of strong protection offered by existing kernels. IX uses hardware virtualization to separate management and scheduling functions of the kernel (control plane) from network processing (dataplane). The dataplane architecture builds upon a native, zero-copy API and optimizes for both bandwidth and latency by dedicating hardware threads and networking queues to dataplane instances, processing bounded batches of packets to completion, and by eliminating coherence traffic and multi-core synchronization. We demonstrate that IX outperforms Linux and state-of-the-art, user-space network stacks significantly in both throughput and end-to-end latency. Moreover, IX improves the throughput of a widely deployed, key-value store by up to 3.6x and reduces tail latency by more than 2x.

Official source

https://infoscience.epfl.ch/record/201671?ln=en

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The conventional wisdom is that aggressive networking requirements, such as high packet rates for small messages and μs-scale tail latency, are best addressed outside the kernel, in a user-level networking stack. We present ix, a dataplane operating system that provides high I/O performance and high resource efficiency while maintaining the protection and isolation benefits of existing kernels. ix uses hardware virtualization to separate management and scheduling functions of the kernel (control plane) from network processing (dataplane). The dataplane architecture builds upon a native, zero-copy API and optimizes for both bandwidth and latency by dedicating hardware threads and networking queues to dataplane instances, processing bounded batches of packets to completion, and eliminating coherence traffic and multicore synchronization. The control plane dynamically adjusts core allocations and voltage/frequency settings to meet service-level objectives. We demonstrate that ix outperforms Linux and a user-space network stack significantly in both throughput and end-to-end latency. Moreover, ix improves the throughput of a widely deployed, key-value store by up to 6.4× and reduces tail latency by more than 2× . With three varying load patterns, the control plane saves 46%--54% of processor energy, and it allows background jobs to run at 35%--47% of their standalone throughput.

Association for Computing Machinery2016

The conventional wisdom is that aggressive networking requirements, such as high packet rates for small messages and microsecond-scale tail latency, are best addressed outside the kernel, in a user-level networking stack. In particular, dataplanes borrow design elements from network middleboxes to run tasks to completion in tight loops. In its basic form, the dataplane design leverages sweeping simplifications such as the elimination of any resource management and any task scheduling to improve throughput and lower latency. As a result, dataplanes perform best when the request rate is predictable (since there is no resource management) and the service time of each task has a low execution time and a low dispersion. On the other hand, they exhibit poor energy proportionality and workload consolidation, and suffer from head-of-line blocking. This thesis proposes the introduction of resource management to dataplanes. Current dataplanes decrease latency by constantly polling for incoming network packets. This approach trades energy usage for latency. We argue that it is possible to introduce a control plane, which manages the resources in the most optimal way in terms of power usage without affecting the performance of the dataplane. Additionally, this thesis proposes the introduction of scheduling to dataplanes. Current designs operate in a strict FIFO and run-to-completion manner. This method is effective only when the incoming request requires a minimal amount of processing in the order of a few microseconds. When the processing time of requests is (a) longer or (b) follows a distribution with higher dispersion, the transient load imbalances and head-of-line blocking deteriorate the performance of the dataplane. We claim that it is possible to introduce a scheduler to dataplanes, which routes requests to the appropriate core and effectively reduce the tail latency of the system while at the same time support a wider range of workloads.

EPFL2018

Optimizing network performance in virtual machines

Aravind Menon

In recent years, there has been a rapid growth in the adoption of virtual machine technology in data centers and cluster environments. This trend towards server virtualization is driven by two main factors: the savings in hardware cost that can be achieved through the use of virtualization, and the increased flexibility in the management of hardware resources in a cluster environment. An important consequence of server virtualization is the negative impact it has on the networking performance of server applications running in virtual machines (VMs). In this thesis, we address the problem of efficiently virtualizing the network interface in Type-II virtual machine monitors. In the Type-II architecture, the VMM relies on a special, 'host' operating system to provide the device drivers to access I/O devices, and executes the drivers within the host operating system. Using the Xen VMM as an example of this architecture, we identify fundamental performance bottlenecks in the network virtualization architecture of Type-II VMMs. We show that locating the device drivers in a separate host VM is the primary reason for performance degradation in Type-II VMMs, because of two reasons: a) the switching between the guest and the host VM for device driver invocation, and, b) I/O virtualization operations required to transfer packets between the guest and the host address spaces. We present a detailed analysis of the virtualization overheads in the Type-II I/O architecture, and we present three solutions which explore the performance that can be achieved while performing network virtualization at three different levels: in the host OS, in the VMM, and in the NIC hardware. Our first solution consists of a set of packet aggregation optimizations that explores the performance achievable while retaining the Type-II I/O architecture in the Xen VMM. This solution retains the core functionality of I/O virtualization, including device driver execution, in the Xen 'driver domain'. With this set of optimizations, we achieve an improvement by a factor of two to four in the networking performance of Xen guest domains. In our second solution, we move the task of I/O virtualization and device driver execution from the host OS to the Xen hypervisor. We propose a new I/O virtualization architecture, called the TwinDrivers framework, which combines the performance advantages of Type-I VMMs with the safety and software engineering benefits of Type-II VMMs. (In a Type-I VMM, the device driver executes directly in the hypervisor, and gives much better performance than a Type-II VMM). The TwinDrivers architecture results in another factor of two improvements in networking performance for Xen guest domains. Finally, in our third solution, we describe a hardware based approach to network virtualization, in which we move the task of network virtualization into the network interface card (NIC). We develop a specialized network interface (CDNA) which allows guest operating systems running in VMs to directly access a private, virtual context on the NIC for network I/O, bypassing the host OS entirely. This approach also yields performance benefits similar to the TwinDrivers software-only approach. Overall, our solutions help significantly bridge the gap between the network performance in a virtualized environment and a native environment, eventually achieving network performance in a virtual machine within 70% of the native performance.

EPFL2009

Optimizing network performance in virtual machines

Aravind Menon

EPFL2009