The latest release of the Xen Project, version 4.9, focuses on advanced features for embedded, automotive and native-cloud-computing use cases, enhanced boot configurations for more portability across different hardware platforms, the addition of new x86 instructions to hasten machine learning computing, and improvements to existing functionality related to the ARM architecture, device model operation hypercall, and more.
The Xen Project, hosted at The Linux Foundation, continues to see growth in embedded and automotive environments as more companies look to expand virtualization to embedded devices while continuing to reap the benefits of the hypervisor, including cost savings due to consolidation; abstraction of the hardware to allow applications to be decoupled from hardware specifics; and the benefit of hardware-based isolation to better protect against software defects and to contain failures.
In addition, more contributions would start to lay the foundation for hypervisor features and benefits in cloud-native platforms.
“Contributions with the Xen Project have greatly expanded over the last few years, and we are seeing more companies participating in the project with an eye toward automotive, embedded, security, and native-cloud computing,” said Lars Kurth, Chairperson of the Xen Project Advisory Board. “We are very excited to see this engagement from a community standpoint as these additional contributors help the Xen Project progress in embedded, automotive and security, but also conversely help our more traditional stronghold environments like in server virtualization, Infrastructure as a Service, and desktop virtualization.”
Expanding Xen Project Features in Embedded and Automotive include:
- The ‘null’ scheduler, which enables use cases where every virtual CPU can be assigned to a physical CPU removing almost all of the scheduler overheads in automotive and embedded environments. Usage of the ‘null’ scheduler would guarantee near zero scheduling overhead, significantly lower latency, and more predictable performance.
- The new vwfi parameter for ARM (virtual Wait For Interrupt) would allow fine-grained control of how the Xen Project Hypervisor handles WFI (Wait for Interrupt) instructions. Setting vwfi to ‘native’ would reduce interrupt latency by approximately 60%.
- Xen 4.9 includes new standard ABIs for sharing devices between virtual machines (including reference implementations) for a number of embedded, automotive and cloud native computing use cases.
- For embedded/automotive a virtual sound ABI was added implementing audio playback and capture as well as volume control and the possibility to mute/unmute audio sources. In addition a new virtual display ABI for complex display devices exposing multiple framebuffers and displays has been added. Multi-touch support has been added to the virtual keyboard/mouse protocol (enabling touch screens).
During the Xen 4.9 release cycle, a Xen 9pfs frontend was upstreamed in the Linux kernel and a backend in QEMU. It is now possible to share a filesystem from one virtual machine to another, which is a requirement for adding Xen Project support to many container engines, such as CoreOS rkt.
The PV Calls ABI has also been introduced to allow forwarding POSIX requests across guests: a POSIX function call originating from an app in a DomU can be forwarded and implemented in Dom0. For example, guest networking socket calls can be executed to Dom0, enabling a new networking model which is a natural fit for cloud-native apps.
“Xen plays an important role in the future of embedded systems and the next generation of data centers and cloud computing,” said Philippe Robin, Director of Open Source, ARM. “Performance, efficiency and reliability are fundamental attributes of the ARM architecture, and enabling lower interrupt latency and the inclusion of features to better support system error detection is a big step forward in improving reliability and serviceability, while maintaining the right levels of performance.”
Contributions for this release of the Xen Project hypervisor came from Amazon, AMD, Aporeto, ARM, BitDefender, Citrix, EPAM, Fujitsu, Huawei Technologies, Intel, Invisible Things Lab, Nokia, Oracle, Star Lab, Suse, Xilinx, Zentific, and a number of universities and individuals.