Chrome Extension
WeChat Mini Program
Use on ChatGLM

Using Xen and KVM As Real-Time Hypervisors.

Journal of Systems Architecture (JSA)(2020)CCF BSCI 2区

Scuola Super Sant Anna | SUSE Software Solut

Cited 33|Views35
Abstract
The recent developments in virtualisation technologies have made feasible the execution of complex and performance-critical applications in virtual machines. Some of such applications are characterised by real-time constraints and require a predictable scheduling of virtual machines on physical cores, hence several works in real-time literature have proposed advanced scheduling and design techniques to respect the application constraints. This paper complements those works, investigating the latencies introduced by two of the most widely used open-source hypervisors, Xen and KVM. Some guidelines for properly configuring the VMs in order to reduce the introduced latencies (so that previous theoretical analysis and algorithms can be used in practice) are also provided, showing that both KVM and Xen are usable as real-time hypervisors.
More
Translated text
Key words
Real-Time,Virtualisation,Xen,KVM
PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers

RT-Cloud: Virtualization Technologies and Cloud Computing for Railway Use-Case

2021 IEEE 24TH INTERNATIONAL SYMPOSIUM ON REAL-TIME DISTRIBUTED COMPUTING (ISORC 2021) 2021

被引用15

PhD Forum Abstract: Ultra-low Latency Communication in TSN-based Virtual Environments

2021 IEEE International Conference on Smart Computing (SMARTCOMP) 2021

被引用0

Control-Flow Integrity for Real-Time Operating Systems: Open Issues and Challenges

2021 IEEE East-West Design &amp Test Symposium (EWDTS) 2021

被引用0

Design of A Dual-Linux System Based on Xen Virtualization Project on i.MX8

何照丹, 倪子豪
Automobile Technology 2021

被引用0

Evaluating the OpenAMP framework in real-time embedded SoC platforms

2021 XXXVI Conference on Design of Circuits and Integrated Systems (DCIS) 2021

被引用5

An Asset-Based Approach to Mitigate Zero-Day Ransomware Attacks

Computers, materials & continua/Computers, materials & continua (Print) 2022

被引用6

Shedding Light on Static Partitioning Hypervisors for Arm-based Mixed-Criticality Systems.

2023 IEEE 29TH REAL-TIME AND EMBEDDED TECHNOLOGY AND APPLICATIONS SYMPOSIUM, RTAS 2023

被引用7

Network Aspects of Virtualized Industrial Control from the Edge Cloud Enabled by COTS Hardware

2023 7th International Conference on Automation, Control and Robots (ICACR) 2023

被引用0

A Time Series-Based Approach to Elastic Kubernetes Scaling

Haibin Yuan, Shengchen Liao
ELECTRONICS 2024

被引用2

Taming Edge Computing for Hard Real-Time Advanced Control of Mechatronic Systems

Luca Orciari, Davide Raggini,Andrea Tilli
IEEE Transactions on Industrial Informatics 2024

被引用0

Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:本文研究了Xen和KVM两种开源虚拟机监视器作为实时虚拟化解决方案的可行性,提出了针对减少引入延迟的配置指南,表明两者均可作为实时虚拟化监视器使用。

方法】:作者通过分析Xen和KVM的延迟,评估了它们作为实时hypervisor的性能。

实验】:实验对Xen和KVM在实时环境下的表现进行了测试,具体数据集名称未提及,但提供了配置虚拟机以减少延迟的指导原则,并得出了两种hypervisor在实时应用中的可用性结论。