XOS: An Application-Defined Operating System for Datacenter Computing
Paper in proceeding, 2018

Rapid growth of datacenter (DC) scale, urgency of cost control, increasing workload diversity, and huge software investment protection place unprecedented demands on the operating system (OS) efficiency, scalability, performance isolation, and backward-compatibility. The traditional OSes are not built to work with deep-hierarchy software stacks, large numbers of cores, tail latency guarantee, and increasingly rich variety of applications seen in modern DCs, and thus they struggle to meet the demands of such workloads.This paper presents XOS, an application-defined OS for modern DC servers. Our design moves resource management out of the OS kernel, supports customizable kernel subsystems in user space, and enables elastic partitioning of hardware resources. Specifically, XOS leverages modern hardware support for virtualization to move resource management functionality out of the conventional kernel and into user space, which lets applications achieve near bare-metal performance. We implement XOS on top of Linux to provide backward compatibility. XOS speeds up a set of DC workloads by up to 1.6× over our baseline Linux on a 24-core server, and outperforms the state-of-the-art Dune by up to 3.3× in terms of virtual memory management. In addition, XOS demonstrates good scalability and strong performance isolation.

Scalability

Datacenter

Operating System

Performance Isolation

Application-defined

Author

Chen Zheng

Chinese Academy of Sciences

L. Wang

Chinese Academy of Sciences

Sally A McKee

Chalmers, Computer Science and Engineering (Chalmers), Computer Engineering (Chalmers)

Lixin Zhang

Chinese Academy of Sciences

Hainan Ye

Beijing Academy of Frontier Sciences and Technology

J. Zhan

Chinese Academy of Sciences

Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

398-407 8622507

2018 IEEE International Conference on Big Data, Big Data 2018
Seattle, USA,

Subject Categories

Computer Engineering

Computer Science

Computer Systems

DOI

10.1109/BigData.2018.8622507

More information

Latest update

3/15/2024