A Labeled Architecture for Low-Entropy Clouds: Theory, Practice, and Lessons

Author:

Zhang Chuanqi12ORCID,Wang Sa123,Yu Zihao12,Wang Huizhe12,Xu Yinan12,Cai Luoshan12,Tang Dan1,Sun Ninghui12,Bao Yungang124

Affiliation:

1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

2. University of Chinese Academy of Sciences, Beijing, China

3. Institute of Computing Technology (Nanjing), Chinese Academy of Sciences, Nanjing, China

4. Peng Cheng Laboratory, Shenzhen, China

Abstract

Resource efficiency and quality of service (QoS) are both long-pursuit goals for cloud providers over the last decade. However, hardly any cloud platform can exactly achieve them perfectly even until today. Improving resource efficiency or resource utilization often could cause complicated resource contention between colocated cloud applications on different resources, spanning from the underlying hardware to the software stack, leading to unexpected performance degradation. The low-entropy cloud proposes a new software-hardware codesigned technology stack to holistically curb performance interference from the bottom up and obtain both high resource efficiency and high quality of application performance. In this paper, we introduce a new computer architecture for the low-entropy cloud stack, called labeled von Neumann architecture (LvNA), which incorporates a set of label-powered control mechanisms to enable shared components and resources on chip to differentiate, isolate, and prioritize user-defined application requests when competing for hardware resource. With the power of these mechanisms, LvNA was able to protect the performance of certain applications, such as latency-critical applications, from disorderly resource contention while improving resource utilization. We further build and tapeout Beihai, a 1.2 GHz 8-core RISC-V processor based on the LvNA architecture. The evaluation results show that Beihai could drastically reduce the performance degradation caused by memory bandwidth contention from 82.8% to 0.4%. When improving the CPU utilization over 70%, Beihai could reduce the 99th tail latency of Redis from 115 ms to 18.1 ms. Furthermore, Beihai can realize hardware virtualization, which boots up two unmodified virtual machines concurrently without the intervention of any software hypervisor.

Funder

Strategic Priority Research Program of the Chinese Academy of Sciences

Youth Innovation Promotion Association of the Chinese Academy of Sciences

National Natural Science Foundation of China

National Basic Research Program of China

Publisher

American Association for the Advancement of Science (AAAS)

Reference44 articles.

1. J. Leverich and C. Kozyrakis “Reconciling high server utilization and sub-millisecond quality–of-service ” in Proceedings of the Ninth European Conference on Computer Systems Amsterdam The Netherlands 2014 pp. 1–14

2. Y. Xu M. Bailey B. Noble and F. Jahanian “Small is better: avoiding latency traps in virtualized data centers ” in Proceedings of the 4th annual Symposium on Cloud Computing Santa Clara California 2013 pp. 1–16

3. Y. Xu Z. Musgrave B. Noble and M. Bailey “Bobtail: avoiding long tails in the cloud ” in 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI 13) Lombard IL 2013 pp. 329–341

4. M. Yu A. Greenberg D. Maltz J. Rexford L. Yuan S. Kandula and C. Kim “Profiling network performance for multi-tier data center applications ” in 8th USENIX Symposium on Networked Systems Design and Implementation (NSDI 11) Boston MA 2011

5. Deadline-aware datacenter tcp (d2tcp);Vamanan B.;ACM SIGCOMM Computer Communication Review,2012

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3