Journal of Computer Science and Technology ›› 2020, Vol. 35 ›› Issue (1): 209-220.doi: 10.1007/s11390-020-9732-x

• Special Section on Applications • Previous Articles     Next Articles

A Case for Adaptive Resource Management in Alibaba Datacenter Using Neural Networks

Sa Wang1,2,3, Member, CCF, ACM, Yan-Hai Zhu4,*, Shan-Pei Chen4, Tian-Ze Wu1,2, Member, CCF, IEEE, Wen-Jie Li1,2, Xu-Sheng Zhan1,2, Hai-Yang Ding4, Wei-Song Shi5, Fellow, IEEE, Yun-Gang Bao1,2,3, Senior Member, CCF, Member, ACM, IEEE   

  1. 1 State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences Beijing 100190, China;
    2 University of Chinese Academy of Sciences, Beijing 100049, China;
    3 Peng Cheng Laboratory, Shenzhen 518055, China;
    4 Alibaba Inc., Hangzhou 311121, China;
    5 Department of Computer Science, Wayne State University, Michigan, MI 48202, U.S.A
  • Received:2019-05-22 Revised:2019-10-14 Online:2020-01-05 Published:2020-01-14
  • Contact: Yan-Hai Zhu E-mail:gaoyang.zyh@taobao.com
  • About author:Sa Wang received his B.S. degree from University of Science and Technology of China, Hefei, in 2009 and Ph.D. degree in computer science from the Chinese Academy of Sciences (CAS), Beijing, in 2016. He is an associate professor in ICT (Institute of Computing Technology), CAS. His current research interests include operating system, system performance evaluation and optimization, distributed system. He is a member of CCF and ACM.
  • Supported by:
    This work is supported in part by the National Key Research and Development Program of China under Grant No. 2016YFB1000201, the National Natural Science Foundation of China under Grant Nos. 61420106013 and 61702480, and the Youth Innovation Promotion Association of Chinese Academy of Sciences and Alibaba Innovative Research (AIR) Program.

Both resource efficiency and application QoS have been big concerns of datacenter operators for a long time, but remain to be irreconcilable. High resource utilization increases the risk of resource contention between co-located workload, which makes latency-critical (LC) applications suffer unpredictable, and even unacceptable performance. Plenty of prior work devotes the effort on exploiting effective mechanisms to protect the QoS of LC applications while improving resource efficiency. In this paper, we propose MAGI, a resource management runtime that leverages neural networks to monitor and further pinpoint the root cause of performance interference, and adjusts resource shares of corresponding applications to ensure the QoS of LC applications. MAGI is a practice in Alibaba datacenter to provide on-demand resource adjustment for applications using neural networks. The experimental results show that MAGI could reduce up to 87.3% performance degradation of LC application when co-located with other antagonist applications.

Key words: resource management, neural network, resource efficiency, tail latency

[1] Reiss C, Tumanov A, Ganger G R, Katz R H, Kozuch M A. Heterogeneity and dynamicity of clouds at scale:Google trace analysis. In Proc. the 3rd ACM Symposium on Cloud Computing, October 2012, Article No. 7.
[2] Liu H. A measurement study of server utilization in public clouds. In Proc. the 9th IEEE International Conference on Dependable, Autonomic and Secure Computing, December 2011, pp.435-442.
[3] Delimitrou C, Kozyrakis C. Quasar:Resource-efficient and QoS-aware cluster management. ACM SIGPLAN Notices, 2014, 49(4):127-144.
[4] Cortez E, Bonde A, Muzio A, Russinovich M, Fontoura M, Bianchini R. Resource central:Understanding and predicting workloads for improved resource management in large cloud platforms. In Proc. the 26th Symposium on Operating Systems Principles, October 2017, pp.153-167.
[5] Lo D, Cheng L Q, Govindaraju R, Ranganathan P, Kozyrakis C. Heracles:Improving resource efficiency at scale. ACM SIGARCH Computer Architecture News, 2015, 43:450-462.
[6] Chen S, Delimitrou C, Martínez J F. PARTIES:QoS-aware resource partitioning for multiple interactive services. In Proc. the 24th International Conference on Architectural Support for Programming Languages and Operating Systems, April 2019, pp.107-120.
[7] Zhuravlev S, Blagodurov S, Fedorova A. Addressing shared resource contention in multicore processors via scheduling. ACM SIGPLAN Notices, 2010, 45:129-142.
[8] Zhang X, Tune E, Hagmann R et al. CPI2:CPU performance isolation for shared compute clusters. In Proc. the 8th ACM European Conference on Computer Systems, April 2013, pp.379-391.
[9] Yasin A. A top-down method for performance analysis and counters architecture. In Proc. the 2014 IEEE International Symposium on Performance Analysis of Systems and Software, March 2014, pp.35-44.
[10] Kasture H, Sanchez D. Tailbench:A benchmark suite and evaluation methodology for latency-critical applications. In Proc. the 2016 IEEE International Symposium on Workload Characterization, September 2016, pp.3-12.
[11] Henning J L. SPEC CPU2006 benchmark descriptions. SIGARCH Comput. Archit. News, 2006, 34(4):1-17.
[12] Verma A, Pedrosa L, Korupolu M, Oppenheimer D, Tune E, Wilkes J. Large-scale cluster management at Google with Borg. In Proc. the 10th European Conference on Computer Systems, April 2015, Article No. 18.
[13] Hindman B, Konwinski A, Zaharia M, Ghodsi A, Joseph A D, Katz R H, Shenker S, Stoica I. Mesos:A platform for fine-grained resource sharing in the data center. In Proc. the 8th USENIX Symposium on Networked Systems Design and Implementation, March 2011, Article No. 4.
[14] Schwarzkopf M, Konwinski A, Abd-El-Malek M, Wilkes J. Omega:Flexible, scalable schedulers for large compute clusters. In Proc. the 8th ACM European Conference on Computer Systems, April 2013, pp.351-364.
[15] Ousterhout K, Wendell P, Zaharia M, Stoica I. Sparrow:Distributed, low latency scheduling. In Proc. the 24th ACM Symposium on Operating Systems Principles, November 2013, pp.69-84.
[16] Zhang Z, Li C, Tao Y Y, Yang R Y, Tang H, Xu J. Fuxi:A fault-tolerant resource management and job scheduling system at Internet scale. Proceedings of the VLDB Endowment, 2014, 7(13):1393-1404.
[17] Guo J, Chang Z H, Wang S, Ding H Y, Feng Y H, Mao L, Bao Y G. Who limits the resource efficiency of my datacenter:An analysis of Alibaba datacenter traces. In Proc. the International Symposium on Quality of Service, June 2019, Article No. 39.
[18] Herdrich A, Verplanke E, Autee P, Illikkal R, Gianos C, Singhal R, Iyer R. Cache QoS:From concept to reality in the intelr Xeonr processor E5-2600 v3 product family. In Proc. the 2016 IEEE International Symposium on High Performance Computer Architecture, March 2016, pp.657-668.
[1] Wen-Li Zhang, Ke Liu, Yi-Fan Shen, Ya-Zhu Lan, Hui Song, Ming-Yu Chen, Yuan-Fei Chen. Labeled Network Stack: A High-Concurrency and Low-Tail Latency Cloud Server Framework for Massive IoT Devices [J]. Journal of Computer Science and Technology, 2020, 35(1): 179-193.
[2] Xing-Gang Wang, Jia-Si Wang, Peng Tang, Wen-Yu Liu. Weakly- and Semi-Supervised Fast Region-Based CNN for Object Detection [J]. Journal of Computer Science and Technology, 2019, 34(6): 1269-1278.
[3] Xin Yang, Dawei Wang, Wenbo Hu, Li-Jing Zhao, Bao-Cai Yin, Qiang Zhang, Xiao-Peng Wei, Hongbo Fu. DEMC: A Deep Dual-Encoder Network for Denoising Monte Carlo Rendering [J]. Journal of Computer Science and Technology, 2019, 34(5): 1123-1135.
[4] Robail Yasrab. SRNET: A Shallow Skip Connection Based Convolutional Neural Network Design for Resolving Singularities [J]. Journal of Computer Science and Technology, 2019, 34(4): 924-938.
[5] Ri-Sheng Liu, Cai-Sheng Mao, Zhi-Hui Wang, Hao-Jie Li. Blind Image Deblurring via Adaptive Optimization with Flexible Sparse Structure Control [J]. Journal of Computer Science and Technology, 2019, 34(3): 609-621.
[6] Han Liu, Hang Du, Dan Zeng, Qi Tian. Cloud Detection Using Super Pixel Classification and Semantic Segmentation [J]. Journal of Computer Science and Technology, 2019, 34(3): 622-633.
[7] Dong-Di Zhao, Fan Li, Kashif Sharif, Guang-Min Xia, Yu Wang. Space Efficient Quantization for Deep Convolutional Neural Networks [J]. Journal of Computer Science and Technology, 2019, 34(2): 305-317.
[8] Feng Zhou, Hao-Min Zhou, Zhi-Hua Yang, Li-Hua Yang. A 2-Stage Strategy for Non-Stationary Signal Prediction and Recovery Using Iterative Filtering and Neural Network [J]. Journal of Computer Science and Technology, 2019, 34(2): 318-338.
[9] Tie-Ke He, Hao Lian, Ze-Min Qin, Zhen-Yu Chen, Bin Luo. PTM: A Topic Model for the Inferring of the Penalty [J]. , 2018, 33(4): 756-767.
[10] Bei-Ji Zou, Yun-Di Guo, Qi He, Ping-Bo Ouyang, Ke Liu, Zai-Liang Chen. 3D Filtering by Block Matching and Convolutional Neural Network for Image Denoising [J]. , 2018, 33(4): 838-848.
[11] Nai-Ming Yao, Hui Chen, Qing-Pei Guo, Hong-An Wang. Non-Frontal Facial Expression Recognition Using a Depth-Patch Based Deep Neural Network [J]. , 2017, 32(6): 1172-1185.
[12] Wei-Qing, Liu Jing Li. An Approach to Automatic Performance Prediction for Cloud-enhanced Mobile Applications with Sparse Data [J]. , 2017, 32(5): 936-956.
[13] Shu-Chang Zhou, Yu-Zhi Wang, He Wen, Qin-Yao He, Yu-Heng Zou. Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks [J]. , 2017, 32(4): 667-682.
[14] Xiang Bai, Zheng Zhang, Hong-Yang Wang, Wei Shen. Directional Edge Boxes: Exploiting Inner Normal Direction Cues for Effective Object Proposal Generation [J]. , 2017, 32(4): 701-713.
[15] Ai-Wen Jiang, Bo Liu, Ming-Wen Wang. Deep Multimodal Reinforcement Network with Contextually Guided Recurrent Attention for Image Question Answering [J]. , 2017, 32(4): 738-748.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] Li Wei;. A Structural Operational Semantics for an Edison Like Language(2)[J]. , 1986, 1(2): 42 -53 .
[2] Chen Shihua;. On the Structure of Finite Automata of Which M Is an(Weak)Inverse with Delay τ[J]. , 1986, 1(2): 54 -59 .
[3] Feng Yulin;. Recursive Implementation of VLSI Circuits[J]. , 1986, 1(2): 72 -82 .
[4] Chen Shihua;. On the Structure of (Weak) Inverses of an (Weakly) Invertible Finite Automaton[J]. , 1986, 1(3): 92 -100 .
[5] Zhang Cui; Zhao Qinping; Xu Jiafu;. Kernel Language KLND[J]. , 1986, 1(3): 65 -79 .
[6] Qu Yanwen;. AGDL: A Definition Language for Attribute Grammars[J]. , 1986, 1(3): 80 -91 .
[7] Huang Heyan;. A Parallel Implementation Model of HPARLOG[J]. , 1986, 1(4): 27 -38 .
[8] Zheng Guoliang; Li Hui;. The Design and Implementation of the Syntax-Directed Editor Generator(SEG)[J]. , 1986, 1(4): 39 -48 .
[9] Shen Li; Stephen Y.H.Su;. Generalized Parallel Signature Analyzers with External Exclusive-OR Gates[J]. , 1986, 1(4): 49 -61 .
[10] Min Yinghua; Han Zhide;. A Built-in Test Pattern Generator[J]. , 1986, 1(4): 62 -74 .

ISSN 1000-9000(Print)

         1860-4749(Online)
CN 11-2296/TP

Home
Editorial Board
Author Guidelines
Subscription
Journal of Computer Science and Technology
Institute of Computing Technology, Chinese Academy of Sciences
P.O. Box 2704, Beijing 100190 P.R. China
Tel.:86-10-62610746
E-mail: jcst@ict.ac.cn
 
  Copyright ©2015 JCST, All Rights Reserved