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

作　　者：Sa Wang Yan-Hai Zhu Shan-Pei Chen Tian-Ze Wu Wen-Jie Li Xu-Sheng Zhan Hai-Yang Ding Wei-Song Shi Yun-Gang Bao 

机构地区：[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

出　　处：《Journal of Computer Science & Technology》2020年第1期209-220,共12页计算机科学技术学报（英文版）

基　　金：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;the Youth Innovation Promotion Association of Chinese Academy of Sciences and Alibaba Innovative Research(AIR)Program.

摘　　要：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.

关 键 词：RESOURCE management NEURAL network RESOURCE efficiency TAIL LATENCY 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP31[自动化与计算机技术—控制科学与工程]