Tails in the cloud: a survey and taxonomy of straggler management within large-scale cloud data centres

被引:17
|
作者
Gill, Sukhpal Singh [1 ]
Ouyang, Xue [2 ]
Garraghan, Peter [3 ]
机构
[1] Queen Mary Univ London, Sch Elect Engn & Comp Sci, London, England
[2] Natl Univ Def Technol, Sch Elect Sci, Changsha, Peoples R China
[3] Univ Lancaster, Sch Comp & Commun, Lancaster, England
来源
JOURNAL OF SUPERCOMPUTING | 2020年 / 76卷 / 12期
基金
英国工程与自然科学研究理事会;
关键词
Computing; Stragglers; Cloud computing; Straggler management; Distributed systems; Cloud data centres;
D O I
10.1007/s11227-020-03241-x
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Cloud computing systems are splitting compute- and data-intensive jobs into smaller tasks to execute them in a parallel manner using clusters to improve execution time. However, such systems at increasing scale are exposed to stragglers, whereby abnormally slow running tasks executing within a job substantially affect job performance completion. Such stragglers are a direct threat towards attaining fast execution of data-intensive jobs within cloud computing. Researchers have proposed an assortment of different mechanisms, frameworks, and management techniques to detect and mitigate stragglers both proactively and reactively. In this paper, we present a comprehensive review of straggler management techniques within large-scale cloud data centres. We provide a detailed taxonomy of straggler causes, as well as proposed management and mitigation techniques based on straggler characteristics and properties. From this systematic review, we outline several outstanding challenges and potential directions of possible future work for straggler research.
引用
收藏
页码:10050 / 10089
页数:40
相关论文
共 50 条
  • [31] Searching for Optimal Configurations Within Large-Scale Models: A Cloud Computing Domain
    Ochoa, Lina
    Gonzalez-Rojas, Oscar
    Verano, Mauricio
    Castro, Harold
    ADVANCES IN CONCEPTUAL MODELING, ER 2016 WORKSHOPS, 2016, 9975 : 65 - 75
  • [32] A Large-scale Survey of CO and Its Isotopologues toward the Rosette Molecular Cloud
    Li, Chong
    Wang, Hongchi
    Zhang, Miaomiao
    Ma, Yuehui
    Fang, Min
    Yang, Ji
    ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES, 2018, 238 (01):
  • [34] Enabling large-scale ligand discovery on the cloud
    Hawkins, Paul
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2016, 251
  • [35] Anomaly Detection in a Large-scale Cloud Platform
    Islam, Mohammad S.
    Pourmajidi, William
    Zhang, Lei
    Steinbacher, John
    Erwin, Tony
    Miranskyy, Andriy
    2021 IEEE/ACM 43RD INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: SOFTWARE ENGINEERING IN PRACTICE (ICSE-SEIP 2021), 2021, : 150 - 159
  • [36] The Application of Cloud Computing in Large-Scale Statistic
    Sun Xiuli
    Li Ying
    Hu Baofang
    Sun Hongfeng
    PROCEEDINGS OF THE 1ST INTERNATIONAL WORKSHOP ON CLOUD COMPUTING AND INFORMATION SECURITY (CCIS 2013), 2013, 52 : 308 - 311
  • [37] LARGE-SCALE CONVERGENCE IN A NUMERICAL CLOUD MODEL
    CHANG, SW
    ORVILLE, HD
    JOURNAL OF THE ATMOSPHERIC SCIENCES, 1973, 30 (05) : 947 - 950
  • [38] A Large-scale Analysis of Cloud Service Abuse
    Fukushi, Naoki
    Chiba, Daiki
    Akiyama, Mitsuaki
    Uchida, Masato
    2020 IEEE CONFERENCE ON COMMUNICATIONS AND NETWORK SECURITY (CNS), 2020,
  • [39] Enabling Large-Scale Biomedical Analysis in the Cloud
    Lin, Ying-Chih
    Yu, Chin-Sheng
    Lin, Yen-Jen
    BIOMED RESEARCH INTERNATIONAL, 2013, 2013
  • [40] Robust Large-Scale Machine Learning in the Cloud
    Rendle, Steffen
    Fetterly, Dennis
    Shekita, Eugene J.
    Su, Bor-yiing
    KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2016, : 1125 - 1134