A trust model-based task scheduling algorithm for data-intensive application

被引:2
|
作者
Xu Y. [1 ]
Qu W. [1 ]
机构
[1] College of Information Science and Technology, Dalian Maritime University, Dalian
关键词
data-intensive; Min-Min; task scheduling; trust;
D O I
10.1109/ChinaGrid.2011.16
中图分类号
学科分类号
摘要
With the increase of data-intensive application, the amount of data that the task requires becomes much larger and the task scheduling performance is greatly affected by the data transfer overhead. In grid, establishing trust model is considered to be an important measure of improving the grid security. Therefore, considering the two problems above, this paper improves the Min-Min algorithm and proposes trust model-based Min-Min scheduling algorithm. This algorithm consists of three phases: data file selecting, task scheduling and data scheduling. Two salient features of this algorithm are: 1) in selecting task-required data files, it considers file server's trust degree and data transmission time, it selects the data file with bigger trust value and smaller data transmission time, 2) in data transmission time calculating and transmission path selecting, it adopts the shortest path algorithm-Dijkstra. The experiment results show that although this scheduling algorithm extends the task completion time, the success rate of task execution is apparently raised. © 2011 IEEE.
引用
下载
收藏
页码:227 / 233
页数:6
相关论文
共 50 条
  • [41] Algorithms for Divisible Load Scheduling of Data-intensive Applications
    Chen Yu
    Dan C. Marinescu
    Journal of Grid Computing, 2010, 8 : 133 - 155
  • [42] Data classification algorithm for data-intensive computing environments
    Chen, Tiedong
    Liu, Shifeng
    Gong, Daqing
    Gao, Honghu
    EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING, 2017,
  • [43] Data classification algorithm for data-intensive computing environments
    Tiedong Chen
    Shifeng Liu
    Daqing Gong
    Honghu Gao
    EURASIP Journal on Wireless Communications and Networking, 2017
  • [44] Scheduling Data-Intensive Scientific Workflows with Reduced Communication
    Pietri, Ilia
    Sakellariou, Rizos
    30TH INTERNATIONAL CONFERENCE ON SCIENTIFIC AND STATISTICAL DATABASE MANAGEMENT (SSDBM 2018), 2018,
  • [45] Transfer scheduling schemes for data-intensive, interactive applications
    Takizawa, Makoto
    Shimizu, Takashi
    Ishida, Osamu
    GLOBECOM 2007: 2007 IEEE GLOBAL TELECOMMUNICATIONS CONFERENCE, VOLS 1-11, 2007, : 2488 - 2491
  • [46] Dynamic Scheduling Approach for Data-Intensive Cloud Environment
    Islam, Md. Rafiqul
    Habiba, Mansura
    2012 INTERNATIONAL CONFERENCE ON CLOUD COMPUTING TECHNOLOGIES, APPLICATIONS AND MANAGEMENT (ICCCTAM), 2012, : 179 - 185
  • [47] Heuristic-based scheduling to maximize throughput of data-intensive grid applications
    Ray, S
    Zhang, Z
    DISTRIBUTED COMPUTING - IWDC 2004, PROCEEDINGS, 2004, 3326 : 63 - 74
  • [48] TailGuard: Tail Latency SLO Guaranteed Task Scheduling for Data-Intensive User-Facing Applications
    Wang, Zhijun
    Li, Huiyang
    Sun, Lin
    Rosenkrantz, Todd
    Che, Hao
    Jiang, Hong
    2023 IEEE 43RD INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS, ICDCS, 2023, : 898 - 909
  • [49] A Trust Evaluation Mechanism for Collaboration of Data-Intensive Services in Cloud
    Huang, Longtao
    Deng, Shuiguang
    Li, Ying
    Wu, Jian
    Yin, Jianwei
    Li, Gexin
    APPLIED MATHEMATICS & INFORMATION SCIENCES, 2013, 7 : 121 - 129
  • [50] PARROT: AN APPLICATION ENVIRONMENT FOR DATA-INTENSIVE COMPUTING
    Thain, Douglas
    Livny, Miron
    SCALABLE COMPUTING-PRACTICE AND EXPERIENCE, 2005, 6 (03): : 9 - 18