Time and cost trade-off management for scheduling parallel applications on Utility Grids

被引:77
作者
Garg, Saurabh Kumar [1 ]
Buyya, Rajkumar [1 ]
Siegel, Howard Jay [2 ,3 ]
机构
[1] Univ Melbourne, Grid Comp & Distributed Syst Lab, Dept Comp Sci & Software Engn, Melbourne, Vic 3010, Australia
[2] Colorado State Univ, Dept Elect & Comp Engn, Ft Collins, CO 80523 USA
[3] Colorado State Univ, Dept Comp Sci, Ft Collins, CO 80523 USA
来源
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF GRID COMPUTING-THEORY METHODS AND APPLICATIONS | 2010年 / 26卷 / 08期
基金
美国国家科学基金会;
关键词
Grid computing; Meta-scheduling; Market-oriented; Resource management; INDEPENDENT TASKS; COMPUTING SYSTEMS; JOBS; ALGORITHM;
D O I
10.1016/j.future.2009.07.003
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
With the growth of Utility Grids and various Grid market infrastructures, the need for efficient and cost effective scheduling algorithms is also increasing rapidly, particularly in the area of meta-scheduling. In these environments, users not only may have conflicting requirements with other users, but also they have to manage the trade-off between time and cost such that their applications can be executed most economically in the minimum time. Thus, selection of the best Grid resources becomes a challenge in such a competitive environment. This paper presents three novel heuristics for scheduling parallel applications on Utility Grids that manage and optimize the trade-off between time and cost constraints. The performance of the heuristics is evaluated through extensive simulations of a real-world environment with real parallel workload models to demonstrate the practicality of our algorithms. We compare our scheduling algorithms against existing common meta-schedulers experimentally. The results show that our algorithms outperform existing algorithms by minimizing the time and cost of application execution on Utility Grids. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码:1344 / 1355
页数:12
相关论文
共 37 条
  • [1] A computational economy for grid computing and its implementation in the Nimrod-G resource broker
    Abramson, D
    Buyya, R
    Giddy, J
    [J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2002, 18 (08): : 1061 - 1074
  • [2] Ali S., 2000, Tamkang Journal of Applied Science and Engineering, V3, P195
  • [3] Altmann J., 2007, P 4 INT WORKSH GRID
  • [4] [Anonymous], 2004, The Grid: Blueprint for a New Computing Infrastructure
  • [5] [Anonymous], P 15 IEEE INT PAR DI
  • [6] [Anonymous], P 1 INT C E SCI GRID
  • [7] A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems
    Braun, TD
    Siegel, HJ
    Beck, N
    Bölöni, LL
    Maheswaran, M
    Reuther, AI
    Robertson, JP
    Theys, MD
    Yao, B
    Hensgen, D
    Freund, RF
    [J]. JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, 2001, 61 (06) : 810 - 837
  • [8] Utility computing SLA management based upon business objectives
    Buco, MJ
    Chang, RN
    Luan, LZ
    Ward, C
    Wolf, JL
    Yu, PS
    [J]. IBM SYSTEMS JOURNAL, 2004, 43 (01) : 159 - 178
  • [9] Scheduling parameter sweep applications on global Grids: a deadline and budget constrained cost-time optimization algorithm
    Buyya, R
    Murshed, M
    Abramson, D
    Venugopal, S
    [J]. SOFTWARE-PRACTICE & EXPERIENCE, 2005, 35 (05) : 491 - 512
  • [10] GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for Grid computing
    Buyya, R
    Murshed, M
    [J]. CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2002, 14 (13-15) : 1175 - 1220