[1] LEFF A, RAYFIELD J T, DIAS D M. Service-level agreements and commercial grids [J]. IEEE Internet Computing, 2003, 7(4): 44-50. [2] GONG L, SUN X, WASTON E. Performance modeling and prediction of non-dedicated network computing [J]. IEEE Transactions on Computers, 2002, 51(9): 1041-1055. [3] KIRAN M, HASHIM A H A, KUAN L M, et al. Execution time prediction of imperative paradigm tasks for grid scheduling optimization [J]. International Journal of Computer Science and Network Security, 2009, 9(2): 155-163. [4] PHINJAROENPHAN P, BEVINAKOPPA S, ZEEPHONGSEKUL P. A method for estimating the execution time of a parallel task on a grid node [C]//EGC 2005: Proceedings of the 2005 European Grid Conference on Advances in Grid Computing, LNCS 3470. Berlin: Springer, 2005: 226-236. [5] SADJADI S M, SHIMIZU S, FIGUEROA J, et al. A modeling approach for estimating execution time of long-running scientific applications [C]//IPDPS 2008: Proceedings of the 22nd IEEE International Symposium on Parallel and Distributed Processing. Piscataway: IEEE, 2008: 1-8. [6] DUAN R, NADEEM F, WANG J, et al. A hybrid intelligent method for performance modeling and prediction of workflow activities in grids [C]//Proceedings of the 9th IEEE/ACM International Symposium on Cluster Computing and the Grid. Piscataway: IEEE, 2009: 339-347. [7] NADEEM F, FAHRINGER T. Using templates to predict execution time of scientific workflow applications in the grid [C]//Proceedings of the 9th IEEE/ACM International Symposium on Cluster Computing and the Grid. Piscataway: IEEE, 2009: 316-323. [8] LI A, QIN Z. Moving windows quadratic autoregressive model for predicting nonlinear time series [J]. Chinese Journal of Computers, 2004, 27(7): 1004-1008. (李爱国,覃征.滑动窗口二次自回归模型预测非线性时间序列[J].计算机学报,2004,27(7):1004-1008.) [9] JIANG Y. Research of task execution time prediction technology in grid computing environments [J]. Computer Engineering and Design, 2011, 32(10): 3428-3430. (蒋炎华.网格环境下任务的执行时间预测技术研究[J].计算机工程与设计,2011,32(10):3428-3430.) [10] TAO M, DONG S, ZHANG L. A multi-strategy collaborative prediction model for the runtime of online tasks in computing cluster/grid [J]. Cluster Computing, 2011, 14(2): 199-210. [11] GROSS D, SHORTLE J F, THOMPSON J M, et al. Fundamentals of queuing theory [M]. New York: Wiley, 1998: 68-82. [12] DEKKING F M, KRAAIKAMP C, LOPUHAA H P, et al. A modern introduction to probability and statistics: understanding why and how [M]. Berlin: Springer, 2005: 103-114. [13] WOLSKI R. Experiences with predicting resource performance online in computational grid settings [J]. SIGMETRICS Performance Evaluation Review, 2003, 30(4): 41-49. [14] BROCKWELL P J, DAVIS R A. Introduction to time series and forecasting [M]. Berlin: Springer, 2002: 317-330. [15] CIRNE W, BRASILEIRO F, PARANHOS D, et al. On the efficacy, efficiency and emergent behavior of task replication in large distributed systems [J]. Parallel Computing, 2007, 33(3): 213-234. [16] BUYYA R, MURSHED M. GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing [J]. Concurrency and Computation: Practice and Experience, 2002, 14(13/14/15): 1175-1220. |