A novel deadline and budget constrained scheduling heuristics for computational grids

The conventional deadline and budget constrained (DBC) scheduling heuristics for economic-based computational grids does not take the inconsistency of grid heterogeneity into account, which can lead to decline of application completion ratios. Motivated by this fact, a novel DBC scheduling heuristics was proposed to deal with sequential workflow applications. In order to valuate the inconsistency, the relative cost (RC) metric was introduced, which was used to indicate the task-starving degree for resources. The new algorithm assigns tasks to resources, considering completion time, budget and RC together. The GridSim toolkit and the benchmark suites of the standard performance evaluation corporation (SPEC) were used to simulate the heterogeneous grid environment and applications. The experimental results show that the task and workflow completion ratios of the new heuristics are higher than those of the conventional heuristics.

[1]  Rajkumar Buyya,et al.  Scheduling Parallel Applications on Utility Grids: Time and Cost Trade-off Management , 2009, ACSC.

[2]  Ashish Sharma,et al.  Dynamic mapping in a heterogeneous environment with tasks having priorities and multiple deadlines , 2003, Proceedings International Parallel and Distributed Processing Symposium.

[3]  Ian Foster,et al.  The Grid 2 - Blueprint for a New Computing Infrastructure, Second Edition , 1998, The Grid 2, 2nd Edition.

[4]  Xingfu Wu,et al.  Performance Prediction-based versus Load-based Site Selection: Quantifying the Difference , 2005, ISCA PDCS.

[5]  Rajkumar Buyya,et al.  GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for Grid computing , 2002, Concurr. Comput. Pract. Exp..

[6]  David Abramson,et al.  Economic models for resource management and scheduling in Grid computing , 2002, Concurr. Comput. Pract. Exp..

[7]  Carl Kesselman,et al.  A provisioning model and its comparison with best-effort for performance-cost optimization in grids , 2007, HPDC '07.

[8]  David Fernández-Baca,et al.  Allocating Modules to Processors in a Distributed System , 1989, IEEE Trans. Software Eng..

[9]  Shashank Shetty,et al.  A Survey of Market-Based Approaches to Distributed Computing , 2003 .

[10]  Robert Kyle Armstrong,et al.  Investigation of effect of different run-time distributions on SmartNet performance , 1997 .

[11]  Rajkumar Buyya,et al.  A Deadline and Budget Constrained Scheduling Algorithm for eScience Applications on Data Grids , 2005, ICA3PP.

[12]  Guanghua Song,et al.  A Deadline and Budget Constrained Cost-Time Optimization Algorithm for Scheduling Dependent Tasks in Grid Computing , 2003, GCC.

[13]  Ladislau Bölöni,et al.  A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems , 2001, J. Parallel Distributed Comput..

[14]  Kavitha Ranganathan,et al.  Simulation Studies of Computation and Data Scheduling Algorithms for Data Grids , 2003, Journal of Grid Computing.

[15]  Ami Marowka,et al.  The GRID: Blueprint for a New Computing Infrastructure , 2000, Parallel Distributed Comput. Pract..

[16]  Radu Prodan,et al.  Bi-criteria Scheduling of Scientific Workflows for the Grid , 2008, 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid (CCGRID).

[17]  Ian T. Foster.  Globus Toolkit Version 4: Software for Service-Oriented Systems , 2005, NPC.

[18]  Ronald H. Perrott,et al.  Bioinformatics Application Integration and Management in GeneGrid: Experiments and Experiences , 2004 .

[19]  David Abramson,et al.  Scheduling parameter sweep applications on global Grids: a deadline and budget constrained cost–time optimization algorithm , 2005, Softw. Pract. Exp..