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Iterative-Improvement-Based Heuristics for Adaptive Scheduling of Tasks Sharing Files on

Summary: Iterative-Improvement-Based Heuristics for
Adaptive Scheduling of Tasks Sharing Files on
Heterogeneous Master-Slave Environments
Kamer Kaya and Cevdet Aykanat, Member, IEEE Computer Society
Abstract--The scheduling of independent but file-sharing tasks on heterogeneous master-slave platforms has recently found
important applications in Grid environments. The scheduling heuristics recently proposed for this problem are all constructive in nature
and based on a common greedy criterion which depends on the momentary completion time values of the tasks. We show that this
greedy decision criterion has shortcomings in exploiting the file-sharing interaction among tasks since completion time values are
inadequate to extract the global view of this interaction. We propose a three-phase scheduling approach which involves initial task
assignment, refinement, and execution ordering phases. For the refinement phase, we model the target application as a hypergraph
and, with an elegant hypergraph-partitioning-like formulation, we propose using iterative-improvement-based heuristics for refining the
task assignments according to two novel objective functions. Unlike the turnaround time, which is the actual schedule cost, the
smoothness of proposed objective functions enables the use of iterative-improvement-based heuristics successfully since their
effectiveness and efficiency depend on the smoothness of the objective function. Experimental results on a wide range of synthetically
generated heterogeneous master-slave frameworks show that the proposed three-phase scheduling approach performs much better
than the greedy constructive approach.
Index Terms--Scheduling, file-sharing tasks, heterogeneous master-slave platform, grid computing, iterative improvement.

IN this work, we investigate the scheduling of independent


Source: Aykanat, Cevdet - Department of Computer Engineering, Bilkent University


Collections: Computer Technologies and Information Sciences