Intelligent strategies for DAG scheduling optimization in Grid environments

TL;DR

This paper introduces a distributed, scalable, and fault-tolerant DAG scheduling algorithm for Grid environments, utilizing heuristics and genetic algorithms to optimize task and resource assignment, showing superior performance in experiments.

Contribution

It proposes a novel combination of heuristic and genetic algorithm-based methods for dynamic DAG scheduling in Grid environments, enhancing efficiency and fault tolerance.

Findings

Demonstrates improved scheduling efficiency over existing methods

Validates approach using MonALISA monitoring environment

Shows robustness and scalability in experiments

Abstract

The paper presents a solution to the dynamic DAG scheduling problem in Grid environments. It presents a distributed, scalable, efficient and fault-tolerant algorithm for optimizing tasks assignment. The scheduler algorithm for tasks with dependencies uses a heuristic model to optimize the total cost of tasks execution. Also, a method based on genetic algorithms is proposed to optimize the procedure of resources assignment. The experiments used the MonALISA monitoring environment and its extensions. The results demonstrate very good behavior in comparison with other scheduling approaches for this kind of DAG scheduling algorithms.