Task differentiation in Polistes wasp colonies: a model for self-organizing groups of robots
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Evaluation of Job-Scheduling Strategies for Grid Computing
GRID '00 Proceedings of the First IEEE/ACM International Workshop on Grid Computing
A Benefit Function Mapping Heuristic for a Class of Meta-Tasks in Grid Environments
CCGRID '01 Proceedings of the 1st International Symposium on Cluster Computing and the Grid
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
CGA: Chaotic Genetic Algorithm for Fuzzy Job Scheduling in Grid Environment
Computational Intelligence and Security
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Task scheduling is one of the bottlenecks in realizing grid computing. We introduce swarm intelligence into task scheduling in a grid environment, and propose a new dynamic task-scheduling algorithm. This algorithm schedules effectively a group of independent tasks based on the interaction model between a wasp colony and its environment. We also present an effective method, using the self-organized dominance hierarchy of wasp colony to solve the dominance struggle problem that occurs in the proposed algorithm. Our evaluation results show that the proposed algorithm is more efficient and more adaptive to the dynamic grid environment than other task-scheduling algorithms.