Allocating Modules to Processors in a Distributed System
IEEE Transactions on Software Engineering
The ant colony optimization meta-heuristic
New ideas in optimization
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
Performance Modeling and Prediction of Nondedicated Network Computing
IEEE Transactions on Computers
IEEE Transactions on Parallel and Distributed Systems
Scheduling Resources in Multi-User, Heterogeneous, Computing Environments with SmartNet
HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
Dynamic Matching and Scheduling of a Class of Independent Tasks onto Heterogeneous Computing Systems
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
SRDS '98 Proceedings of the The 17th IEEE Symposium on Reliable Distributed Systems
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant Colony Inspired Microeconomic Based Resource Management in Ad Hoc Grids
GPC '09 Proceedings of the 4th International Conference on Advances in Grid and Pervasive Computing
Grid jobs scheduling: The Alienated Ant Algorithm solution
Multiagent and Grid Systems
Hybrid enhanced ant colony algorithm and enhanced bee colony algorithm for grid scheduling
International Journal of Grid and Utility Computing
A bio-inspired distributed algorithm to improve scheduling performance of multi-broker grids
Natural Computing: an international journal
Advances in Engineering Software
Balanced Job Scheduling Based on Ant Algorithm for Grid Network
International Journal of Grid and High Performance Computing
Service vulnerability scanning based on service-oriented architecture in Web service environments
Journal of Systems Architecture: the EUROMICRO Journal
Software Survey: Distributed job scheduling based on Swarm Intelligence: A survey
Computers and Electrical Engineering
QoS based resource provisioning and scheduling in grids
The Journal of Supercomputing
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Grid computing is a form of distributed computing that involves coordinating and sharing computing, application, data storage or network resources across dynamic and geographically dispersed organizations. The goal of grid task scheduling is to achieve high system throughput and to match the application needed with the available computing resources. This is matching of resources in a non-deterministically shared heterogeneous environment. The complexity of scheduling problem increases with the size of the grid and becomes highly difficult to solve effectively. To obtain good methods to solve this problem a new area of research is implemented. This area is based on developed heuristic techniques that provide an optimal or near optimal solution for large grids. In this paper we introduce a tasks scheduling algorithm for grid computing. The algorithm is based on Ant Colony Optimization (ACO) which is a Monte Carlo method. The paper shows how to search for the best tasks scheduling for grid computing.