Future Generation Computer Systems
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
An ant algorithm for balanced job scheduling in grids
Future Generation Computer Systems
An Improved PSO Algorithm and its Application to Grid Scheduling Problem
ISCSCT '08 Proceedings of the 2008 International Symposium on Computer Science and Computational Technology - Volume 01
A discrete particle swarm optimization algorithm for scheduling parallel machines
Computers and Industrial Engineering
Computers and Electrical Engineering
Computational models and heuristic methods for Grid scheduling problems
Future Generation Computer Systems
AINA '10 Proceedings of the 2010 24th IEEE International Conference on Advanced Information Networking and Applications
Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm
Future Generation Computer Systems
ICMA '10 Proceedings of the 2010 International Conference on Manufacturing Automation
Microprocessors & Microsystems
Swarm Intelligence Approaches for Grid Load Balancing
Journal of Grid Computing
A survey on parallel ant colony optimization
Applied Soft Computing
Expert Systems with Applications: An International Journal
High Performance Computing: From Grids and Clouds to Exascale Volume 20 Advances in Parallel Computing
Ant algorithm for grid scheduling problem
LSSC'05 Proceedings of the 5th international conference on Large-Scale Scientific Computing
Engineering Applications of Artificial Intelligence
Advances in Engineering Software
Honey bee behavior inspired load balancing of tasks in cloud computing environments
Applied Soft Computing
Hi-index | 0.00 |
Scientists and engineers need computational power to satisfy the increasing resource intensive nature of their simulations. For example, running Parameter Sweep Experiments (PSE) involve processing many independent jobs, given by multiple initial configurations (input parameter values) against the same program code. Hence, paradigms like Grid Computing and Cloud Computing are employed for gaining scalability. However, job scheduling in Grid and Cloud environments represents a difficult issue since it is basically NP-complete. Thus, many variants based on approximation techniques, specially those from Swarm Intelligence (SI), have been proposed. These techniques have the ability of searching for problem solutions in a very efficient way. This paper surveys SI-based job scheduling algorithms for bag-of-tasks applications (such as PSEs) on distributed computing environments, and uniformly compares them based on a derived comparison framework. We also discuss open problems and future research in the area.