Observations on Using Genetic Algorithms for Dynamic Load-Balancing
IEEE Transactions on Parallel and Distributed Systems
Journal of Parallel and Distributed Computing
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Experiments with Scheduling Using Simulated Annealing in a Grid Environment
GRID '02 Proceedings of the Third International Workshop on Grid Computing
Workload Evolution on the Cornell Theory Center IBM SP2
IPPS '96 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
Task Execution Time Modeling for Heterogeneous Computing Systems
HCW '00 Proceedings of the 9th Heterogeneous Computing Workshop
Efficient Hierarchical Parallel Genetic Algorithms using Grid computing
Future Generation Computer Systems
Batch mode scheduling in grid systems
International Journal of Web and Grid Services
Scheduling Algorithms
A parallel solution for scheduling of real time applications on grid environments
Future Generation Computer Systems
A GA(TS) Hybrid Algorithm for Scheduling in Computational Grids
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Computational models and heuristic methods for Grid scheduling problems
Future Generation Computer Systems
Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm
Future Generation Computer Systems
Computers & Mathematics with Applications
Information Sciences: an International Journal
An agent-based model of hierarchic genetic search
Computers & Mathematics with Applications
Bacterial foraging based hyper-heuristic for resource scheduling in grid computing
Future Generation Computer Systems
Hierarchical genetic-based grid scheduling with energy optimization
Cluster Computing
Security, energy, and performance-aware resource allocation mechanisms for computational grids
Future Generation Computer Systems
QoS based resource provisioning and scheduling in grids
The Journal of Supercomputing
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Independent Job Scheduling is one of the most useful versions of scheduling in grid systems. It aims at computing efficient and optimal mapping of jobs and/or applications submitted by independent users to the grid resources. Besides traditional restrictions, mapping of jobs to resources should be computed under high degree of heterogeneity of resources, the large scale and the dynamics of the system. Because of the complexity of the problem, the heuristic and meta-heuristic approaches are the most feasible methods of scheduling in grids due to their ability to deliver high quality solutions in reasonable computing time. One class of such meta-heuristics is Hierarchic Genetic Strategy (HGS). It is defined as a variant of Genetic Algorithms (GAs) which differs from the other genetic methods by its capability of concurrent search of the solution space. In this work, we present an implementation of HGS for Independent Job Scheduling in dynamic grid environments. We consider the bi-objective version of the problem in which makespan and flowtime are simultaneously optimized. Based on our previous work, we improve the HGS scheduling strategy by enhancing its main branching operations. The resulting HGS-based scheduler is evaluated under the heterogeneity, the large scale and dynamics conditions using a grid simulator. The experimental study showed that the HGS implementation outperforms existing GA-based schedulers proposed in the literature.