Observations on Using Genetic Algorithms for Dynamic Load-Balancing
IEEE Transactions on Parallel and Distributed Systems
Journal of Parallel and Distributed Computing
Tabu Search
Design and Evaluation of Tabu Search Algorithms forMultiprocessor Scheduling
Journal of Heuristics
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
Sub optimal scheduling in a grid using genetic algorithms
Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
Framework for Task Scheduling in Heterogeneous Distributed Computing Using Genetic Algorithms
Artificial Intelligence Review
Efficient Hierarchical Parallel Genetic Algorithms using Grid computing
Future Generation Computer Systems
Joint QoS optimization for layered computational grid
Information Sciences: an International Journal
CCGRID '08 Proceedings of the 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid
An ant algorithm for balanced job scheduling in grids
Future Generation Computer Systems
A Dynamic Resource Broker and Fuzzy Logic Based Scheduling Algorithm in Grid Environment
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
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
Research on Fuzzy Reinforcement Learning Algorithm for Agents in Grids
IITAW '09 Proceedings of the 2009 Third International Symposium on Intelligent Information Technology Application Workshops
Adaptive grid job scheduling with genetic algorithms
Future Generation Computer Systems
Computational models and heuristic methods for Grid scheduling problems
Future Generation Computer Systems
A fuzzy neural network based scheduling algorithm for job assignment on computational grids
NBiS'07 Proceedings of the 1st international conference on Network-based information systems
Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm
Future Generation Computer Systems
A novel multi-agent reinforcement learning approach for job scheduling in Grid computing
Future Generation Computer Systems
Future Generation Computer Systems
Information Sciences: an International Journal
Achieving balance between proximity and diversity in multi-objective evolutionary algorithm
Information Sciences: an International Journal
Information Sciences: an International Journal
An agent-based model of hierarchic genetic search
Computers & Mathematics with Applications
Security, energy, and performance-aware resource allocation mechanisms for computational grids
Future Generation Computer Systems
The Journal of Supercomputing
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Task scheduling and resource allocation are the key rationale behind the computational grid. Distributed resource clusters usually work in different autonomous domains with their own access and security policies that have a great impact on the successful task execution across the domain boundaries. Heuristics and metaheuristics are the effective technologies for scheduling in grids due to their ability to deliver high quality solutions in reasonable time. In this paper, we develop a Hierarchic Genetic Scheduler (HGS-Sched) for improving the effectiveness of the single-population genetic-based schedulers in the dynamic grid environment. The HGS-Sched enables a concurrent exploration of the solution space by many small dependent populations. We consider a bi-objective independent batch job scheduling problem with makespan and flowtime minimized in hierarchical mode (makespan is a dominant criterion). The empirical results show the high effectiveness of the proposed method in comparison with the mono-population and hybrid genetic-based schedulers.