Predicting Application Run Times Using Historical Information
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Scheduling under Uncertainty: Planning for the Ubiquitous Grid
COORDINATION '02 Proceedings of the 5th International Conference on Coordination Models and Languages
Resource scheduling on grid: handling uncertainty
GRID '03 Proceedings of the 4th International Workshop on Grid Computing
Multicriteria aspects of Grid resource management
Grid resource management
Sub optimal scheduling in a grid using genetic algorithms
Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
A Genetic Algorithm Based Approach for Scheduling Decomposable Data Grid Applications
ICPP '04 Proceedings of the 2004 International Conference on Parallel Processing
Learning-Based Negotiation Strategies for Grid Scheduling
CCGRID '06 Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid
| Hi-index | 0.00 |
One of the biggest challenges in building grid schedulers is how to deal with the uncertainty in what future computational resources will be available. Current techniques for Grid scheduling rarely account for resources whose performance, reliability, and cost vary with time simultaneously. In this paper we address the problem of delivering a deadline based scheduling in a dynamic and uncertain environment represented by dynamic Bayesian network based stochastic resource model. The genetic algorithm is used to find the optimal and robust solutions so that the highest probability of satisfying the user's QoS objectives at a specified deadline can be achieved. It is shown via a simulation that the new methodology will not only achieving a relatively high probability of scheduling workflow with multiple goals successfully, but also be resilient to environment changes.