A provisioning model and its comparison with best-effort for performance-cost optimization in grids
Proceedings of the 16th international symposium on High performance distributed computing
Applying double auctions for scheduling of workflows on the Grid
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Performance analysis of allocation policies for interGrid resource provisioning
Information and Software Technology
Multi-objective planning for workflow execution on Grids
GRID '07 Proceedings of the 8th IEEE/ACM International Conference on Grid Computing
HiPC'08 Proceedings of the 15th international conference on High performance computing
Grids and Clouds: Making Workflow Applications Work in Heterogeneous Distributed Environments
International Journal of High Performance Computing Applications
Negotiation-Based Scheduling of Scientific Grid Workflows Through Advance Reservations
Journal of Grid Computing
Deadline-sensitive workflow orchestration without explicit resource control
Journal of Parallel and Distributed Computing
Hybrid Computing-Where HPC meets grid and Cloud Computing
Future Generation Computer Systems
BTS: Resource capacity estimate for time-targeted science workflows
Journal of Parallel and Distributed Computing
Formal QoS Policy Based Grid Resource Provisioning Framework
Journal of Grid Computing
Characterizing and profiling scientific workflows
Future Generation Computer Systems
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In this paper, we present algorithms for Grid resource provisioning that employ agreement-based resource management. These algorithms allow userlevel resource allocation and scheduling of applications that are structured as a precedenceconstrained set of tasks. We present a provisioning model where the resource availability in the Grid can be enumerated as a set of slots. A slot is defined as a number of processors available from a certain start time for a certain duration at a certain cost. Using a cost model that combines the cost of resource allocation and the expected application runtime, we evaluate the performance of the Min-Min and of the Genetic algorithm (GA)-based heuristics for a range of synthetic applications. We show that the GA paired with a list scheduling algorithm can obtain significantly better solutions than the Min-Min heuristic alone.