Benchmarking and comparison of the task graph scheduling algorithms
Journal of Parallel and Distributed Computing
Static scheduling algorithms for allocating directed task graphs to multiprocessors
ACM Computing Surveys (CSUR)
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
A Dynamic Matching and Scheduling Algorithm for Heterogeneous Computing Systems
HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
Triplet: A Clustering Scheduling Algorithm for Heterogeneous Systems
ICPPW '01 Proceedings of the 2001 International Conference on Parallel Processing Workshops
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
Scheduling workflow applications on processors with different capabilities
Future Generation Computer Systems - Collaborative and learning applications of grid technology
A low-cost rescheduling policy for efficient mapping of workflows on grid systems
Scientific Programming - AxGrids 2004
Reliable DAG scheduling on grids with rewinding and migration
Proceedings of the first international conference on Networks for grid applications
Scheduling DAGs on grids with copying and migration
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
Computers & Mathematics with Applications
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We consider the problem of scheduling parallel applications, represented by directed acyclic graphs (DAGs), onto Grid style resource pools. The core issues are that the availability and performance of grid resources, which are already by their nature heterogeneous, can be expected to vary dynamically, even during the course of an execution. Typical scheduling methods in the literature partially address this issue because they consider static heterogenous computing environments (i.e. heterogeneous resources are dedicated and unchanging over time). This paper presents the Grid Task Positioning GTP scheduling method, which addresses the problem by allowing rescheduling of an executing application in response to significant variations in resource characteristics. GTP considers the impact of partial completion of tasks and task migration. We compare the performance of GTP with that of the well-known, and static, Heterogeneous Earliest Finish Time (HEFT) algorithm.