Static and dynamic processor scheduling disciplines in heterogeneous parallel architectures
Journal of Parallel and Distributed Computing
Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment
Journal of the ACM (JACM)
The AppLeS parameter sweep template: user-level middleware for the grid
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
Chameleon: A Resource Scheduler in A Data Grid Environment
CCGRID '03 Proceedings of the 3st International Symposium on Cluster Computing and the Grid
Dynamic Matching and Scheduling of a Class of Independent Tasks onto Heterogeneous Computing Systems
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
A Study of Deadline Scheduling for Client-Server Systems on the Computational Grid
HPDC '01 Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing
Real-Time System Design and Analysis
Real-Time System Design and Analysis
Grid Computing
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
Deadline Scheduling with Priority for Client-Server Systems on the Grid
GRID '04 Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing
A deadline and budget constrained scheduling algorithm for escience applications on data grids
ICA3PP'05 Proceedings of the 6th international conference on Algorithms and Architectures for Parallel Processing
A general distributed scalable grid scheduler for independent tasks
Journal of Parallel and Distributed Computing
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We consider non-preemptively scheduling a bag of independent mixed tasks in computational grids. We construct a novel Generalized Distributed Scheduler (GDS) for tasks with different priorities and deadlines. Tasks are ranked based upon priority and deadline and scheduled. Tasks are shuffled to earlier points to pack the schedule and create fault tolerance. Dispatching is based upon task-resource matching and accounts for computation as well as communication capacities. Simulation results demonstrate that with respect to the number of high-priority tasks meeting deadlines, GDS outperforms prior approaches by over 40% without degrading schedulability of other tasks. Indeed, with respect to the total number of schedulable tasks meeting deadlines, GDS outperforms them by 4%. The complexity of GDS is O(n2m) where n is the number of tasks and m the number of machines. GDS successfully schedules tasks with hard deadlines in a mix of soft and firm tasks, without a knowledge of a complete state of the grid. This way it helps open the grid and makes it amenable for commercialization.