Factoring: a method for scheduling parallel loops
Communications of the ACM
Divisible task scheduling — concept and verification
Parallel Computing - Special issue on task scheduling problems for parallel and distributed systems
Future Generation Computer Systems - Special issue on metacomputing
Journal of Parallel and Distributed Computing
Scheduling Divisible Loads in Parallel and Distributed Systems
Scheduling Divisible Loads in Parallel and Distributed Systems
Sharing Partitionable Workloads in Heterogeneous NOWs: Greedier Is Not Better
CLUSTER '01 Proceedings of the 3rd IEEE International Conference on Cluster Computing
Heuristics for Scheduling Parameter Sweep Applications in Grid Environments
HCW '00 Proceedings of the 9th Heterogeneous Computing Workshop
Design and Evaluation of a Resource Selection Framework for Grid Applications
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
RUMR: Robust Scheduling for Divisible Workloads
HPDC '03 Proceedings of the 12th IEEE International Symposium on High Performance Distributed Computing
UMR: A Multi-Round Algorithm for Scheduling Divisible Workloads
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Homeostatic and Tendency-Based CPU Load Predictions
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Scheduling divisible workloads on heterogeneous platforms
Parallel Computing - Parallel matrix algorithms and applications (PMAA '02)
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
A dynamic task scheduling algorithm for grid computing system
ISPA'04 Proceedings of the Second international conference on Parallel and Distributed Processing and Applications
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Scheduling divisible workloads in distributed systems has been one of the interesting research problems over the last few years. Most of the scheduling algorithms previously introduced are based on the master-worker paradigm. However, the majority of these algorithms assume that workers are dedicated machines, which is a wrong assumption in distributed environments such as Grids. In this work, we propose a dynamic scheduling methodology that takes into account the two prominent aspects of Grids: heterogeneity and dynamicity. The premise of our methodology is to use a prediction strategy to estimate the CPU speed of each Grid resource and subsequently feed this estimation to a static scheduling algorithm in order to divide the workload into suitable chunks in light of the available computational power. Such integration can produce dynamic scheduling algorithms that can handle the constantly changing properties of Grid resources.