A taxonomy and survey of grid resource management systems for distributed computing
Software—Practice & Experience
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
G-commerce: Market Formulations Controlling Resource Allocation on the Computational Grid
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Core Algorithms of the Maui Scheduler
JSSPP '01 Revised Papers from the 7th International Workshop on Job Scheduling Strategies for Parallel Processing
High Performance Parametric Modeling with Nimrod/G: Killer Application for the Global Grid?
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
Scalable Grid Application Scheduling via Decoupled Resource Selection and Scheduling
CCGRID '06 Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid
A Multiobjective Resources Scheduling Approach Based on Genetic Algorithms in Grid Environment
GCCW '06 Proceedings of the Fifth International Conference on Grid and Cooperative Computing Workshops
Relative Performance of Scheduling Algorithms in Grid Environments
CCGRID '07 Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid
The portable batch scheduler and the maui scheduler on linux clusters
ALS'00 Proceedings of the 4th annual Linux Showcase & Conference - Volume 4
Fair Scheduling Algorithms in Grids
IEEE Transactions on Parallel and Distributed Systems
Scheduling strategies for mapping application workflows onto the grid
HPDC '05 Proceedings of the High Performance Distributed Computing, 2005. HPDC-14. Proceedings. 14th IEEE International Symposium
Editorial: Infrastructure and Network-aware Grids and Service Oriented Architectures
Future Generation Computer Systems
Scheduling efficiency of resource information aggregation in grid networks
Future Generation Computer Systems
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Efficient task scheduling is fundamental for the success of the Grids, since it directly affects the Quality of Service (QoS) offered to the users. Efficient scheduling policies should be evaluated based not only on performance metrics that are of interest to the infrastructure side, such as the Grid resources utilization efficiency, but also on user satisfaction metrics, such as the percentage of tasks served by the Grid without violating their QoS requirements. In this paper, we propose a scheduling algorithm for tasks with strict timing requirements, given in the form of a desired start and finish time. Our algorithm aims at minimizing the violations of the time constraints, while at the same time minimizing the number of processors used. The proposed scheduling method exploits concepts derived from spectral clustering, and groups together for assignment to a computing resource the tasks so to a) minimize the time overlapping of the tasks assigned to a given processor and b) maximize the degree of time overlapping among tasks assigned to different processors. Experimental results show that our proposed strategy outperforms greedy scheduling algorithms for different values of the task load submitted.