Introduction to operations research, 4th ed.
Introduction to operations research, 4th ed.
Adaptive Optimal Load Balancing in a Nonhomogeneous Multiserver System with a Central Job Scheduler
IEEE Transactions on Computers
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
Predicting Application Run Times Using Historical Information
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
A decoupled scheduling approach for the GrADS program development environment
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Performance Evaluation of an Agent-Based Resource Management Infrastructure for Grid Computing
CCGRID '01 Proceedings of the 1st International Symposium on Cluster Computing and the Grid
HPDC '00 Proceedings of the 9th IEEE International Symposium on High Performance Distributed Computing
Dynamic Allocation of Servers in a Grid Hosting Environment
GRID '04 Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
Making the Grid Predictable through Reservations and Performance Modelling
The Computer Journal
Efficient Response Time Predictions by Exploiting Application and Resource State Similarities
GRID '05 Proceedings of the 6th IEEE/ACM International Workshop on Grid Computing
Composing geoinformatics workflows with user preferences
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An ant colony optimization algorithm for the time-varying workflow scheduling problem in grids
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Dynamic remote host classification in grid computing using Clonalg
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
A rotary chaotic PSO algorithm for trustworthy scheduling of a grid workflow
Computers and Operations Research
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Grid computing infrastructures are inherently dynamic and unpredictable environments shared by many users. Grid schedulers aim to make efficient use of Grid resources while providing the best possible performance to the Grid applications and satisfying the associated performance and policy constraints. Additionally, in commercial Grid settings, where the Grid resource brokering becomes an increasingly important part of Grid scheduling, it is necessary to minimise the cost of application execution on the behalf of the Grid users, while ensuring that the applications meet their QoS constraints. Efficient resource allocation could in turn also allow the resource broker to maximise it's profit by minimising the number of resources procured. Scheduling in such a large-scale, dynamic and distributed environment is a complex undertaking. In this paper, we propose an approach to Grid scheduling which abstracts over the details of individual applications, focusing instead on the global cost optimisation problem and the scheduling of the entire Grid workload. Our model places particular emphasis on the stochastic and unpredictable nature of the Grid, leading to a more accurate reflection of the state of the Grid and hence more efficient and accurate scheduling decisions.