Dynamic Storage Allocation: A Survey and Critical Review
IWMM '95 Proceedings of the International Workshop on Memory Management
Scheduling with Advanced Reservations
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
Future Generation Computer Systems - Special section: Information engineering and enterprise architecture in distributed computing environments
A P2P strategy for QoS discovery and SLA negotiation in Grid environment
Future Generation Computer Systems
Measuring Fragmentation of Two-Dimensional Resources Applied to Advance Reservation Grid Scheduling
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Concurrency and Computation: Practice & Experience
HiPC'08 Proceedings of the 15th international conference on High performance computing
Analysis of Tasks Reallocation in a Dedicated Grid Environment
CLUSTER '10 Proceedings of the 2010 IEEE International Conference on Cluster Computing
Exponential Smoothing for Network-Aware Meta-scheduler in Advance in Grids
ICPPW '10 Proceedings of the 2010 39th International Conference on Parallel Processing Workshops
Network-aware meta-scheduling in advance with autonomous self-tuning system
Future Generation Computer Systems
GRID '11 Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing
Mathematical and Computer Modelling: An International Journal
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In highly heterogeneous and distributed systems, like Grids, it is rather difficult to provide QoS to the users. As reservations of resources may not always be possible, another possible way of enhancing the perceived QoS is by performing meta-scheduling of jobs in advance, where jobs are scheduled some time before they are actually executed. Thank to this, it is more likely that the appropriate resources are available to execute the job when needed. When using this type of scheduling, fragmentation appears and may become the cause of poor resource utilization. Because of that, some techniques are needed to perform rescheduling of tasks that may reduce the existing fragmentation. To this end, knowing the status of the system is a must. However, how to measure and quantify the existing fragmentation in a Grid system is a challenging task. This paper proposes different metrics aiming at measuring that fragmentation not only at resource level but also taking into account all the resources of the Grid environment as a whole. Finally, a performance evaluation of the proposed metrics over a real test bed is presented.