Goodness-of-fit techniques
The workload on parallel supercomputers: modeling the characteristics of rigid jobs
Journal of Parallel and Distributed Computing
What is worth learning from parallel workloads?: a user and session based analysis
Proceedings of the 19th annual international conference on Supercomputing
Using Site-Level Modeling to Evaluate the Performance of Parallel System Schedulers
MASCOTS '06 Proceedings of the 14th IEEE International Symposium on Modeling, Analysis, and Simulation
The performance of bags-of-tasks in large-scale distributed systems
HPDC '08 Proceedings of the 17th international symposium on High performance distributed computing
Model-based simulation and performance evaluation of grid scheduling strategies
Future Generation Computer Systems
Fault-aware scheduling for Bag-of-Tasks applications on Desktop Grids
GRID '06 Proceedings of the 7th IEEE/ACM International Conference on Grid Computing
On Simulation and Design of Parallel-Systems Schedulers: Are We Doing the Right Thing?
IEEE Transactions on Parallel and Distributed Systems
The Failure Trace Archive: Enabling Comparative Analysis of Failures in Diverse Distributed Systems
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Predicting the Quality of Service of a Peer-to-Peer Desktop Grid
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
IEEE Internet Computing
Towards a profound analysis of bags-of-tasks in parallel systems and their performance impact
Proceedings of the 20th international symposium on High performance distributed computing
IEEE Transactions on Parallel and Distributed Systems
Modeling machine availability in enterprise and wide-area distributed computing environments
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
The characteristics and performance of groups of jobs in grids
Euro-Par'07 Proceedings of the 13th international Euro-Par conference on Parallel Processing
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A computational grid is a large scale federated infrastructure where users execute several types of applications with different submission rates. On the evaluation of solutions for grids, there are not much effort on using realistic workloads for experiments, and most of the time users' activities and applications are not well represented. In this work, we propose a user-based grid workload model which is based on clustering users according to their behaviour in the system and their applications. The results show that according to a new metric proposed, the model quality increases when using clustering and extracting models for the group of users with similar behaviour. Moreover, we compare our user-based modelling with a state-of-the-art system-based modelling approach. We show that by using our user-based model the system load can be easily changed by varying the number of users in the grid, creating different evaluation scenarios without affecting individual users' behaviour. On the other hand, varying the number of users in the system-based model does not affect the system load and change the way individual user's behave on the system, which can result in unrealistic users' activities.