Interfacing Condor and PVM to harness the cycles of workstation clusters
Future Generation Computer Systems - Special issue: resource management in distributed systems
Approximation algorithms for scheduling
Approximation algorithms for NP-hard problems
Using parallel program characteristics in dynamic processor allocation policies
Performance Evaluation
High Performance Cluster Computing: Architectures and Systems
High Performance Cluster Computing: Architectures and Systems
Adaptive Scheduling for Master-Worker Applications on the Computational Grid
GRID '00 Proceedings of the First IEEE/ACM International Workshop on Grid Computing
Nimrod: a tool for performing parametrised simulations using distributed workstations
HPDC '95 Proceedings of the 4th IEEE International Symposium on High Performance Distributed Computing
Adaptive Scheduling for Task Farming with Grid Middleware
International Journal of High Performance Computing Applications
Adaptive Scheduling for Master-Worker Applications on the Computational Grid
GRID '00 Proceedings of the First IEEE/ACM International Workshop on Grid Computing
Evolutionary Optimization Techniques on Computational Grids
ICCS '02 Proceedings of the International Conference on Computational Science-Part I
Self-Adjusting Scheduling of Master-Worker Applications on Distributed Clusters
Euro-Par '01 Proceedings of the 7th International Euro-Par Conference Manchester on Parallel Processing
Solving Engineering Applications with LAMGAC over MPI-2
Proceedings of the 9th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
Parallel discovery of network motifs
Journal of Parallel and Distributed Computing
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We investigate the problem arising in scheduling parallel applications that follow a master-worker paradigm in order to maximize both the resource efficiency and the application performance. We propose a simple scheduling strategy that dynamically measures application execution time and uses these measurements to automatically adjust the number of allocated processors to achieve the desirable efficiency, minimizing the impact in loss of speedup. The effectiveness of the proposed strategy has been assessed by means of simulation experiments in which several scheduling policies were compared. We have observed that our strategy obtains similar results to other strategies that use a priori information about the application, and we have derived a set of empirical rules that can be used to dynamically adjust the number of processors allocated to the application.