Estimating Capacity for Sharing in a Privately Owned Workstation Environment
IEEE Transactions on Software Engineering
A worldwide flock of Condors: load sharing among workstation clusters
Future Generation Computer Systems - Special issue: resource management in distributed systems
Parallel application scheduling on networks of workstations
Journal of Parallel and Distributed Computing
Coordinating parallel processes on networks of workstations
Journal of Parallel and Distributed Computing
Availability and utility of idle memory in workstation clusters
SIGMETRICS '99 Proceedings of the 1999 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
An Efficient Adaptive Scheduling Scheme for Distributed Memory Multicomputers
IEEE Transactions on Parallel and Distributed Systems
SETI@home: an experiment in public-resource computing
Communications of the ACM
Online Prediction of the Running Time of Tasks
Cluster Computing
Predicting Queue Times on Space-Sharing Parallel Computers
IPPS '97 Proceedings of the 11th International Symposium on Parallel Processing
A Historical Application Profiler for Use by Parallel Schedulers
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
Using Run-Time Predictions to Estimate Queue Wait Times and Improve Scheduler Performance
IPPS/SPDP '99/JSSPP '99 Proceedings of the Job Scheduling Strategies for Parallel Processing
Experiences with predicting resource performance on-line in computational grid settings
ACM SIGMETRICS Performance Evaluation Review
Cycle stealing under immediate dispatch task assignment
Proceedings of the fifteenth annual ACM symposium on Parallel algorithms and architectures
Effective Metacomputing using LSF MultiCluster
CCGRID '01 Proceedings of the 1st International Symposium on Cluster Computing and the Grid
Entropia: architecture and performance of an enterprise desktop grid system
Journal of Parallel and Distributed Computing - Special issue on computational grids
Predicting application run times with historical information
Journal of Parallel and Distributed Computing
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
Automatic methods for predicting machine availability in desktop Grid and peer-to-peer systems
CCGRID '04 Proceedings of the 2004 IEEE International Symposium on Cluster Computing and the Grid
Predicting job start times on clusters
CCGRID '04 Proceedings of the 2004 IEEE International Symposium on Cluster Computing and the Grid
Characterizing resource availability in enterprise desktop grids
Future Generation Computer Systems
Backfilling Using System-Generated Predictions Rather than User Runtime Estimates
IEEE Transactions on Parallel and Distributed Systems
Scheduling Policies for Processor Coallocation in Multicluster Systems
IEEE Transactions on Parallel and Distributed Systems
Mining performance data for metascheduling decision support in the grid
Future Generation Computer Systems - Special section: Data mining in grid computing environments
Cooperating coscheduling: a coscheduling proposal aimed at non-dedicated heterongeneous NOWs
Journal of Computer Science and Technology
Future Generation Computer Systems
A multicriteria approach to two-level hierarchy scheduling in grids
Journal of Scheduling
Trace-based evaluation of job runtime and queue wait time predictions in grids
Proceedings of the 18th ACM international symposium on High performance distributed computing
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Using Historical Data to Predict Application Runtimes on Backfilling Parallel Systems
PDP '10 Proceedings of the 2010 18th Euromicro Conference on Parallel, Distributed and Network-based Processing
On/off-line prediction applied to job scheduling on non-dedicated NOWs
Journal of Computer Science and Technology - Special issue on natural language processing
Two level job-scheduling strategies for a computational grid
PPAM'05 Proceedings of the 6th international conference on Parallel Processing and Applied Mathematics
Workload characteristics of a multi-cluster supercomputer
JSSPP'04 Proceedings of the 10th international conference on Job Scheduling Strategies for Parallel Processing
MetaLoRaS: a predictable metascheduler for non-dedicated multiclusters
ISPA'06 Proceedings of the 4th international conference on Parallel and Distributed Processing and Applications
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The abundant computing resources in current organizations provide new opportunities for executing parallel scientific applications and using resources. The Enterprise Desktop Grid Computing (EDGC) paradigm addresses the potential for harvesting the idle computing resources of an organization's desktop PCs to support the execution of the company's large-scale applications. In these environments, the accuracy of response-time predictions is essential for effective metascheduling that maximizes resource usage without harming the performance of the parallel and local applications. However, this accuracy is a major challenge due to the heterogeneity and non-dedicated nature of EDGC resources. In this paper, two new prediction techniques are presented based on the state of resources. A thorough analysis by linear regression demonstrated that the proposed techniques capture the real behavior of the parallel applications better than other common techniques in the literature. Moreover, it is possible to reduce deviations with a proper modeling of prediction errors, and thus, a Self-adjustable Correction method (SAC) for detecting and correcting the prediction deviations was proposed with the ability to adapt to the changes in load conditions. An extensive evaluation in a real environment was conducted to validate the SAC method. The results show that the use of SAC increases the accuracy of response-time predictions by 35%. The cost of predictions with self-correction and its accuracy in a real environment was analyzed using a combination of the proposed techniques. The results demonstrate that the cost of predictions is negligible and the combined use of the prediction techniques is preferable.