Artificial Intelligence Review - Special issue on lazy learning
ACM Computing Surveys (CSUR)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Predicting Application Run Times Using Historical Information
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
A computational economy for grid computing and its implementation in the Nimrod-G resource broker
Future Generation Computer Systems - Grid computing: Towards a new computing infrastructure
Predictive Application-Performance Modeling in a Computational Grid Environment
HPDC '99 Proceedings of the 8th IEEE International Symposium on High Performance Distributed Computing
Resource management in metacomputing environments (parallel computing)
Resource management in metacomputing environments (parallel computing)
Heuristic scheduling for bag-of-tasks applications in combination with QoS in the computational grid
Future Generation Computer Systems - Special issue: Advanced grid technologies
Grid load balancing using intelligent agents
Future Generation Computer Systems
Predicting job start times on clusters
CCGRID '04 Proceedings of the 2004 IEEE International Symposium on Cluster Computing and the Grid
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Predict task running time in grid environments based on CPU load predictions
Future Generation Computer Systems
Future Generation Computer Systems
Knowledge discovery for scheduling in computational grids
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
State-based predictions with self-correction on Enterprise Desktop Grid environments
Journal of Parallel and Distributed Computing
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Metaschedulers in the Grid need dynamic information to support their scheduling decisions. Job response time on computing resources, for instance, is such a performance metric. In this paper, we propose an Instance Based Learning technique to predict response times by mining historical performance data. The novelty of our approach is to introduce policy attributes in representing and comparing resource states, which are defined as the pools of running and queued jobs on the resources at the time of making predictions. The policy attributes reflect the local scheduling policies and they can be automatically discovered using genetic search. An extensive empirical evaluation is conducted to validate our technique using real workload traces, which are collected from the NIKHEF production cluster on the LHC Computing Grid and Blue Horizon in the San Diego Supercomputer Center (SDSC). The experimental results show that acceptable prediction accuracy can be achieved, where the normalized average prediction errors for response times are ranging from 0.57 to 0.79.