Probability, stochastic processes, and queueing theory: the mathematics of computer performance modeling
Performance bounds for distributed systems with workload variabilities and uncertainties
Parallel Computing - Special issue: distributed and parallel systems: environments and tools
Modelling with Generalized Stochastic Petri Nets
ACM SIGMETRICS Performance Evaluation Review - Special issue on Stochastic Petri Nets
Predicting Performance of Parallel Computations
IEEE Transactions on Parallel and Distributed Systems
Symbolic Performance Modeling of Parallel Systems
IEEE Transactions on Parallel and Distributed Systems
Performance Evaluation of Computer and Communication Systems, Joint Tutorial Papers of Performance '93 and Sigmetrics '93
Interval Based Workload Characterization for Distributed Systems
Proceedings of the 9th International Conference on Computer Performance Evaluation: Modelling Techniques and Tools
A probabilistic approach to parallel system performance modelling
HICSS '95 Proceedings of the 28th Hawaii International Conference on System Sciences
Using Stochastic Intervals to Predict Application Behavior on Contended Resources
ISPAN '99 Proceedings of the 1999 International Symposium on Parallel Architectures, Algorithms and Networks
A Hybrid Intelligent Method for Performance Modeling and Prediction of Workflow Activities in Grids
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Predicting the execution time of grid workflow applications through local learning
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
PV-EASY: a strict fairness guaranteed and prediction enabled scheduler in parallel job scheduling
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
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Performance prediction for parallel applications running in heterogeneous clusters is difficult to accomplish due to the unpredictable resource contention patterns that can be found in such environments. Typically, components of a parallel application will contend for the use of resources among themselves and with entities external to the application, such as other processes running in the computers of the cluster. The performance modeling approach should be able to represent these sources of contention and to produce an estimate of the execution time, preferably in polynomial time. This paper presents a polynomial time static performance prediction approach in which the prediction takes the form of an interval of values instead of a single value. The extra information given by an interval of values represents the variability of the underlying environment more accurately, as indicated by the practical examples presented.