IEEE Transactions on Computers
Allocating Bandwidth for Bursty Connections
SIAM Journal on Computing
Determining Asynchronous Acyclic Pipeline Execution Times
IPPS '96 Proceedings of the 10th International Parallel Processing Symposium
Proceedings of the 2002 IEEE/ACM international conference on Computer-aided design
WCET Analysis of Probabilistic Hard Real-Time Systems
RTSS '02 Proceedings of the 23rd IEEE Real-Time Systems Symposium
Stochastic Analysis of Periodic Real-Time Systems
RTSS '02 Proceedings of the 23rd IEEE Real-Time Systems Symposium
Execution-time Prediction for Dynamic Streaming Applications with Task-level Parallelism
DSD '07 Proceedings of the 10th Euromicro Conference on Digital System Design Architectures, Methods and Tools
The worst-case execution-time problem—overview of methods and survey of tools
ACM Transactions on Embedded Computing Systems (TECS)
A method for estimating the execution time of a parallel task on a grid node
EGC'05 Proceedings of the 2005 European conference on Advances in Grid Computing
A survey of convergence results on particle filtering methods forpractitioners
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
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This paper addresses the problem of stochastic task execution time estimation agnostic to the process distributions. The proposed method is orthogonal to the application structure and underlying architecture. We build the time varying state space model of the task execution time. In the case of software pipelined tasks, to refine the estimate quality, the state-space is modeled as Multiple Input Single Output (MISO) system by taking into account the current execution time of the predecessor task. To obtain nearly Bayesian estimates, irrespective of the process distribution, the sequential Monte Carlo method is applied which form the recursive solution to reduce the overheads and comprises of time update and correction steps. We experimented on three different platforms, including multi-core, using the time parallelized H.264 decoder: a control dominant computationally demanding application and AES encoder: a pure data flow application. Results show that estimates obtained by our method are superior in quality and are up to 68% better in comparison to others.