IEEE Transactions on Computers
Modulation, Detection and Coding
Modulation, Detection and Coding
Kalman Filtering and Neural Networks
Kalman Filtering and Neural Networks
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
Stochastic Load Balancing and Related Problems
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Exploring the Probabilistic Design Space of Multimedia Systems
RSP '03 Proceedings of the 14th IEEE International Workshop on Rapid System Prototyping (RSP'03)
Schedulability Analysis for Tasks with Static and Dynamic Offsets
RTSS '98 Proceedings of the IEEE Real-Time Systems Symposium
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
A Hyperbolic Bound for the Rate Monotonic Algorithm
ECRTS '01 Proceedings of the 13th Euromicro Conference on Real-Time Systems
New sequential Monte Carlo methods for nonlinear dynamic systems
Statistics and Computing
Microprocessors in the era of terascale integration
Proceedings of the conference on Design, automation and test in Europe
The worst-case execution-time problem—overview of methods and survey of tools
ACM Transactions on Embedded Computing Systems (TECS)
A Stochastic Framework for Multiprocessor Soft Real-Time Scheduling
RTAS '10 Proceedings of the 2010 16th IEEE Real-Time and Embedded Technology and Applications Symposium
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Proceedings of the 48th Design Automation Conference
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The advent of multicore platforms has renewed the interest in scheduling techniques for real-time systems. Historically, 'scheduling decisions' are implemented considering fixed task execution times, as for the case of Worst Case Execution Time (WCET). The limitations of scheduling considering WCET manifest in terms of under-utilization of resources for large application classes. In the realm of multicore systems, the notion of WCET is hardly meaningful due to the large set of factors influencing it. Within soft real-time systems, a more realistic modeling approach would be to consider tasks featuring varying execution times (i.e. stochastic). This paper addresses the problem of stochastic task execution time scheduling that is agnostic to statistical properties of the execution time. Our proposed method is orthogonal to any number of linear acyclic task graphs and their underlying architecture. The joint estimation of execution time and the associated parameters, relying on the interdependence of parallel tasks, help build a 'nonlinear Non-Gaussian state space' model. To obtain nearly Bayesian estimates, irrespective of the execution time characteristics, a recursive solution of the state space model is found by means of the Monte Carlo method. The recursive solution reduces the computational and memory overhead and adapts statistical properties of execution times at run time. Finally, the variable laxity EDF scheduler schedules the tasks considering the predicted execution times. We show that variable execution time scheduling improves the utilization of resources and ensures the quality of service. Our proposed new solution does not require any a priori knowledge of any kind and eliminates the fundamental constraints associated with the estimation of execution times. Results clearly show the advantage of the proposed method as it achieves 76% better task utilization, 68% more task scheduling and deadline miss reduction by 53% compared to current state-of-the-art methods.