Multiprocessor Online Scheduling of Hard-Real-Time Tasks
IEEE Transactions on Software Engineering
Bandwidth quantization and states reduction in the broadband ISDN
IEEE/ACM Transactions on Networking (TON)
A new O (N(2)) algorithm for the symmetric tridiagonal eigenvalue/eigenvector problem
A new O (N(2)) algorithm for the symmetric tridiagonal eigenvalue/eigenvector problem
Fast scheduling of periodic tasks on multiple resources
IPPS '95 Proceedings of the 9th International Symposium on Parallel Processing
Mixed Pfair/ERfair Scheduling of Asynchronous Periodic Tasks
ECRTS '01 Proceedings of the 13th Euromicro Conference on Real-Time Systems
Optimal Scheduling of Periodic Tasks on Multiple Identical Processors
Optimal Scheduling of Periodic Tasks on Multiple Identical Processors
Real Time Scheduling Theory: A Historical Perspective
Real-Time Systems
Computers and Operations Research
Real-time task scheduling by multiobjective genetic algorithm
Journal of Systems and Software
IEEE/ACM Transactions on Networking (TON)
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We simplify the periodic tasks scheduling problem by making a trade off between processor load and computational complexity. A set \big. N\bigr. of periodic tasks, each characterized by its density \big. \rho _i\bigr., contains \big. n\bigr. possibly unique values of \big. \rho _{i}\bigr.. We transform \big. N\bigr. through a process called quantization, in which each \big. \rho _{i}\in_{ } N\bigr. is mapped onto a service level \big. s_{j}\in_{ } L\bigr., where \big. \left|L\right|=l\ll n\bigr. and \big. \rho _{i}\leq s_{j}\bigr. (this second condition differentiates this problem from the p-median problem on the real line). We define the Periodic Task Quantization problem with Deterministic input (PTQ-D) and present an optimal polynomial time dynamic programming solution. We also introduce the problem PTQ-S (with Stochastic input) and present an optimal solution. We examine, in a simulation study, the trade off penalty of excess processor load needed to service the set of quantized tasks over the original set, and find that, through quantization onto as few as 15 or 20 service levels, no more than 5 percent processor load is required above the amount requested. Finally, we demonstrate that the scheduling of a set of periodic tasks is greatly simplified through quantization and we present a fast online algorithm that schedules quantized periodic tasks.