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This modeling vastly extends previous models of execution times as either single points or range-bound intervals.
For this purpose, we present the timed discrete event models of execution of periodic tasks and sporadic tasks and resource access for shared resources.
The approach, while being very simple, is able to derive analytic models of execution time as a function of parameters, such as processor speed, network latency, or bandwidth without even looking at the application source.
In this paper, we show the existence of randomized truthful (non-polynomial-time) algorithms with an expected approximation ratio equal to 3/2 for different scheduling settings (identical machines with and without release dates and unrelated machines) and models of execution (strong or weak).
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We propose a semi-partitioned model of execution with work-stealing for fork-join tasks.
We propose a synchronous model of execution used to coordinate membrane evolution.
A streaming dataflow model of execution addresses the problem to some extent, exploiting all natural opportunities for parallel execution, as allowed by the data dependencies in a plan.
Existing interpreters for i-DSMLs exhibit tight coupling between the implicit model of execution (MoE) and the semantics of the domain.
(2006) [1] for the problem with identical machines (P∥Cmax) in the strong model of execution and reaches, asymptotically, the lower bound of Christodoulou et al.
The developed model base management system has the facilities such as model base, model directory, model execution, integration of models and command processor.
We show that more realistic models of transactional execution require speculative execution.
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