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Energy efficient real-time task scheduling attracted a lot of attention in the past decade. Most of the time, deterministic execution lengths for tasks were considered, but this model fits less and less with the reality, especially with the increasing number of multimedia applications. Its why a lot of research is starting to consider stochastic models, where execution times are only known stochastically. However, authors consider that they have a pretty much precise knowledge about the properties of the system, especially regarding to the worst case execution time (or worst case execution cycles, WCEC). In this work, we try to relax this hypothesis, and assume that the WCEC can vary. We propose miscellaneous methods to react to such a situation, and give many simulation results attesting that with a small effort, we can provide very good results, allowing to keep a low deadline miss rate as well as an energy consumption similar to clairvoyant algorithms.
Estimating Worst-Case Execution Time (WCET) is of utmost importance for developing Cyber-Physical and Safety-Critical Systems. The systems scheduler uses the estimated WCET to schedule each task of these systems, and failure may lead to catastrophic
Despite the attempts of well-designed anonymous communication tools to protect users from tracking or identification, flaws in surrounding software (such as web browsers) and mistakes in configuration may leak the users identity. We introduce Nymix,
Flow routing over inter-datacenter networks is a well-known problem where the network assigns a path to a newly arriving flow potentially according to the network conditions and the properties of the new flow. An essential system-wide performance met
Recent commercial hardware platforms for embedded real-time systems feature heterogeneous processing units and computing accelerators on the same System-on-Chip. When designing complex real-time application for such architectures, the designer needs
In this paper, we consider the worst-case regret of Linear Thompson Sampling (LinTS) for the linear bandit problem. citet{russo2014learning} show that the Bayesian regret of LinTS is bounded above by $widetilde{mathcal{O}}(dsqrt{T})$ where $T$ is the