A mixed-criticality system consists of multiple components with different criticalities. While mixed-criticality scheduling has been extensively studied for the uniprocessor case, the problem of efficient scheduling for the multiprocessor case has largely remained open. We design a fluid model-based multiprocessor mixedcriticality scheduling algorithm, called MC-Fluid in which each task is executed in proportion to its criticalitydependent rate. We propose an exact schedulability condition for MC-Fluid and an optimal assignment algorithm for criticality-dependent execution rates with polynomial-time complexity. Since MC-Fluid cannot be implemented directly on real hardware platforms, we propose another scheduling algorithm, called MC-DP-Fair, which can be implemented while preserving the same schedulability properties as MC-Fluid. We show that MC-Fluid has a speedup factor of (1 + √ 5) /2 (~ 1.618), which is best known in multiprocessor MC scheduling, and simulation results show that MC-DP-Fair outperforms all existing algorithms. Abstract-A mixed-criticality system consists of multiple components with different criticalities. While mixed-criticality scheduling has been extensively studied for the uniprocessor case, the problem of efficient scheduling for the multiprocessor case has largely remained open. We design a fluid model-based multiprocessor mixed-criticality scheduling algorithm, called MCFluid, in which each task is executed in proportion to its criticality-dependent rate. We propose an exact schedulability condition for MC-Fluid and an optimal assignment algorithm for criticality-dependent execution rates with polynomial complexity. Since MC-Fluid cannot construct a schedule on real hardware platforms due to the fluid assumption, we propose MC-DP-Fair algorithm, which can generate a non-fluid schedule while preserving the same schedulability properties as MC-Fluid. We show that MC-Fluid has a speedup factor of (1 + √ 5)/2 (≈ 1.618), which is best known in multiprocessor MC scheduling, and simulation results show that MC-DP-Fair outperforms all existing algorithms.
Mixed-criticality real-time scheduling has been developed to improve resource utilization while guaranteeing safe execution of critical applications. These studies use optimistic resource reservation for all the applications to improve utilization, but prioritize critical applications when the reservations become insufficient at runtime. Many of them however share an impractical assumption that all the critical applications will simultaneously demand additional resources. As a consequence, they under-utilize resources by penalizing all the low-criticality applications. In this paper we overcome this shortcoming using a novel mechanism that comprises a parameter to model the expected number of critical applications simultaneously demanding more resources, and an execution strategy based on the parameter to improve resource utilization. Since most mixedcriticality systems in practice are component-based, we design our mechanism such that the component boundaries provide the isolation necessary to support the execution of low-criticality applications, and at the same time protect the critical ones. We also develop schedulability tests for the proposed mechanism under both a flat as well as a hierarchical scheduling framework. Finally, through simulations, we compare the performance of the proposed approach with existing studies in terms of schedulability and the capability to support low-criticality applications.
A deep learning (DL)-based approach has been proposed to accurately model the relationship between design parameters and the Q factor of photonic crystal (PC) nanocavities. A convolutional neural network (CNN), which consists of two convolutional layers and three fully-connected layers is trained on a large-scale dataset consisting of 12,500 nanocavities. The experimental results show that the CNN is able to achieve a state-of-the-art performance in terms of prediction accuracy (i.e., up to 99.9999%) and convergence speed (i.e., orders-of-magnitude speedup). The proposed approach overcomes shortcomings of existing methods and paves the way for DL-based on-demand and data-driven optimization of PC nanocavities applicable to the rapid design of nanoscale lasers and photonic integrated circuits. We will open source the database and code as one of our main contributions to the photonics research community.
Many existing studies on mixed-criticality (MC) scheduling assume that low-criticality budgets for high-criticality applications are known apriori. These budgets are primarily used as guidance to determine when the scheduler should switch the system mode from low to high. Based on this key observation, in this paper we propose a dynamic MC scheduling model under which low-criticality budgets for individual high-criticality applications are determined at runtime as opposed to being fixed offline. To ensure sufficient budget for high-criticality applications at all times, we use offline schedulability analysis to determine a system-wide total low-criticality budget allocation for all the high-criticality applications combined. This total budget is used as guidance in our model to determine the need for a modeswitch. The runtime strategy then distributes this total budget among the various applications depending on their execution requirement and with the objective of postponing mode-switch as much as possible. We show that this runtime strategy is able to postpone mode-switches for a longer time than any strategy that uses a fixed low-criticality budget allocation for each application. Finally, since we are able to control the total budget allocation for high-criticality applications before mode-switch, we also propose techniques to determine these budgets considering system-wide objectives such as schedulability and service guarantee for lowcriticality applications.
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