Response Time Bounds for DAG Tasks with Arbitrary Intra-Task Priority Assignment

Most parallel real-time applications can be modeled as directed acyclic graph (DAG) tasks. Intra-task priority assignment can reduce the nondeterminism of runtime behavior of DAG tasks, possibly resulting in a smaller worst-case response time. However, intra-task priority assignment incurs dependencies between different parts of the graph, making it a challenging problem to compute the response time bound. Existing work on intra-task task priority assignment for DAG tasks is subject to the constraint that priority assignment must comply with the topological order of the graph, so that the response time bound can be computed in polynomial time. In this paper, we relax this constraint and propose a new method to compute response time bound of DAG tasks with arbitrary priority assignment. With the benefit of our new method, we present a simple but effective priority assignment policy, leading to smaller response time bounds. Comprehensive evaluation with both single-DAG systems and multi-DAG systems demonstrates that our method outperforms the state-of-the-art method with a considerable margin

Multi-Path Bound for DAG Tasks

This paper studies the response time bound of a DAG (directed acyclic graph) task. Recently, the idea of using multiple paths to bound the response time of a DAG task, instead of using a single longest path in previous results, was proposed and leads to the so-called multi-path bound. Multi-path bounds can greatly reduce the response time bound and significantly improve the schedulability of DAG tasks. This paper derives a new multi-path bound and proposes an optimal algorithm to compute this bound. We further present a systematic analysis on the dominance and the sustainability of three existing multi-path bounds and the proposed multi-path bound. Our bound theoretically dominates and empirically outperforms all existing multi-path bounds. What's more, the proposed bound is the only multi-path bound that is proved to be self-sustainable

Deep Learning on Energy Harvesting IoT Devices: Survey and Future Challenges

Internet-of-Things (IoT) devices are becoming both intelligent and green. On the one hand, Deep Neural Network (DNN) compression techniques make it possible to run deep learning applications on devices equipped with low-end microcontrollers (MCUs). By performing deep learning on IoT devices, in-situ decision-making can be made, which can improve the responsiveness of such devices to the environment and reduce data uploading to edge servers or clouds to save valuable network bandwidth. On the other hand, many IoT devices in the future will be powered by energy harvesters instead of batteries to reduce environmental pollution and achieve permanent service free of battery maintenance. As the energy output of energy harvesters is tiny and unstable, energy harvesting IoT (EH-IoT) devices will experience frequent power failures during their execution, making the software task hard to progress. The deep learning tasks running on such devices must face this challenge and, at the same time, ensure satisfactory execution efficiency. We believe deploying deep learning on EH-IoT devices that execute intermittently will be a challenging yet promising research direction. To motivate research in this direction, this paper summarizes existing solutions and provides an in-depth discussion of future challenges that deserve further investigation. With IoT devices becoming more intelligent and green, DNN inference on EH-IoT devices will generate a much more significant impact in the future in academia and industry

A multi-step estimation approach for optimal control strategies of interconnected systems with weakly connected topology

This paper studies optimal linear quadratic regulation (LQR) problem of discrete-time interconnected systems (ISs) defined over a weakly connected graph. Subsystems in an IS share information based on the topology of the system. The main challenge of this work in comparison to the standard LQR problem, stems from that a subsystem may never acquire information from some other subsystems, due to the weakly connected topology. In this paper, a multiple-step estimation approach is proposed to analytically design the optimal controller for ISs with weakly connected topology. Also, the optimal value of the cost function is explicitly derived. Finally, the effectiveness of the proposed method is illustrated by simulations using a connected vehicle system.Agency for Science, Technology and Research (A*STAR)This research is partially supported by the PolyU Start-up Fund, Hong Kong for RAPs under the Strategic Hiring Scheme ‘‘Managing Energy Consumption for Green IoT (ZVUT)’’; and partially supported by A*STAR under its RIE2020 Advanced Manufacturing and Engineering (AME) Industry Alignment Fund, Singapore C Pre Positioning (IAF-PP)(Award A19D6a0053)

FIFO Cache Analysis for WCET Estimation: A Quantitative Approach

Abstract—Although most previous work in cache analysis for WCET estimation assumes the LRU replacement policy, in practise more processors use simpler non-LRU policies for lower cost, power consumption and thermal output. This paper focuses on the analysis of FIFO, one of the most widely used cache replacement policies. Previous analysis techniques for FIFO caches are based on the same framework as for LRU caches using qualitative always-hit/always-miss classifications. This approach, though works well for LRU caches, is not suitable to analyze FIFO and usually leads to poor WCET estimation quality. In this paper, we propose a quantitative approach for FIFO cache analysis. Roughly speaking, the proposed quantitative analysis derives an upper bound on the “miss ratio ” of an instruction (set), which can better capture the FIFO cache behavior and support more accurate WCET estimations. Experiments with benchmarks show that our proposed quantitative FIFO analysis can drastically improve the WCET estimation accuracy over pervious techniques (the average overestimation ratio is reduced from around 70 % to 10 % under typical setting). I

Flexible Mixed-Criticality Scheduling with Dynamic Slack Management

Mixed-criticality (MC) system has attracted a lot of research attention in the past few years for its resource efficiency. Recent work attempted to provide a new MC model, the so-called Flexible Mixed-Criticality (FMC) task model, to relax the pessimistic assumptions in classic MC scheduling. However, in FMC, the behavior of MC tasks is still analyzed in offline stage. The run-time behavior such as dynamic slack has not yet been studied in FMC scheduling framework. In this paper, we present a utilization-based slack scheduling framework for FMC tasks. In particular, we monitor task execution on run time and collect dynamic slacks generated by task early completion. And these slacks can then be used either by high-criticality tasks to reduce mode-switches, or by low-criticality tasks so that less suspensions are triggered with more execution time, and thus quality of service is improved. We evaluate our approach with extensive simulations, and experiment results demonstrate the effectiveness of the proposed approaches

Photovoltaic Array Fault Detection by Automatic Reconfiguration

Photovoltaic (PV) system output electricity is related to PV cells’ conditions, with the PV faults decreasing the efficiency of the PV system and even causing a possible source of fire. In industrial production, PV fault detection is typically laborious manual work. In this paper, we present a method that can automatically detect PV faults. Based on the observation that different faults will have different impacts on a PV system, we propose a method that systematically and iteratively reconfigures the PV array until the faults are located based on the specific current-voltage (I-V) curve of the (sub-)array. Our method can detect several main types of faults including open-circuit faults, mismatch faults, short circuit faults, etc. We evaluate our methods by Matlab/Simulink-based simulation. The results show that the proposed methods can accurately detect and classify the different faults occurring in a PV system