Dynamic scheduling techniques for adaptive applications on real-time embedded systems

Ph.DDOCTOR OF PHILOSOPH

Energy-Efficient, Reliable and QoS-Aware Task Mapping on Cyber-Physical Systems

Cyber-Physical Systems (CPS) usually consist of a set of embedded systems (CPS nodes) connected through wireless communication, providing multiple functionalities that support different types of applications. During CPS deployment, application tasks are mapped on the CPS nodes with the objective of enhancing real-time performance, energy efficiency, and execution reliability. To satisfy these requirements, effective task mapping approaches should be designed based on different types of tasks, platforms, applications, and system requirements. In this paper, we provide a comprehensive survey regarding the task mapping methods in CPS

Energy-Quality-Time Optimized Task Mapping on DVFS-enabled Multicores

International audienceMulticore architectures have great potential for energy-constrained embedded systems, such as energy-harvestingwireless sensor networks. Some embedded applications, especially the real-time ones, can be modeled as imprecise computation tasks. A task is divided into a mandatory subtask that provides a baseline Quality-of-Service (QoS) and an optional subtask that refines the result to increase the QoS. Combining dynamic voltage and frequency scaling, task allocation and task adjustment, we can maximize the system QoS under real-time and energy supply constraints. However, the nonlinear and combinatorial nature of this problem makes it difficult to solve. This work first formulates a mixed-integer non-linear programming problem to concurrently carry out task-to-processor allocation, frequencyto- task assignment and optional task adjustment. We provide a mixed-integer linear programming form of this formulation without performance degradation and we propose a novel decomposition algorithm to provide an optimal solution withreduced computation time compared to state-of-the-art optimal approaches (22.6% in average). We also propose a heuristic version that has negligible computation tim

Deep intelligence as a service: A real-time scheduling perspective

This thesis presents a new type of cloud service, called {\em deep intelligence as a service\/}, that is expected to become increasingly common in the near future to support emerging "smart" embedded applications. This work offers a real-time scheduling model motivated by the special needs of embedded applications that use this service. A simple run-time scheduler is proposed for the server and prove an approximation bound in terms of application-perceived service utility. The service is implemented on representative device hardware and tested with a machine vision application illustrating the advantages of our scheme. The work is motivated by the proliferation of increasingly ubiquitous but resource-constrained embedded devices (often referred to as the Internet of Things -- IoT -- devices) and the growing desire to endow them with advanced interaction capabilities, such as voice recognition or machine vision. The trend suggests that machine intelligence will be increasingly offloaded to cloud or edge services that will offer advanced capabilities to the otherwise simple devices. New services will feature farms of complex trainable (or pre-trained) classifiers that client-side applications can send data to in order to gain certain types of advanced functionality, such as face recognition, voice command recognition, gesture recognition, or other. These new services will revolutionize human interaction with their physical environment, but may impose interesting real-time scheduling challenges in order to maintain responsiveness while maximizing service quality. This work includes challenges, designs for an efficient real-time scheduling algorithm for the new machine intelligence service, and evaluation on an implemented prototype

Quality estimation and optimization of adaptive stereo matching algorithms for smart vehicles

Stereo matching is a promising approach for smart vehicles to find the depth of nearby objects. Transforming a traditional stereo matching algorithm to its adaptive version has potential advantages to achieve the maximum quality (depth accuracy) in a best-effort manner. However, it is very challenging to support this adaptive feature, since (1) the internal mechanism of adaptive stereo matching (ASM) has to be accurately modeled, and (2) scheduling ASM tasks on multiprocessors to generate the maximum quality is difficult under strict real-time constraints of smart vehicles. In this article, we propose a framework for constructing an ASM application and optimizing its output quality on smart vehicles. First, we empirically convert stereo matching into ASM by exploiting its inherent characteristics of disparity–cycle correspondence and introduce an exponential quality model that accurately represents the quality–cycle relationship. Second, with the explicit quality model, we propose an efficient quadratic programming-based dynamic voltage/frequency scaling (DVFS) algorithm to decide the optimal operating strategy, which maximizes the output quality under timing, energy, and temperature constraints. Third, we propose two novel methods to efficiently estimate the parameters of the quality model, namely location similarity-based feature point thresholding and street scenario-confined CNN prediction. Results show that our DVFS algorithm achieves at least 1.61 times quality improvement compared to the state-of-the-art techniques, and average parameter estimation for the quality model achieves 96.35% accuracy on the straight road