Energy Efficient Semi-Partitioned Scheduling for Embedded Multiprocessor Streaming Systems

Computer Systems, Imagery and Medi

Generalized strictly periodic scheduling analysis, resource optimization, and implementation of adaptive streaming applications

This thesis focuses on addressing four research problems in designing embedded streaming systems. Embedded streaming systems are those systems thatprocess a stream of input data coming from the environment and generate a stream of output data going into the environment. For many embeddedstreaming systems, the timing is a critical design requirement, in which the correct behavior depends on both the correctness of output data and on the time at which the data is produced. An embedded streaming system subjected to such a timing requirement is called a real-time system. Some examples of real-time embedded streaming systems can be found in various autonomous mobile systems, such as planes, self-driving cars, and drones. To handle the tight timing requirements of such real-time embedded streaming systems, modern embedded systems have been equipped with hardware platforms, the so-called Multi-Processor Systems-on-Chip (MPSoC), that contain multiple processors, memories, interconnections, and other hardware peripherals on a single chip, to benefit from parallel execution. To efficiently exploit the computational capacity of an MPSoC platform, a streaming application which is going to be executed on the MPSoC platform must be expressed primarily in a parallel fashion, i.e., the application is represented as a set of parallel executing and communicating tasks. Then, the main challenge is how to schedule the tasks spatially, i.e., task mapping, and temporally, i.e., task scheduling, on the MPSoC platform such that all timing requirements are satisfied while making efficient utilization of available resources (e.g, processors, memory, energy, etc.) on the platform. Another challenge is how to implement and run the mapped and scheduled application tasks on the MPSoC platform. This thesis proposes several techniques to address the aforementioned two challenges.NWOComputer Systems, Imagery and Medi

An Efficient Online Benefit-aware Multiprocessor Scheduling Technique for Soft Real-Time Tasks Using Online Choice of Approximation Algorithms

Maximizing the benefit gained by soft real-time tasks in many applications and embedded systems is highly needed to provide an acceptable QoS (Quality of Service). Examples of such applications and embedded systems include real-time medical monitoring systems, video- streaming servers, multiplayer video games, and mobile multimedia devices. In these systems, tasks are not equally critical (or beneficial). Each task comes with its own benefit-density function which can be different from the others’. The sooner a task completes, the more benefit it gains. In this work, a novel online benefit-aware preemptive approach is presented in order to enhance scheduling of soft real-time aperiodic and periodic tasks in multiprocessor systems. The objective of this work is enhancing the QoS by increasing the total benefit, while reducing flow times and deadline misses. This method prioritizes the tasks using their benefit-density functions, which imply their importance to the system, and schedules them in a real-time basis. The first model I propose is for scheduling soft real-time aperiodic tasks. An online choice of two approximation algorithms, greedy and load-balancing, is used in order to distribute the low- priority tasks among identical processors at the time of their arrival without using any statistics. The results of theoretical analysis and simulation experiments show that this method is able to maximize the gained benefit and decrease the computational complexity (compared to existing algorithms) while minimizing makespan with fewer missed deadlines and more balanced usage of processors. I also propose two more versions of this algorithm for scheduling SRT periodic tasks, with implicit and non-implicit deadlines, in addition to another version with a modified loadbalancing factor. The extensive simulation experiments and empirical comparison of these algorithms with the state of the art, using different utilization levels and various benefit density functions show that these new techniques outperform the existing ones. A general framework for benefit-aware multiprocessor scheduling in applications with periodic, aperiodic or mixed real-time tasks is also provided in this work.Computer Science, Department o

A Survey of Research into Mixed Criticality Systems

This survey covers research into mixed criticality systems that has been published since Vestal’s seminal paper in 2007, up until the end of 2016. The survey is organised along the lines of the major research areas within this topic. These include single processor analysis (including fixed priority and EDF scheduling, shared resources and static and synchronous scheduling), multiprocessor analysis, realistic models, and systems issues. The survey also explores the relationship between research into mixed criticality systems and other topics such as hard and soft time constraints, fault tolerant scheduling, hierarchical scheduling, cyber physical systems, probabilistic real-time systems, and industrial safety standards

A Survey and Comparative Study of Hard and Soft Real-time Dynamic Resource Allocation Strategies for Multi/Many-core Systems

Multi-/many-core systems are envisioned to satisfy the ever-increasing performance requirements of complex applications in various domains such as embedded and high-performance computing. Such systems need to cater to increasingly dynamic workloads, requiring efficient dynamic resource allocation strategies to satisfy hard or soft real-time constraints. This article provides an extensive survey of hard and soft real-time dynamic resource allocation strategies proposed since the mid-1990s and highlights the emerging trends for multi-/many-core systems. The survey covers a taxonomy of the resource allocation strategies and considers their various optimization objectives, which have been used to provide comprehensive comparison. The strategies employ various principles, such as market and biological concepts, to perform the optimizations. The trend followed by the resource allocation strategies, open research challenges, and likely emerging research directions have also been provided

A survey of techniques for reducing interference in real-time applications on multicore platforms

This survey reviews the scientific literature on techniques for reducing interference in real-time multicore systems, focusing on the approaches proposed between 2015 and 2020. It also presents proposals that use interference reduction techniques without considering the predictability issue. The survey highlights interference sources and categorizes proposals from the perspective of the shared resource. It covers techniques for reducing contentions in main memory, cache memory, a memory bus, and the integration of interference effects into schedulability analysis. Every section contains an overview of each proposal and an assessment of its advantages and disadvantages.This work was supported in part by the Comunidad de Madrid Government "Nuevas Técnicas de Desarrollo de Software de Tiempo Real Embarcado Para Plataformas. MPSoC de Próxima Generación" under Grant IND2019/TIC-17261

Multiprocessor System-on-Chips based Wireless Sensor Network Energy Optimization

Wireless Sensor Network (WSN) is an integrated part of the Internet-of-Things (IoT) used to monitor the physical or environmental conditions without human intervention. In WSN one of the major challenges is energy consumption reduction both at the sensor nodes and network levels. High energy consumption not only causes an increased carbon footprint but also limits the lifetime (LT) of the network. Network-on-Chip (NoC) based Multiprocessor System-on-Chips (MPSoCs) are becoming the de-facto computing platform for computationally extensive real-time applications in IoT due to their high performance and exceptional quality-of-service. In this thesis a task scheduling problem is investigated using MPSoCs architecture for tasks with precedence and deadline constraints in order to minimize the processing energy consumption while guaranteeing the timing constraints. Moreover, energy-aware nodes clustering is also performed to reduce the transmission energy consumption of the sensor nodes. Three distinct problems for energy optimization are investigated given as follows: First, a contention-aware energy-efficient static scheduling using NoC based heterogeneous MPSoC is performed for real-time tasks with an individual deadline and precedence constraints. An offline meta-heuristic based contention-aware energy-efficient task scheduling is developed that performs task ordering, mapping, and voltage assignment in an integrated manner. Compared to state-of-the-art scheduling our proposed algorithm significantly improves the energy-efficiency. Second, an energy-aware scheduling is investigated for a set of tasks with precedence constraints deploying Voltage Frequency Island (VFI) based heterogeneous NoC-MPSoCs. A novel population based algorithm called ARSH-FATI is developed that can dynamically switch between explorative and exploitative search modes at run-time. ARSH-FATI performance is superior to the existing task schedulers developed for homogeneous VFI-NoC-MPSoCs. Third, the transmission energy consumption of the sensor nodes in WSN is reduced by developing ARSH-FATI based Cluster Head Selection (ARSH-FATI-CHS) algorithm integrated with a heuristic called Novel Ranked Based Clustering (NRC). In cluster formation parameters such as residual energy, distance parameters, and workload on CHs are considered to improve LT of the network. The results prove that ARSH-FATI-CHS outperforms other state-of-the-art clustering algorithms in terms of LT.University of Derby, Derby, U

A weakly hard scheduling approach of partitioned scheduling on multiprocessor systems

Real-time systems or tasks can be classified into three categories, based on the “seriousness” of deadline misses – hard, soft and weakly hard real-time tasks. The consequences of a deadline miss of a hard real-time task can be prohibitively expensive because all the tasks must meet their deadlines whereas soft real-time tasks tolerate “some” deadline misses. Meanwhile, in a weakly hard real-time task, the distribution of its met and missed deadlines is stated and specified precisely. As real-time application systems increasingly come to be implemented upon multiprocessor environments, thus, this study applies multiprocessor scheduling approach for verification of weakly hard real-time tasks and to guaranteeing the timing requirements of the tasks. In fact, within the multiprocessor, the task allocation problem seem even harder than in uniprocessor case; thus, in order to cater that problem, the sufficient and efficient scheduling algorithm supported by accurate schedulability analysis technique is present to provide weakly hard real-time guarantees. In this paper, a weakly hard scheduling approach has been proposed and schedulability analysis of proposed approach consists of the partitioned multiprocessor scheduling techniques with solutions for the bin-packing problem, called R-BOUND-MP-NFRNS (R-BOUND-MP with next-fit-ring noscaling) combining with the exact analysis, named hyperperiod analysis and deadline models; weakly hard constraints and µ-pattern under static priority scheduling. Then, Matlab simulation tool is used in order to validate the result of analysis. From the evaluation results, it can be proven that the proposed approach outperforms the existing approaches in terms of satisfaction of the tasks deadlines