CPU Energy-Aware Parallel Real-Time Scheduling

Both energy-efficiency and real-time performance are critical requirements in many embedded systems applications such as self-driving car, robotic system, disaster response, and security/safety control. These systems entail a myriad of real-time tasks, where each task itself is a parallel task that can utilize multiple computing units at the same time. Driven by the increasing demand for parallel tasks, multi-core embedded processors are inevitably evolving to many-core. Existing work on real-time parallel tasks mostly focused on real-time scheduling without addressing energy consumption. In this paper, we address hard real-time scheduling of parallel tasks while minimizing their CPU energy consumption on multicore embedded systems. Each task is represented as a directed acyclic graph (DAG) with nodes indicating different threads of execution and edges indicating their dependencies. Our technique is to determine the execution speeds of the nodes of the DAGs to minimize the overall energy consumption while meeting all task deadlines. It incorporates a frequency optimization engine and the dynamic voltage and frequency scaling (DVFS) scheme into the classical real-time scheduling policies (both federated and global) and makes them energy-aware. The contributions of this paper thus include the first energy-aware online federated scheduling and also the first energy-aware global scheduling of DAGs. Evaluation using synthetic workload through simulation shows that our energy-aware real-time scheduling policies can achieve up to 68% energy-saving compared to classical (energy-unaware) policies. We have also performed a proof of concept system evaluation using physical hardware demonstrating the energy efficiency through our proposed approach

Developing an energy efficient real-time system

Increasing number of battery operated devices creates a need for energy-efficient real-time operating system for such devices. Designing a truly energy-efficient system is a multi-staged effort; this thesis consists of three main tasks that address different aspects of energy efficiency of a real-time system (RTS). The first chapter introduces an energy-efficient algorithm that alternates processor frequency using DVFS to schedule tasks on cores. Speed profiles is calculated for every task that gives information about how long a task would run for and at what processor speed. We pair tasks with similar speed profiles to give us a resultant merged speed profile that can be efficient scheduled on a cluster. Experiments carried out on ODROID-XU3 are compared with a reference approach that provides energy saving of up to 20%. The second chapter proposes power-aware techniques to segregate a task set over a heterogeneous platform such that the overall energy consumption is minimized. With the help of calculated speed profiles, second contribution of this work feasibly partitions a given task set into individual sets for a cluster based homogeneous platform. Various heuristics are proposed that are compared against a baseline approach with simulation results. The final chapter of this thesis focuses on the importance of having an underlying energy-efficient operating system. We discuss an energy-efficient way of porting a real-time operating system (RTOS), QP, over TMS320F28377S along with modifications to make the Operating System (OS) consume minimal energy for its operation --Abstract, page iii

Energy-Efficient Multi-Core Scheduling for Real-Time DAG Tasks

In this work, we study energy-aware real-time scheduling of a set of sporadic Directed Acyclic Graph (DAG) tasks with implicit deadlines. While meeting all real-time constraints, we try to identify the best task allocation and execution pattern such that the average power consumption of the whole platform is minimized. To the best of our knowledge, this is the first work that addresses the power consumption issue in scheduling multiple DAG tasks on multi-cores and allows intra-task processor sharing. We first adapt the decomposition-based framework for federated scheduling and propose an energy-sub-optimal scheduler. Then we derive an approximation algorithm to identify processors to be merged together for further improvements in energy-efficiency and to prove the bound of the approximation ratio. We perform a simulation study to demonstrate the effectiveness and efficiency of the proposed scheduling. The simulation results show that our algorithms achieve an energy saving of 27% to 41% compared to existing DAG task schedulers

Power, Energy, and Thermal Management for Clustered Manycores

Efficient and effective system-level power, energy, and thermal management are very important issues in modern computing systems, for which clustered architectures with multiple voltage islands are an expected compromise between global and per-core DVFS. In this dissertation, we focus on two of the most relevant problems for such architectures, specifically, optimizing performance under power/thermal constraints, and minimizing energy under performance constraints

A Control-Theoretic Design And Analysis Framework For Resilient Hard Real-Time Systems

We introduce a new design metric called system-resiliency which characterizes the maximum unpredictable external stresses that any hard-real-time performance mode can withstand. Our proposed systemresiliency framework addresses resiliency determination for real-time systems with physical and hardware limitations. Furthermore, our framework advises the system designer about the feasible trade-offs between external system resources for the system operating modes on a real-time system that operates in a multi-parametric resiliency environment. Modern multi-modal real-time systems degrade the system’s operational modes as a response to unpredictable external stimuli. During these mode transitions, real-time systems should demonstrate a reliable and graceful degradation of service. Many control-theoretic-based system design approaches exist. Although they permit real-time systems to operate under various physical constraints, none of them allows the system designer to predict the system-resiliency over multi-constrained operating environment. Our framework fills this gap; the proposed framework consists of two components: the design-phase and runtime control. With the design-phase analysis, the designer predicts the behavior of the real-time system for variable external conditions. Also, the runtime controller navigates the system to the best desired target using advanced control-theoretic techniques. Further, our framework addresses the system resiliency of both uniprocessor and multicore processor systems. As a proof of concept, we first introduce a design metric called thermal-resiliency, which characterizes the maximum external thermal stress that any hard-real-time performance mode can withstand. We verify the thermal-resiliency for the external thermal stresses on a uniprocessor system through a physical testbed. We show how to solve some of the issues and challenges of designing predictable real-time systems that guarantee hard deadlines even under transitions between modes in an unpredictable thermal environment where environmental temperature may dynamically change using our new metric. We extend the derivation of thermal-resiliency to multicore systems and determine the limitations of external thermal stress that any hard-real-time performance mode can withstand. Our control-theoretic framework allows the system designer to allocate asymmetric processing resources upon a multicore proiii cessor and still maintain thermal constraints. In addition, we develop real-time-scheduling sub-components that are necessary to fully implement our framework; toward this goal, we investigate the potential utility of parallelization for meeting real-time constraints and minimizing energy. Under malleable gang scheduling of implicit-deadline sporadic tasks upon multiprocessors, we show the non-necessity of dynamic voltage/frequency regarding optimality of our scheduling problem. We adapt the canonical schedule for DVFS multiprocessor platforms and propose a polynomial-time optimal processor/frequency-selection algorithm. Finally, we verify the correctness of our framework through multiple measurable physical and hardware constraints and complete our work on developing a generalized framework

Power-Aware Job Dispatching in High Performance Computing Systems

This works deals with the power-aware job dispatching problem in supercomputers; broadly speaking the dispatching consists of assigning finite capacity resources to a set of activities, with a special concern toward power and energy efficient solutions. We introduce novel optimization approaches to address its multiple aspects. The proposed techniques have a broad application range but are aimed at applications in the field of High Performance Computing (HPC) systems. Devising a power-aware HPC job dispatcher is a complex, where contrasting goals must be satisfied. Furthermore, the online nature of the problem request that solutions must be computed in real time respecting stringent limits. This aspect historically discouraged the usage of exact methods and favouring instead the adoption of heuristic techniques. The application of optimization approaches to the dispatching task is still an unexplored area of research and can drastically improve the performance of HPC systems. In this work we tackle the job dispatching problem on a real HPC machine, the Eurora supercomputer hosted at the Cineca research center, Bologna. We propose a Constraint Programming (CP) model that outperforms the dispatching software currently in use. An essential element to take power-aware decisions during the job dispatching phase is the possibility to estimate jobs power consumptions before their execution. To this end, we applied Machine Learning techniques to create a prediction model that was trained and tested on the Euora supercomputer, showing a great prediction accuracy. Then we finally develop a power-aware solution, considering the same target machine, and we devise different approaches to solve the dispatching problem while curtailing the power consumption of the whole system under a given threshold. We proposed a heuristic technique and a CP/heuristic hybrid method, both able to solve practical size instances and outperform the current state-of-the-art techniques

Energy-Aware Real-Time Scheduling on Heterogeneous and Homogeneous Platforms in the Era of Parallel Computing

Multi-core processors increasingly appear as an enabling platform for embedded systems, e.g., mobile phones, tablets, computerized numerical controls, etc. The parallel task model, where a task can execute on multiple cores simultaneously, can efficiently exploit the multi-core platform\u27s computational ability. Many computation-intensive systems (e.g., self-driving cars) that demand stringent timing requirements often evolve in the form of parallel tasks. Several real-time embedded system applications demand predictable timing behavior and satisfy other system constraints, such as energy consumption. Motivated by the facts mentioned above, this thesis studies the approach to integrating the dynamic voltage and frequency scaling (DVFS) policy with real-time embedded system application\u27s internal parallelism to reduce the worst-case energy consumption (WCEC), an essential requirement for energy-constrained systems. First, we propose an energy-sub-optimal scheduler, assuming the per-core speed tuning feature for each processor. Then we extend our solution to adapt the clustered multi-core platform, where at any given time, all the processors in the same cluster run at the same speed. We also present an analysis to exploit a task\u27s probabilistic information to improve the average-case energy consumption (ACEC), a common non-functional requirement of embedded systems. Due to the strict requirement of temporal correctness, the majority of the real-time system analysis considered the worst-case scenario, leading to resource over-provisioning and cost. The mixed-criticality (MC) framework was proposed to minimize energy consumption and resource over-provisioning. MC scheduling has received considerable attention from the real-time system research community, as it is crucial to designing safety-critical real-time systems. This thesis further addresses energy-aware scheduling of real-time tasks in an MC platform, where tasks with varying criticality levels (i.e., importance) are integrated into a common platform. We propose an algorithm GEDF-VD for scheduling MC tasks with internal parallelism in a multiprocessor platform. We also prove the correctness of GEDF-VD, provide a detailed quantitative evaluation, and reported extensive experimental results. Finally, we present an analysis to exploit a task\u27s probabilistic information at their respective criticality levels. Our proposed approach reduces the average-case energy consumption while satisfying the worst-case timing requirement