Scheduling techniques to improve the worst-case execution time of real-time parallel applications on heterogeneous platforms

The key to providing high performance and energy-efficient execution for hard real-time applications is the time predictable and efficient usage of heterogeneous multiprocessors. However, schedulability analysis of parallel applications executed on unrelated heterogeneous multiprocessors is challenging and has not been investigated adequately by earlier works. The unrelated model is suitable to represent many of the multiprocessor platforms available today because a task (i.e., sequential code) may exhibit a different work-case-execution-time (WCET) on each type of processor on an unrelated heterogeneous multiprocessors platform. A parallel application can be realistically modeled as a directed acyclic graph (DAG), where the nodes are sequential tasks and the edges are dependencies among the tasks. This thesis considers a sporadic DAG model which is used broadly to analyze and verify the real-time requirements of parallel applications. A global work-conserving scheduler can efficiently utilize an unrelated platform by executing the tasks of a DAG on different processor types. However, it is challenging to compute an upper bound on the worst-case schedule length of the DAG, called makespan, which is used to verify whether the deadline of a DAG is met or not. There are two main challenges. First, because of the heterogeneity of the processors, the WCET for each task of the DAG depends on which processor the task is executing on during actual runtime. Second, timing anomalies are the main obstacle to compute the makespan even for the simpler case when all the processors are of the same type, i.e., homogeneous multiprocessors. To that end, this thesis addresses the following problem: How we can schedule multiple sporadic DAGs on unrelated multiprocessors such that all the DAGs meet their deadlines. Initially, the thesis focuses on homogeneous multiprocessors that is a special case of unrelated multiprocessors to understand and tackle the main challenge of timing anomalies. A novel timing-anomaly-free scheduler is proposed which can be used to compute the makespan of a DAG just by simulating the execution of the tasks based on this proposed scheduler. A set of representative task-based parallel OpenMP applications from the BOTS benchmark suite are modeled as DAGs to investigate the timing behavior of real-world applications. A simulation framework is developed to evaluate the proposed method. Furthermore, the thesis targets unrelated multiprocessors and proposes a global scheduler to execute the tasks of a single DAG to an unrelated multiprocessors platform. Based on the proposed scheduler, methods to compute the makespan of a single DAG are introduced. A set of representative parallel applications from the BOTS benchmark suite are modeled as DAGs that execute on unrelated multiprocessors. Furthermore, synthetic DAGs are generated to examine additional structures of parallel applications and various platform capabilities. A simulation framework that simulates the execution of the tasks of a DAG on an unrelated multiprocessor platform is introduced to assess the effectiveness of the proposed makespan computations. Finally, based on the makespan computation of a single DAG this thesis presents the design and schedulability analysis of global and federated scheduling of sporadic DAGs that execute on unrelated multiprocessors

Workflow Scheduling Techniques and Algorithms in IaaS Cloud: A Survey

In the modern era, workflows are adopted as a powerful and attractive paradigm for expressing/solving a variety of applications like scientific, data intensive computing, and big data applications such as MapReduce and Hadoop. These complex applications are described using high-level representations in workflow methods. With the emerging model of cloud computing technology, scheduling in the cloud becomes the important research topic. Consequently, workflow scheduling problem has been studied extensively over the past few years, from homogeneous clusters, grids to the most recent paradigm, cloud computing. The challenges that need to be addressed lies in task-resource mapping, QoS requirements, resource provisioning, performance fluctuation, failure handling, resource scheduling, and data storage. This work focuses on the complete study of the resource provisioning and scheduling algorithms in cloud environment focusing on Infrastructure as a service (IaaS). We provided a comprehensive understanding of existing scheduling techniques and provided an insight into research challenges that will be a possible future direction to the researchers

Edge Generation Scheduling for DAG Tasks using Deep Reinforcement Learning

Directed acyclic graph (DAG) tasks are currently adopted in the real-time domain to model complex applications from the automotive, avionics, and industrial domain that implement their functionalities through chains of intercommunicating tasks. This paper studies the problem of scheduling real-time DAG tasks by presenting a novel schedulability test based on the concept of trivial schedulability. Using this schedulability test, we propose a new DAG scheduling framework (edge generation scheduling -- EGS) that attempts to minimize the DAG width by iteratively generating edges while guaranteeing the deadline constraint. We study how to efficiently solve the problem of generating edges by developing a deep reinforcement learning algorithm combined with a graph representation neural network to learn an efficient edge generation policy for EGS. We evaluate the effectiveness of the proposed algorithm by comparing it with state-of-the-art DAG scheduling heuristics and an optimal mixed-integer linear programming baseline. Experimental results show that the proposed algorithm outperforms the state-of-the-art by requiring fewer processors to schedule the same DAG tasks.Comment: Under revie

Bounding the execution time of parallel applications on unrelated multiprocessors

Heterogeneous multiprocessors, that consist of processor types with different execution capabilities, are critical today, and in future, to offer high performance and high energy efficiency. In order to use them in hard real-time systems to support parallel processing, a tight estimation of the upper bound on the completion time (WCET) of parallel applications is needed. This paper presents, for the first time, a closed-form solution for the calculation of the WCET for task-based parallel applications modeled as directed acyclic-graphs (DAG) using the general unrelated multiprocessor model that is capable of modeling a wide range of heterogeneous multiprocessor platforms. The paper contributes with a polynomial time algorithm to calculate the WCET (i.e., makespan) for the unrelated model. In addition, it presents simulation results that are based on modeling a set of representative OpenMP task-based parallel applications from the BOTS benchmark suite

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

Fault Tolerant Scheduling of Precedence Task Graphs on Heterogeneous Platforms

Fault tolerance and latency are important requirements in several applications which are time critical in nature: such applications require guaranties in terms of latency, even when processors are subject to failures. In this paper, we propose a fault tolerant scheduling heuristic for mapping precedence task graphs on heterogeneous systems. Our approach is based on an active replication scheme, capable of supporting $\varepsilon$ arbitrary fail-silent (fail-stop) processor failures, hence valid results will be provided even if $\varepsilon$ processors fail. We focus on a bi-criteria approach, where we aim at minimizing the latency given a fixed number of failures supported in the system, or the other way round. Major achievements include a low complexity, and a drastic reduction of the number of additional communications induced by the replication mechanism. Experimental results demonstrate that our heuristics, despite their lower complexity, outperform their direct competitor, the FTBAR scheduling algorithm[8].La tolérance aux pannes et la latence sont deux critères importants pour plusieurs applications qui sont critiques par nature. Ce type d’applications exige des garanties en terme de temps de latence, même lorsque les processeurs sont sujets aux pannes. Dans ce rapport, nous proposons une heuristique tolérante aux pannes pour l’ordonnancement de graphes de tâches sur des systèmes hétérogènes. Notre approche est basée sur un mécanisme de réplication active, capable de supporter " pannes arbitraires de type silence sur défaillance. En d’autres termes, des résultats valides seront fournis même si " processeurs tombent en panne. Nous nous concentrons sur une approche bi-critère, où nous avons pour objectif de minimiser le temps de latence pour un nombre donné (fixé) de pannes tolérées dans le système, ou l’inverse. Les principales contributions incluent une faible complexité en temps d’exécution, et une réduction importante du nombre de communications induites par le mécanisme de réplication.Les résultats expérimentaux montrent que notre algorithme, en dépit de sa faible complexité temporelle, est meilleur que son direct compétiteur,l’algorithme FTBA