U-EDF: An Unfair But Optimal Multiprocessor Scheduling Algorithm for Sporadic Tasks

A multiprocessor scheduling algorithm named U-EDF, was presented in [1] for the scheduling of periodic tasks with implicit deadlines. It was claimed that U-EDF is optimal for periodic tasks (i.e. it can meet all deadlines of every schedulable task set) and extensive simulations showed a drastic improvement in the number of task preemptions and migrations in comparison to state-of-the-art optimal algorithms. However, there was no proof of its optimality and U-EDF was not designed to schedule sporadic tasks. In this work, we propose a generalization of U-EDF for the scheduling of sporadic tasks with implicit deadlines, and we prove its optimality. Contrarily to all other existing optimal multiprocessor scheduling algorithms for sporadic tasks, U-EDF is not based on the fairness property. Instead, it extends the main principles of EDF so that it achieves optimality while benefiting from a substantial reduction in the number of preemptions and migrations. © 2012 IEEE.SCOPUS: cp.pinfo:eu-repo/semantics/publishe

Energy Efficient Scheduling for Real-Time Systems

The goal of this dissertation is to extend the state of the art in real-time scheduling algorithms to achieve energy efficiency. Currently, Pfair scheduling is one of the few scheduling frameworks which can optimally schedule a periodic real-time taskset on a multiprocessor platform. Despite the theoretical optimality, there exist large concerns about efficiency and applicability of Pfair scheduling in practical situations. This dissertation studies and proposes solutions to such efficiency and applicability concerns. This dissertation also explores temperature aware energy management in the domain of real-time scheduling. The thesis of this dissertation is: the implementation efficiency of Pfair scheduling algorithms can be improved. Further, temperature awareness of a real-time system can be improved while considering variation of task execution times to reduce energy consumption. This thesis is established through research in a number of directions. First, we explore the applicability of Dynamic Voltage and Frequency Scaling (DVFS) feature in the underlying platform, within Pfair scheduled systems. We propose techniques to reduce energy consumption in Pfair scheduling by using DVFS. Next, we explore the problem of quantum size selection in Pfair scheduled system so that runtime overheads are minimized. We also propose a hardware design for a central Pfair scheduler core in a multiprocessor system to minimized the overheads and energy consumption of Pfair scheduling. Finally, we propose a temperature aware energy management scheme for tasks with varying execution times

Placement, ordonnancement et mécanismes de migration de tâches temps-réel pour des architectures distribuées multicoeurs

Les systèmes temps-réel embarqués critiques intègrent un nombre croissant de fonctionnalités comme le montrent les domaines de l'automobile ou de l'aéronautique. Ces systèmes doivent offrir un niveau maximal de sûreté de fonctionnement en disposant des mécanismes pour traiter les défaillances éventuelles et doivent être également performants, avec le respect de contraintes temps-réel strictes. Ces systèmes sont en outre contraints par leur nature embarquée : les ressources sont limitées, tels que par exemple leur espace mémoire et leur capacité de calcul. Dans cette thèse, nous traitons deux problématiques principales de ce type de systèmes. La première porte sur la manière d'apporter une meilleure tolérance aux fautes dans les systèmes temps-réel distribués subissant des défaillances matérielles multiples et permanentes. Ces systèmes sont souvent conçus avec une allocation statique des tâches. Une approche plus flexible effectuant des reconfigurations est utile si elle permet d'optimiser l'allocation à chaque défaillance rencontrée, pour les ressources restantes. Nous proposons une telle approche hors-ligne assurant un dimensionnement adapté pour prendre en compte les ressources nécessaires à l'exécution de ces actions. Ces reconfigurations peuvent demander une réallocation des tâches ou répliques si l'espace mémoire local est limité. Dans un contexte temps-réel strict, nous définissons notamment des mécanismes et des techniques de migration garantissant l'ordonnançabilité globale du système. La deuxième problématique se focalise sur l'optimisation de l'exécution des tâches au niveau local dans un contexte multicoeurs préemptif. Nous proposons une méthode d'ordonnancement optimal disposant d'une meilleure extensibilité que les approches existantes en minimisant les surcoûts : le nombre de changements de contexte préemptions et migrations locales) et la complexité de l'ordonnanceur. ABSTRACT : Critical real-time embedded systems are integrating an increasing number of functionalities, as shown in automotive domain or aeronautics. These systems require high dependability including mechanisms to handle possible failures and have to be effective, meeting hard real-time constraints. These systems are also constrained by their embedded nature : resources are limited, such as their memory and their computing capacities. In this thesis, we focus on two main problems for this type of systems. The first one is about a way to bring a better fault-tolerance in distributed real-time systems when multiple and permanent hardware failures can occur. In classical systems, the design is limited to a static task assignment. A more flexible approach exploiting reconfigurations is useful if it allows to optimize assignment at each failure for the remaining resources. We propose an off-line approach to obtain an adapted sizing taking into account necessary resources to execute these actions. These reconfigurations may require to reallocate tasks or replicas if memory capacities are limited. In a hard real-time context, we define mechanisms and migration techniques to guarantee global schedulability of the system. The second problem focus on optimizing performance to run tasks at a local level in a multicore preemptive context. We propose an optimal scheduling method allowing a better scalability than existing approaches by minimizing overheads : the number of context switches (local preemptions and migrations) and the scheduler complexity

Placement, ordonnancement et mécanismes de migration de tâches temps-réel pour des architectures distribuées multicoeurs

Les systèmes temps-réel embarqués critiques intègrent un nombre croissant de fonctionnalités comme le montrent les domaines de l'automobile ou de l'aéronautique. Ces systèmes doivent offrir un niveau maximal de sûreté de fonctionnement en disposant des mécanismes pour traiter les défaillances éventuelles et doivent être également performants, avec le respect de contraintes temps-réel strictes. Ces systèmes sont en outre contraints par leur nature embarquée : les ressources sont limitées, tels que par exemple leur espace mémoire et leur capacité de calcul. Dans cette thèse, nous traitons deux problématiques principales de ce type de systèmes. La première porte sur la manière d'apporter une meilleure tolérance aux fautes dans les systèmes temps-réel distribués subissant des défaillances matérielles multiples et permanentes. Ces systèmes sont souvent conçus avec une allocation statique des tâches. Une approche plus exible effectuant des recon gurations est utile si elle permet d'optimiser l'allocation à chaque défaillance rencontrée, pour les ressources restantes. Nous proposons une telle approche hors-ligne assurant un dimensionnement adapté pour prendre en compte les ressources nécessaires à l'exécution de ces actions. Ces recon gurations peuvent demander une réallocation des tâches ou répliques si l'espace mémoire local est limité. Dans un contexte temps-réel strict, nous dé nissons notamment des mécanismes et des techniques de migration garantissant l'ordonnançabilité globale du système. La deuxième problématique se focalise sur l'optimisation de l'exécution des tâches au niveau local dans un contexte multicoeurs préemptif. Nous proposons une méthode d'ordonnancement optimal disposant d'une meilleure extensibilité que les approches existantes en minimisant les surcoûts : le nombre de changements de contexte préemptions et migrations locales) et la complexité de l'ordonnanceurCritical real-time embedded systems are integrating an increasing number of functionalities, as shown in automotive domain or aeronautics. These systems require high dependability including mechanisms to handle possible failures and have to be effective, meeting hard real-time constraints. These systems are also constrained by their embedded nature : resources are limited, such as their memory and their computing capacities. In this thesis, we focus on two main problems for this type of systems. The rst one is about a way to bring a better fault-tolerance in distributed real-time systems when multiple and permanent hardware failures can occur. In classical systems, the design is limited to a static task assignment. A more exible approach exploiting recon gurations is useful if it allows to optimize assignment at each failure for the remaining resources. We propose an off-line approach to obtain an adapted sizing taking into account necessary resources to execute these actions. These recon gurations may require to reallocate tasks or replicas if memory capacities are limited. In a hard real-time context, we de ne mechanisms and migration techniques to guarantee global schedulability of the system. The second problem focus on optimizing performance to run tasks at a local level in a multicore preemptive context. We propose an optimal scheduling method allowing a better scalability than existing approaches by minimizing overheads : the number of context switches (local preemptions and migrations) and the scheduler complexityTOULOUSE-INP (315552154) / SudocSudocFranceF

Energy and Reliability Management in Parallel Real-Time Systems

Historically, slack time in real-time systems has been used as temporal redundancy by rollback recovery schemes to increase system reliability in the presence of faults. However, with advancedtechnologies, slack time can also be used by energy management schemes to save energy. For reliable real-time systems where higher levels of reliability are as important as lower levels of energy consumption, centralized management of slack time is desired.For frame-based parallel real-time applications, energy management schemes are first explored. Although the simple static power management that evenly allocates static slack over a schedule isoptimal for uni-processor systems, it is not optimal for parallel systems due to different levels of parallelism in a schedule. Taking parallelism variations into consideration, a parallel static power management scheme is proposed. When dynamic slack is considered,assuming global scheduling strategies, slack shifting and sharing schemes as well as speculation schemes are proposed for moreenergy savings.For simultaneous management of power and reliability, checkpointing techniques are first deployed to efficiently use slack time and theoptimal numbers of checkpoints needed to minimize energy consumption or to maximize system reliability are explored. Then, an energyefficient optimistic modular redundancy scheme is addressed. Finally, a framework that encompasses energy and reliability management isproposed for obtaining optimal redundant configurations. While exploring the trade-off between energy and reliability, the effects ofvoltage scaling on fault rates are considered

An optimal multiprocessor real-time scheduling algorithm

An optimal scheduling algorithm is described that feasibly schedules a set ofmperiodic tasks onnprocessors before their respective deadlines, if the task set satisfies certain conditions. The complexity of this scheduling algorithm in terms of the number of scheduled tasks and the number of processors and upper bounds on the number of preemptions in a given time interval and for any single task is also derived. The optimal algorithm is shown to be particularly useful when schedules are built from the integral flow values obtained from the corresponding maximum flow network