Response Time Analysis for Thermal-Aware Real-Time Systems Under Fixed-Priority Scheduling

International audienceThis paper investigates schedulability analysis for thermal-aware real-time systems. Thermal constraints are becoming more and more critical in new generation miniaturized embedded systems, e.g. Medicals implants. As part of this work, we adapt the PFPasap algorithm proposed in [1] for energy-harvesting systems to thermal-aware ones. We prove its optimality for non-concrete1 fixed-priority task sets and propose a response-time analysis based on worst-case response-time upper bounds. We evaluate the efficacy of the proposed bounds via extensive simulation over randomly-generated task systems

Modeling and Algorithmic Development for Selected Real-World Optimization Problems with Hard-to-Model Features

Mathematical optimization is a common tool for numerous real-world optimization problems. However, in some application domains there is a scope for improvement of currently used optimization techniques. For example, this is typically the case for applications that contain features which are difficult to model, and applications of interdisciplinary nature where no strong optimization knowledge is available. The goal of this thesis is to demonstrate how to overcome these challenges by considering five problems from two application domains. The first domain that we address is scheduling in Cloud computing systems, in which we investigate three selected problems. First, we study scheduling problems where jobs are required to start immediately when they are submitted to the system. This requirement is ubiquitous in Cloud computing but has not yet been addressed in mathematical scheduling. Our main contributions are (a) providing the formal model, (b) the development of exact and efficient solution algorithms, and (c) proofs of correctness of the algorithms. Second, we investigate the problem of energy-aware scheduling in Cloud data centers. The objective is to assign computing tasks to machines such that the energy required to operate the data center, i.e., the energy required to operate computing devices plus the energy required to cool computing devices, is minimized. Our main contributions are (a) the mathematical model, and (b) the development of efficient heuristics. Third, we address the problem of evaluating scheduling algorithms in a realistic environment. To this end we develop an approach that supports mathematicians to evaluate scheduling algorithms through simulation with realistic instances. Our main contributions are the development of (a) a formal model, and (b) efficient heuristics. The second application domain considered is powerline routing. We are given two points on a geographic area and respective terrain characteristics. The objective is to find a ``good'' route (which depends on the terrain), connecting both points along which a powerline should be built. Within this application domain, we study two selected problems. First, we study a geometric shortest path problem, an abstract and simplified version of the powerline routing problem. We introduce the concept of the k-neighborhood and contribute various analytical results. Second, we investigate the actual powerline routing problem. To this end, we develop algorithms that are built upon the theoretical insights obtained in the previous study. Our main contributions are (a) the development of exact algorithms and efficient heuristics, and (b) a comprehensive evaluation through two real-world case studies. Some parts of the research presented in this thesis have been published in refereed publications [119], [110], [109]

Actes du 11ème Atelier en Évaluation de Performances

International audienceLe présent document contient les actes du 11ème Atelier en Évaluation des Performances qui s'est tenu les 15-17 Mars 2016 au LAAS-CNRS, Toulouse. L’Atelier en Évaluation de Performances est une réunion destinée à faire s’exprimer et se rencontrer les jeunes chercheurs (doctorants et postdoctorants) dans le domaine de la Modélisation et de l’Évaluation de Performances, une discipline consacrée à l’étude et l’optimisation de systèmes dynamiques stochastiques et/ou temporisés apparaissant en Informatique, Télécommunications, Productique et Robotique entre autres. La présentation informelle de travaux, même en cours, y est encouragée afin de renforcer les interactions entre jeunes chercheurs et préparer des soumissions de nouveaux projets scientifiques. Des exposés de synthèse sur des domaines de recherche d’actualité, donnés par des chercheurs confirmés du domaine renforcent la partie formation de l’atelier

Asymptotically Optimal Energy Efficient Offloading Policies in Multi-Access Edge Computing Systems with Task Handover

We study energy-efficient offloading strategies in a large-scale MEC system with heterogeneous mobile users and network components. The system is considered with enabled user-task handovers that capture the mobility of various mobile users. We focus on a long-run objective and online algorithms that are applicable to realistic systems. The problem is significantly complicated by the large problem size, the heterogeneity of user tasks and network components, and the mobility of the users, for which conventional optimizers cannot reach optimum with a reasonable amount of computational and storage power. We formulate the problem in the vein of the restless multi-armed bandit process that enables the decomposition of high-dimensional state spaces and then achieves near-optimal algorithms applicable to realistically large problems in an online manner. Following the restless bandit technique, we propose two offloading policies by prioritizing the least marginal costs of selecting the corresponding computing and communication resources in the edge and cloud networks. This coincides with selecting the resources with the highest energy efficiency. Both policies are scalable to the offloading problem with a great potential to achieve proved asymptotic optimality - approach optimality as the problem size tends to infinity. With extensive numerical simulations, the proposed policies are demonstrated to clearly outperform baseline policies with respect to power conservation and robust to the tested heavy-tailed lifespan distributions of the offloaded tasks.Comment: 15 pages, 22 figure

SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems

We study combinatorial problems with real world applications such as machine scheduling, routing, and assignment. We propose a method that combines Reinforcement Learning (RL) and planning. This method can equally be applied to both the offline, as well as online, variants of the combinatorial problem, in which the problem components (e.g., jobs in scheduling problems) are not known in advance, but rather arrive during the decision-making process. Our solution is quite generic, scalable, and leverages distributional knowledge of the problem parameters. We frame the solution process as an MDP, and take a Deep Q-Learning approach wherein states are represented as graphs, thereby allowing our trained policies to deal with arbitrary changes in a principled manner. Though learned policies work well in expectation, small deviations can have substantial negative effects in combinatorial settings. We mitigate these drawbacks by employing our graph-convolutional policies as non-optimal heuristics in a compatible search algorithm, Monte Carlo Tree Search, to significantly improve overall performance. We demonstrate our method on two problems: Machine Scheduling and Capacitated Vehicle Routing. We show that our method outperforms custom-tailored mathematical solvers, state of the art learning-based algorithms, and common heuristics, both in computation time and performance

Coordinated Multi-Agent Patrolling with History-Dependent Cost Rates -- Asymptotically Optimal Policies for Large-Scale Systems

We study a large-scale patrol problem with history-dependent costs and multi-agent coordination, where we relax the assumptions on the past patrol studies, such as identical agents, submodular reward functions and capabilities of exploring any location at any time. Given the complexity and uncertainty of the practical situations for patrolling, we model the problem as a discrete-time Markov decision process (MDP) that consists of a large number of parallel restless bandit processes and aim to minimize the cumulative patrolling cost over a finite time horizon. The problem exhibits an excessively large size of state space, which increases exponentially in the number of agents and the size of geographical region for patrolling. We extend the Whittle relaxation and Lagrangian dynamic programming (DP) techniques to the patrolling case, where the additional, non-trivial constraints used to track the trajectories of all the agents are inevitable and significantly complicate the analysis. The past results cannot ensure the existence of patrol policies with theoretically bounded performance degradation. We propose a patrol policy applicable and scalable to the above mentioned large, complex problem. By invoking Freidlin's theorem, we prove that the performance deviation between the proposed policy and optimality diminishes exponentially in the problem size.Comment: 37 pages, 4 figure

Parallel Real-Time Scheduling for Latency-Critical Applications

In order to provide safety guarantees or quality of service guarantees, many of today\u27s systems consist of latency-critical applications, e.g. applications with timing constraints. The problem of scheduling multiple latency-critical jobs on a multiprocessor or multicore machine has been extensively studied for sequential (non-parallizable) jobs and different system models and different objectives have been considered. However, the computational requirement of a single job is still limited by the capacity of a single core. To provide increasingly complex functionalities of applications and to complete their higher computational demands within the same or even more stringent timing constraints, we must exploit the internal parallelism of jobs, where individual jobs are parallel programs and can potentially utilize more than one core in parallel. However, there is little work considering scheduling multiple parallel jobs that are latency-critical. This dissertation focuses on developing new scheduling strategies, analysis tools, and practical platform design techniques to enable efficient and scalable parallel real-time scheduling for latency-critical applications on multicore systems. In particular, the research is focused on two types of systems: (1) static real-time systems for tasks with deadlines where the temporal properties of the tasks that need to execute is known a priori and the goal is to guarantee the temporal correctness of the tasks prior to their executions; and (2) online systems for latency-critical jobs where multiple jobs arrive over time and the goal to optimize for a performance objective of jobs during the execution. For static real-time systems for parallel tasks, several scheduling strategies, including global earliest deadline first, global rate monotonic and a novel federated scheduling, are proposed, analyzed and implemented. These scheduling strategies have the best known theoretical performance for parallel real-time tasks under any global strategy, any fixed priority scheduling and any scheduling strategy, respectively. In addition, federated scheduling is generalized to systems with multiple criticality levels and systems with stochastic tasks. Both numerical and empirical experiments show that federated scheduling and its variations have good schedulability performance and are efficient in practice. For online systems with multiple latency-critical jobs, different online scheduling strategies are proposed and analyzed for different objectives, including maximizing the number of jobs meeting a target latency, maximizing the profit of jobs, minimizing the maximum latency and minimizing the average latency. For example, a simple First-In-First-Out scheduler is proven to be scalable for minimizing the maximum latency. Based on this theoretical intuition, a more practical work-stealing scheduler is developed, analyzed and implemented. Empirical evaluations indicate that, on both real world and synthetic workloads, this work-stealing implementation performs almost as well as an optimal scheduler