Feasibility Analysis of Conditional DAG Tasks

Feasibility analysis for Conditional DAG tasks (C-DAGs) upon multiprocessor platforms is shown to be complete for the complexity class pspace. It is shown that as a consequence integer linear programming solvers (ILP solvers) are likely to prove inadequate for such analysis. A demarcation is identified between the feasibility-analysis problems on C-DAGs that are efficiently solvable using ILP solvers and those that are not, by characterizing a restricted class of C-DAGs for which feasibility analysis is shown to be efficiently solvable using ILP solvers

Approximation and online algorithms in scheduling and coloring

In the last three decades, approximation and online algorithms have become a major area of theoretical computer science and discrete mathematics. Scheduling and coloring problems are among the most popular ones for which approximation and online algorithms have been analyzed. On one hand, motivated by the well-known difficulty to obtain good lower bounds for the problems, it is particularly hard to prove results on the online and offline performance of algorithms. On the other hand, the theoretically oriented studies of approximation and online algorithms for scheduling and coloring have also impact on the development of better algorithms for real world applications. In the thesis we present approximation algorithms and online algorithms for a number of scheduling and labeling (coloring) problems. Our work in the first part of the thesis is devoted to scheduling problems with the average weighted completion time objective function, that is primarily motivated by some theoretical questions which were open for a number of recent years. Here we present a general method which leads to the design of polynomial time approximation schemes (PTASs), best possible approximation results. In contrast, our work in the second part of the thesis is motivated by practical applications. We consider a number of new labeling and scheduling problems which occur in the design of communication networks. Here we present and analyze efficient approximation and online algorithms. We use very simple techniques which do not require large computational resources

Optimization and Management of Large-scale Scientific Workflows in Heterogeneous Network Environments: From Theory to Practice

Next-generation computation-intensive scientific applications feature large-scale computing workflows of various structures, which can be modeled as simple as linear pipelines or as complex as Directed Acyclic Graphs (DAGs). Supporting such computing workflows and optimizing their end-to-end network performance are crucial to the success of scientific collaborations that require fast system response, smooth data flow, and reliable distributed operation.We construct analytical cost models and formulate a class of workflow mapping problems with different mapping objectives and network constraints. The difficulty of these mapping problems essentially arises from the topological matching nature in the spatial domain, which is further compounded by the resource sharing complicacy in the temporal dimension. We provide detailed computational complexity analysis and design optimal or heuristic algorithms with rigorous correctness proof or performance analysis. We decentralize the proposed mapping algorithms and also investigate these optimization problems in unreliable network environments for fault tolerance.To examine and evaluate the performance of the workflow mapping algorithms before actual deployment and implementation, we implement a simulation program that simulates the execution dynamics of distributed computing workflows. We also develop a scientific workflow automation and management platform based on an existing workflow engine for experimentations in real environments. The performance superiority of the proposed mapping solutions are illustrated by extensive simulation-based comparisons with existing algorithms and further verified by large-scale experiments on real-life scientific workflow applications through effective system implementation and deployment in real networks

A Survey of Pipelined Workflow Scheduling: Models and Algorithms

International audienceA large class of applications need to execute the same workflow on different data sets of identical size. Efficient execution of such applications necessitates intelligent distribution of the application components and tasks on a parallel machine, and the execution can be orchestrated by utilizing task-, data-, pipelined-, and/or replicated-parallelism. The scheduling problem that encompasses all of these techniques is called pipelined workflow scheduling, and it has been widely studied in the last decade. Multiple models and algorithms have flourished to tackle various programming paradigms, constraints, machine behaviors or optimization goals. This paper surveys the field by summing up and structuring known results and approaches

Scheduling with processing set restrictions : a survey

2008-2009 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Scheduling Problems

Scheduling is defined as the process of assigning operations to resources over time to optimize a criterion. Problems with scheduling comprise both a set of resources and a set of a consumers. As such, managing scheduling problems involves managing the use of resources by several consumers. This book presents some new applications and trends related to task and data scheduling. In particular, chapters focus on data science, big data, high-performance computing, and Cloud computing environments. In addition, this book presents novel algorithms and literature reviews that will guide current and new researchers who work with load balancing, scheduling, and allocation problems

Order Acceptance and Scheduling: A Taxonomy and Review

Over the past 20 years, the topic of order acceptance has attracted considerable attention from those who study scheduling and those who practice it. In a firm that strives to align its functions so that profit is maximized, the coordination of capacity with demand may require that business sometimes be turned away. In particular, there is a trade-off between the revenue brought in by a particular order, and all of its associated costs of processing. The present study focuses on the body of research that approaches this trade-off by considering two decisions: which orders to accept for processing, and how to schedule them. This paper presents a taxonomy and a review of this literature, catalogs its contributions and suggests opportunities for future research in this area

Politiques de gestion d’Énergie et de Température dans les Systèmes Informatiques

Nowadays, the energy consumption and the heat dissipation of computing environmentshave emerged as crucial issues. Indeed, large data centers consume as much electricityas a city while modern processors attain high temperatures degrading their performanceand decreasing their reliability. In this thesis, we study various energy and temperatureaware scheduling problems and we focus on their complexity and approximability.A dominant technique for saving energy is by proper scheduling of the jobs through theoperating system combined with appropriate scaling of the processor’s speed. This techniqueis referred to as speed scaling in the literature. The theoretical study of speed scalingwas initiated by Yao, Demers and Shenker (1995) who considered the single-processorproblem of scheduling preemptively a set of jobs, each one specified by an amount ofwork, a release date and a deadline, so as to minimize the total energy consumption.In order to measure the energy consumption of a processor, the authors considered thewell-known rule according to which the processor’s power consumption is P(t) = s(t)α ateach time t, where s(t) is the processor’s speed at t and α > 1 is a machine-dependentconstant (usually α ∈ [2, 3]). Here, we study speed scaling problems on a single processor,on homogeneous parallel processors, on heterogeneous environments and on shopenvironments. In most cases, the objective is the minimization of the energy but we alsoaddress problems in which we are interested in capturing the trade-off between energyand performance.We tackle speed scaling problems through different approaches. For non-preemptiveproblems, we explore the idea of transforming optimal preemptive schedules to nonpreemptiveones. Moreover, we exploit the fact that some problems can be formulatedas convex programs and we propose greedy algorithms that produce optimal solutionssatisfying the KKT conditions which are necessary and sufficient for optimality in convexprogramming. In the context of convex programming and KKT conditions, we also studythe design of primal-dual algorithms. Additionally, we solve speed scaling problems byformulating them as convex cost flow or minimum weighted bipartite matching problems.Finally, we elaborate on approximating energy minimization problems that can be formulatedas integer configuration linear programs. We can obtain an approximate solutionfor such a problem by solving the fractional relaxation of an integer configuration linearprogram for it and applying randomized rounding.In this thesis, we solve some new energy aware scheduling problems and we improvethe best-known algorithms for some other problems. For instance, we improve the bestknownapproximation algorithm for the single-processor non-preemptive energy minimizationproblem which is strongly NP-hard. When α = 3, we decrease the approximationratio from 2048 to 20. Furthermore, we propose a faster optimal combinatorial algorithmviiviiifor the preemptive migratory energy minimization problem on power-homogeneous processors,while the best-known algorithm was based on solving linear programs. Last butnot least, we improve the best-known approximation algorithm for the preemptive nonmigratoryenergy minimization problem on power-homogeneous processors for fractionalvalues of α. Our algorithm can be applied even in the more general case where the processorsare heterogeneous and, for αmax = 2.5 (which is the maximum constant α amongall processors), we get an improvement of the approximation ratio from 5 to 3.08.In order to manage the thermal behavior of a computing device, we adopt the approachof Chrobak, Dürr, Hurand and Robert (2011). The main assumption is that some jobsare more CPU intensive than others and more heat is generated during their execution.So, each job is associated with a heat contribution which is the impact of the job on theprocessor’s temperature. In this setting, we study the complexity and the approximabilityof multiprocessor scheduling problems where either there is a constraint on the processors’temperature and our aim is to optimize some performance metric or the temperature isthe optimization goal itself.La gestion de la consommation d’énergie et de la température est devenue un enjeucrucial dans les systèmes informatiques. En effet, un grand centre de données consommeautant d’électricité qu’une ville et les processeurs modernes atteignent des températuresimportantes dégradant ainsi leurs performances et leur fiabilité. Dans cette thèse, nousétudions différents problèmes d’ordonnancement prenant en compte la consommationd’énergie et la température des processeurs en se focalisant sur leur complexité et leurapproximabilité. Pour cela, nous utilisons le modèle de Yao et al. (1995) (modèle devariation de vitesse) pour la gestion d’énergie et le modèle de Chrobak et al. (2008) pourla gestion de la température