A novel bicriteria scheduling heuristics providing a guaranteed global system failure rate

International audienceWe propose a new framework for the (length,reliability) bicriteria static multiprocessor scheduling problem. Our first criterion remains the schedule's length, crucial to assess the system's real-time property. For our second criterion, we consider the global system failure rate, seen as if the whole system were a single task scheduled onto a single processor, instead of the usual reliability, because it does not depend on the schedule length like the reliability does (due to its computation in the classical exponential distribution model). Therefore, we control better the replication factor of each individual task of the dependency task graph given as a specification, with respect to the desired failure rate. To solve this bicriteria optimization problem, we take the failure rate as a constraint, and we minimize the schedule length. We are thus able to produce, for a given dependency task graph and multiprocessor architecture, a Pareto curve of non-dominated solutions, among which the user can choose the compromise that fits his requirements best. Compared to the other bicriteria (length,reliability) scheduling algorithms found in the literature, the algorithm we present here is the first able to improve significantly the reliability, by several orders of magnitude, making it suitable to safety critical systems

Optimisation du développement de nouveaux produits dans l'industrie pharmaceutique par algorithme génétique multicritère

Le développement de nouveaux produits constitue une priorité stratégique de l'industrie pharmaceutique, en raison de la présence d'incertitudes, de la lourdeur des investissements mis en jeu, de l'interdépendance entre projets, de la disponibilité limitée des ressources, du nombre très élevé de décisions impliquées dû à la longueur des processus (de l'ordre d'une dizaine d'années) et de la nature combinatoire du problème. Formellement, le problème se pose ainsi : sélectionner des projets de Ret D parmi des projets candidats pour satisfaire plusieurs critères (rentabilité économique, temps de mise sur le marché) tout en considérant leur nature incertaine. Plus précisément, les points clés récurrents sont relatifs à la détermination des projets à développer une fois que les molécules cibles sont identifiées, leur ordre de traitement et le niveau de ressources à affecter. Dans ce contexte, une approche basée sur le couplage entre un simulateur à événements discrets stochastique (approche Monte Carlo) pour représenter la dynamique du système et un algorithme d'optimisation multicritère (de type NSGA II) pour choisir les produits est proposée. Un modèle par objets développé précédemment pour la conception et l'ordonnancement d'ateliers discontinus, de réutilisation aisée tant par les aspects de structure que de logique de fonctionnement, a été étendu pour intégrer le cas de la gestion de nouveaux produits. Deux cas d'étude illustrent et valident l'approche. Les résultats de simulation ont mis en évidence l'intérêt de trois critères d'évaluation de performance pour l'aide à la décision : le bénéfice actualisé d'une séquence, le risque associé et le temps de mise sur le marché. Ils ont été utilisés dans la formulation multiobjectif du problème d'optimisation. Dans ce contexte, des algorithmes génétiques sont particulièrement intéressants en raison de leur capacité à conduire directement au front de Pareto et à traiter l'aspect combinatoire. La variante NSGA II a été adaptée au problème pour prendre en compte à la fois le nombre et l'ordre de lancement des produits dans une séquence. A partir d'une analyse bicritère réalisée pour un cas d'étude représentatif sur différentes paires de critères pour l'optimisation bi- et tri-critère, la stratégie d'optimisation s'avère efficace et particulièrement élitiste pour détecter les séquences à considérer par le décideur. Seules quelques séquences sont détectées. Parmi elles, les portefeuilles à nombre élevé de produits provoquent des attentes et des retards au lancement ; ils sont éliminés par la stratégie d'optimistaion bicritère. Les petits portefeuilles qui réduisent les files d'attente et le temps de lancement sont ainsi préférés. Le temps se révèle un critère important à optimiser simultanément, mettant en évidence tout l'intérêt d'une optimisation tricritère. Enfin, l'ordre de lancement des produits est une variable majeure comme pour les problèmes d'ordonnancement d'atelier. ABSTRACT : New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline, namely, the presence of uncertainty, the high level of the involved capital costs, the interdependency between projects, the limited availability of resources, the overwhelming number of decisions due to the length of the time horizon (about 10 years) and the combinatorial nature of a portfolio. Formally, the NPD problem can be stated as follows: select a set of R and D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while copying with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGA II type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. An object-oriented model previously developed for batch plant scheduling and design is then extended to embed the case of new product management, which is particularly adequate for reuse of both structure and logic. Two case studies illustrate and validate the approach. From this simulation study, three performance evaluation criteria must be considered for decision making: the Net Present Value (NPV) of a sequence, its associated risk defined as the number of positive occurrences of NPV among the samples and the time to market. Theyv have been used in the multiobjective optimization formulation of the problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. NSGA II has been adapted to the treated case for taking into account both the number of products in a sequence and the drug release order. From an analysis performed for a representative case study on the different pairs of criteria both for the bi- and tricriteria optimization, the optimization strategy turns out to be efficient and particularly elitist to detect the sequences which can be considered by the decision makers. Only a few sequences are detected. Among theses sequences, large portfolios cause resource queues and delays time to launch and are eliminated by the bicriteria optimization strategy. Small portfolio reduces queuing and time to launch appear as good candidates. The optimization strategy is interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems

Multiobjective optimization of New Product Development in the pharmaceutical industry

New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline, namely, the presence of uncertainty, the high level of the involved capital costs, the interdependency between projects, the limited availability of resources, the overwhelming number of decisions due to the length of the time horizon (about 10 years) and the combinatorial nature of a portfolio. Formally, the NPD problem can be stated as follows: select a set of R and D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while copying with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGA II type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. An object-oriented model previously developed for batch plant scheduling and design is then extended to embed the case of new product management, which is particularly adequate for reuse of both structure and logic. Two case studies illustrate and validate the approach. From this simulation study, three performance evaluation criteria must be considered for decision making: the Net Present Value (NPV) of a sequence, its associated risk defined as the number of positive occurrences of NPV among the samples and the time to market. Theyv have been used in the multiobjective optimization formulation of the problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. NSGA II has been adapted to the treated case for taking into account both the number of products in a sequence and the drug release order. From an analysis performed for a representative case study on the different pairs of criteria both for the bi- and tricriteria optimization, the optimization strategy turns out to be efficient and particularly elitist to detect the sequences which can be considered by the decision makers. Only a few sequences are detected. Among theses sequences, large portfolios cause resource queues and delays time to launch and are eliminated by the bicriteria optimization strategy. Small portfolio reduces queuing and time to launch appear as good candidates. The optimization strategy is interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems

Tabu search for ship routing and scheduling

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 20/12/2006.This thesis examines exact and heuristic approaches to solve the Ship Routing and Scheduling Problem (SRSP). The method was developed to address the problem of loading cargos for many customers using heterogeneous vessels. Constraints relate to delivery time windows imposed by customers, the time horizon by which all deliveries must be made and vessel capacities. The objective is to minimise the overall operation cost, where all customers are satisfied. Two types of routing and scheduling are considered, one called single-cargo problem, where only one cargo can be loaded into a ship, and the second type called multi-cargo problem, where multiple products can be carried on a ship to be delivered to different customers. The exact approach comprises two stages. In the first stage, a number of candidate feasible schedules is generated for each ship in the fleet. The second stage is to model the problem as a set partitioning problem (SPP) where the columns are the candidate feasible schedules obtained in the first stage. The heuristic approach uses Tabu Search (TS). Most of the TS operations, such as insert and swap moves, tenure, tabu list, intensification, and diversification are used. The results of a computational investigation are presented. Solution quality and execution time are explored with respect to problem size and parameters controlling the tabu search such as tenure and neighbourhood size. The results showed that the average of the solution gap between TS solution and SPP solution is up to 28% (for small problems) and up to 18% for large problems. However, obtaining an optimal solution requires a large amount of computer time to produce the solution compared to obtaining approximate solutions using the TS approach. The use of Tabu Search for SRSP is novel and the results indicate that it is viable approach for large problems

ERPOT: Une heuristique d’ordonnancement quadri-critère pour optimiser le temps d’exécution, le taux de défaillance, la puissance électrique et la température sur les multi-cœurs

We investigate multi-criteria optimization and Pareto front generation. Given an application modeled as a Directed Acyclic Graph (DAG) of tasks and a multicore architecture, we produce a set of non-dominated (in the Pareto sense) static schedules of this DAG onto this multicore. The criteria we address are the execution time, reliability, power consumption, and peak temperature. These criteria exhibit complex antagonistic relations, which make the problem challenging. For instance, improving the reliability requires adding some redundancy in the schedule, which penalizes the execution time. To produce Pareto fronts in this 4-dimension space, we transform three of the four criteria into constraints (the reliability, the power consumption, and the peak temperature), and we minimize the fourth one (the execution time of the schedule) under these three constraints. By varying the thresholds used for the three constraints, we are able to produce a Pareto front of non-dominated solutions. We propose two algorithms to compute static schedules. The first is a ready list scheduling heuristic called ERPOT (Execution time, Reliability, POwer consumption and Temperature). ERPOT actively replicates the tasks to increase the reliability, uses Dynamic Voltage and Frequency Scaling to decrease the power consumption, and inserts cooling times to control the peak temperature. The second algorithm uses an Integer Linear Programming (ILP) program to compute an optimal schedule. However, because our multi-criteria scheduling problem is NP-complete, the ILP algorithm is limited to very small problem instances. Comparisons showed that the schedules produced by ERPOT are on average only 10% worse than the optimal schedules computed by the ILP program, and that ERPOT outperforms the PowerPerf-PET heuristic from the literature on average by 33%.Nous nous attaquons à l’optimisation multi-critères et à la génération de fronts de Pareto. Etant données une application modélisée sous la forme d’un graphe orienté sans cycle (DAG) de tâches et une architecture multi-cœurs, nous calculons un ensemble d’ordonnancements statiques non dominés (au sens de Pareto) de ce DAG sur ce multi-cœurs. Les critères que nous considérons sont le temps d’exécution, la fiabilité, la puissance électrique et la température de crête. Ces critères présentent des relations complexes d’antagonisme, ce qui fait de notre problème d’ordonnancement un vrai défi. Par exemple, améliorer la fiabilité requiert d’ajouter de la redondance dans l’ordonnancement, ce qui pénalise le temps d’exécution. Afin de produire des fronts de Pareto dans cet espace à quatre dimensions, nous transformons trois de ces quatre critères en contraintes (la fiabilité, la puissance électrique et la température de crête) et nous minimisons le quatrième (le temps d’exécution) sous ces trois contraintes. En faisant varier les seuils utilisés pour les trois contraintes, nous sommes capables de produire un front de Pareto de solutions non-dominées. Nous proposons deux algorithmes pour calculer des ordonnancements statiques. Le premier est une heuristique de liste appelé ERPOT (Execution time, failure Rate, POwer consumption and Temperature). ERPOT réplique activement la tâches pour améliorer la fiabilité, utilise l’Ajustement Dynamique de la Fréquence et de la Tension (ADFT) pour réduire la puissance électrique, et insère des intervalles d’inactivité pour contrôler la température de crête. Le second algorithme repose sur un Programme Linéaire en Nombres Entiers (PLNE) pour construire un ordonnancement optimal. Toutefois, dans la mesure où notre problème d’ordonnancement multi-critères est NP-complet, l’algorithme PLNE est limité à des instances de très petite taille. Les comparaisons montrent que les ordonnancements produits par ERPOT sont en moyenne 10% moins bons que les ordonnancements optimaux calculés par l’algorithme PNLE, et que ERPOT améliore en moyenne de 33% les ordonnancements produit par l’heuristique PowerPerf-PET de la littérature

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

Tabu search for ship routing and scheduling

This thesis examines exact and heuristic approaches to solve the Ship Routing and Scheduling Problem (SRSP). The method was developed to address the problem of loading cargos for many customers using heterogeneous vessels. Constraints relate to delivery time windows imposed by customers, the time horizon by which all deliveries must be made and vessel capacities. The objective is to minimise the overall operation cost, where all customers are satisfied. Two types of routing and scheduling are considered, one called single-cargo problem, where only one cargo can be loaded into a ship, and the second type called multi-cargo problem, where multiple products can be carried on a ship to be delivered to different customers. The exact approach comprises two stages. In the first stage, a number of candidate feasible schedules is generated for each ship in the fleet. The second stage is to model the problem as a set partitioning problem (SPP) where the columns are the candidate feasible schedules obtained in the first stage. The heuristic approach uses Tabu Search (TS). Most of the TS operations, such as insert and swap moves, tenure, tabu list, intensification, and diversification are used. The results of a computational investigation are presented. Solution quality and execution time are explored with respect to problem size and parameters controlling the tabu search such as tenure and neighbourhood size. The results showed that the average of the solution gap between TS solution and SPP solution is up to 28% (for small problems) and up to 18% for large problems. However, obtaining an optimal solution requires a large amount of computer time to produce the solution compared to obtaining approximate solutions using the TS approach. The use of Tabu Search for SRSP is novel and the results indicate that it is viable approach for large problems.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Tolérance aux pannes dans des environnements de calcul parallèle et distribué (optimisation des stratégies de sauvegarde/reprise et ordonnancement)

Le passage de l'échelle des nouvelles plates-formes de calcul parallèle et distribué soulève de nombreux défis scientifiques. À terme, il est envisageable de voir apparaître des applications composées d'un milliard de processus exécutés sur des systèmes à un million de coeurs. Cette augmentation fulgurante du nombre de processeurs pose un défi de résilience incontournable, puisque ces applications devraient faire face à plusieurs pannes par jours. Pour assurer une bonne exécution dans ce contexte hautement perturbé par des interruptions, de nombreuses techniques de tolérance aux pannes telle que l'approche de sauvegarde et reprise (checkpoint) ont été imaginées et étudiées. Cependant, l'intégration de ces approches de tolérance aux pannes dans le couple formé par l'application et la plate-forme d'exécution soulève des problématiques d'optimisation pour déterminer le compromis entre le surcoût induit par le mécanisme de tolérance aux pannes d'un coté et l'impact des pannes sur l'exécution d'un autre coté. Dans la première partie de cette thèse nous concevons deux modèles de performance stochastique (minimisation de l'impact des pannes et du surcoût des points de sauvegarde sur l'espérance du temps de complétion de l'exécution en fonction de la distribution d'inter-arrivées des pannes). Dans la première variante l'objectif est la minimisation de l'espérance du temps de complétion en considérant que l'application est de nature préemptive. Nous exhibons dans ce cas de figure tout d'abord une expression analytique de la période de sauvegarde optimale quand le taux de panne et le surcoût des points de sauvegarde sont constants. Par contre dans le cas où le taux de panne ou les surcoûts des points de sauvegarde sont arbitraires nous présentons une approche numérique pour calculer l'ordonnancement optimal des points de sauvegarde. Dans la deuxième variante, l'objectif est la minimisation de l'espérance de la quantité totale de temps perdu avant la première panne en considérant les applications de nature non-préemptive. Dans ce cas de figure, nous démontrons tout d'abord que si les surcoûts des points sauvegarde sont arbitraires alors le problème du meilleur ordonnancement des points de sauvegarde est NP-complet. Ensuite, nous exhibons un schéma de programmation dynamique pour calculer un ordonnancement optimal. Dans la deuxième partie de cette thèse nous nous focalisons sur la conception des stratégies d'ordonnancement tolérant aux pannes qui optimisent à la fois le temps de complétion de la dernière tâche et la probabilité de succès de l'application. Nous mettons en évidence dans ce cas de figure qu'en fonction de la nature de la distribution de pannes, les deux objectifs à optimiser sont tantôt antagonistes, tantôt congruents. Ensuite en fonction de la nature de distribution de pannes nous donnons des approches d'ordonnancement avec des ratios de performance garantis par rapport aux deux objectifs.The parallel computing platforms available today are increasingly larger. Typically the emerging parallel platforms will be composed of several millions of CPU cores running up to a billion of threads. This intensive growth of the number of parallel threads will make the application subject to more and more failures. Consequently it is necessary to develop efficient strategies providing safe and reliable completion for HPC parallel applications. Checkpointing is one of the most popular and efficient technique for developing fault-tolerant applications on such a context. However, checkpoint operations are costly in terms of time, computation and network communications. This will certainly affect the global performance of the application. In the first part of this thesis, we propose a performance model that expresses formally the checkpoint scheduling problem. Two variants of the problem have been considered. In the first variant, the objective is the minimization of the expected completion time. Under this model we prove that when the failure rate and the checkpoint cost are constant the optimal checkpoint strategy is necessarily periodic. For the general problem when the failure rate and the checkpoint cost are arbitrary we provide a numerical solution for the problem. In the second variant if the problem, we exhibit the tradeoff between the impact of the checkpoints operations and the lost computation due to failures. In particular, we prove that the checkpoint scheduling problem is NP-hard even in the simple case of uniform failure distribution. We also present a dynamic programming scheme for determining the optimal checkpointing times in all the variants of the problem. In the second part of this thesis, we design several fault tolerant scheduling algorithms that minimize the application makespan and in the same time maximize the application reliability. Mainly, in this part we point out that the growth rate of the failure distribution determines the relationship between both objectives. More precisely we show that when the failure rate is decreasing the two objectives are antagonist. In the second hand when the failure rate is increasing both objective are congruent. Finally, we provide approximation algorithms for both failure rate cases.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field