Task scheduling to extend platform useful life using prognostics.

International audienceIn this paper, we aim at maximizing the useful life of a heterogeneous distributed platform which has to deliver a given production. The machines (one nominal mode and several degraded ones). Depending on the profile, a machine reaches a given throughput. At each time the sum of the machine throughputs that are currentky running determines the global throughput. Moreover, each machine is supposed to be monitored and a prognostic module gives its remaining useful life depending on both its past and future usage (profile). the objective is to configure the platform so as to reach the demand as long as possible. We propose to discretize the time into periods and to choose a configuration for each period. We propose an Integer Linear Programming (ILP) model to find such configurations for a fixed time horizon. Due to the number of variables and constraints in the ILP, the largest horizon can be computed for small instances of the problem. For larger ones , we propose polynomial time heuristics to maximize the useful life. Exhaustive simulations show that the heuristics solutions are close to the optimal (5% in average) in the case where the optimal horizon can to computed. for other platforms with a very large number of machines, simulations assess the efficienty of our heuristics. The distance to the theoretical maximal value is about 8% in average

Utilisation du {D}istributed {S}panning {T}ree en tant qu'{O}verlay

International audienceLe Distributed Spanning Tree est une topologie originale developpee pour ameliorer les performances des mecanismes de recherche par flooding en terme de vitesse de recherche et en terme de charge supportee. Ces mecanismes de recherche diffusent des messages par l intermediaire d un graphe de communication. Deux types de topologie sont communement utilises pour construire ces overlay networks : ce sont les graphes et les arbres. En utilisant une vision originale des arbres, il est possible de faire disparaitre leurs goulets d etranglements. Nous decrivons cette structure ainsi que deux algorithmes de parcours qui lui sont associes. Puis, les gains de performances du Distributed Spanning Tree par rapport aux graphes et aux arbres classiques sont soulignes a l aide de simulations

Comparison of Batch Scheduling for Identical Multi-Tasks Jobs on Heterogeneous Platforms

International audienceIn this paper we consider the scheduling of a batch of the same job on a heterogeneous execution platform. A job is represented by a directed acyclic graph without forks (intree) but with typed tasks. The execution resources are distributed and each resource can carry out a set of task types. The objective function is to minimize the makespan of the batch execution. Three algorithms are studied in this context: an on-line algorithm, a genetic algorithm and a steady-state algorithm. The contribution of this paper is on the experimental analysis of these algorithms and on their adaptation to the context. We show that their performances depend on the size of the batch and on the characteristics of the execution platform

Adaptation d'un algorithme optimal d'ordonnancement en régime permanent pour des lots bornés

International audienceLe contexte de cet article est l ordonnancement de lots bornes de travaux identiques sur une plate-forme d execution heterogene comme la grille. Les travaux executes sont des graphes de t?ches orientes et sans cycle (DAG), en forme d anti-arbre. Les t?ches sont de plusieurs types et les n{oe}uds de la plate-forme ne sont pas toujours en mesure d executer tous les types de t?ches. Le probleme de minimisation du temps d execution d un lot est un probleme NP-Complet. Sous l angle du regime permanent, il est possible de decrire le probleme sous la forme d un programme lineaire donnant une solution optimale pour l ordonnancement cyclique de lots infinis. Lorsque les lots sont bornes, les resultats restent bons bien que sous optimaux. Nous montrons ici que les phases d initialisation et de terminaison ajoutent un sur-co?t qui penalise le temps global d execution. Nous montrons ensuite le lien entre la taille de ces phases et la taille de la periode de l ordonnancement cyclique et donnons un algorithme permettant le calcul de la periode minimale. Des experimentations, obtenues par simulations avec SimGrid, illustrent en fin d article le gain apporte par le choix d une periode minimal

Prognostics-based Scheduling to Extend a Distributed Platform Production Horizon under Service Constraint: Model, Complexity and Resolution.

In the field of production scheduling, this paper addresses the problem of optimizing the useful life of a heterogeneous distributed platform composed of identical parallel machines and which has to provide a given production service. Each machine is supposed to be able to provide several throughputs corresponding to different operating conditions. The purpose is to provide a production scheduling that maximizes the production horizon. The use of Prognostics and Health Management (PHM) results in the form of Remaining Useful Life (RUL) allows to adapt the schedule to the wear and tear of machines. This work comes within the scope of Prognostics Decision Making (DM). The key point is to configure the platform, i.e., to select the appropriate profile for each machine during the whole production horizon so as to reach a total throughput based on a customer demand as long as possible. In the homogeneous case, the Longest Remaining Useful Life first algorithm (LRUL) is proposed to find a solution and its optimality is proven. The NP-Completeness of the general case is then shown. A Binary Integer Linear Programming (BILP) model which allows to find optimal solutions for fixed time horizons has been defined. As solving such a BILP is NP-Complete, solutions can however be computed in reasonable time only for small size instances of the problem. Many heuristics are then proposed to cope with large scale decision problems and are compared through simulation results. Exhaustive simulations assess the efficiency of these heuristics. Distance to the theoretical maximal value comes indeed close to 5% for the most efficient ones

Optimizing the Cost of an Heterogeneous Distributed Platform

International audienceDistributed platforms become heterogeneous in more and more domains, as heterogeneous computing (HC) onto grids or reconfigurable factories in the industry. For production grids and factories, it is mandatory to control and optimize the economic cost of a such platforms regarding performance objectives. We present in this paper a study which purpose is to optimize the size of such environments depending on the workflow to execute or product to realize. The target platforms are either micro-factories, sized to manufacture products at the micrometric scale, or the heterogeneous computing domain where the key point is to reserve processors of an execution platform onto a grid to compute workflows like medical imaging applications. Thanks to the sizing of the platform, optimal or not, scheduling a workflow in HC environment or a production in the micro-factory is easy because the size of the platform already takes the performance constraints into account. In this paper, we present general results on the platform size optimization. Numerical results are also presented to illustrate 3 cases of our study

Processing Identical Workflows on {SOA} Grids: Comparison of Three Approaches

International audienceIn this paper we consider the scheduling of a batch of workflows on a service oriented grid. A job is represented by a directed acyclic graph without forks (intree) but with typed tasks. The processors are distributed and each processor have a set of services that carry out equivalent task types. The objective function is to minimize the makespan of the batch execution. Three algorithms are studied in this context: an on-line algorithm, a genetic algorithm and a steady-state algorithm. The contribution of this paper is on the experimental analysis of these algorithms and on their adaptation to the context. We show that their performances depend on the size and complexity of the batch and on the characteristics of the execution platform. end{abstract

The Distributed Spanning Tree Structure

International audienceSearch algorithms are a key issue to share resources in large distributed systems as peer networks. Several distributed interconnection structures and algorithms have already been studied in this context. With expanding ring algorithms, the efficiency of searches depends on the topology used to send query requests and on the dynamics of the structure. In this paper, we present an interconnection structure that limits the number of messages needed for search queries. This structure, called Distributed Spanning Tree (DST), defines each node as the root of a spanning tree. So, it behaves as a tree for the number of messages but it balances the load generated by the requests among computers and, thus, it avoids to overload the root~node. This structure is scalable because it only needs a logarithmic memory space per computer to be maintained. A formal and practical description of the structure is presented and we describe traversal algorithms. Simulations show that DST based searches behave better than randomly generated graphs and trees as it generates less messages to query all computers while avoiding the tree bottlenecks