A First Step Towards Automatically Building Network Representations

To fully harness Grids, users or middlewares must have some knowledge on the topology of the platform interconnection network. As such knowledge is usually not available, one must uses tools which automatically build a topological network model through some measurements. In this article, we define a methodology to assess the quality of these network model building tools, and we apply this methodology to representatives of the main classes of model builders and to two new algorithms. We show that none of the main existing techniques build models that enable to accurately predict the running time of simple application kernels for actual platforms. However some of the new algorithms we propose give excellent results in a wide range of situations

Redundant movements in autonomous mobility: experimental and theoretical analysis

<p>Distributed load balancers exhibit thrashing where tasks are repeatedly moved between locations due to incomplete global load information. This paper shows that systems of autonomous mobile programs (AMPs) exhibit the same behaviour, and identifies two types of redundant movement (greedy effect). AMPs are unusual in that, in place of some external load management system, each AMP periodically recalculates network and program parameters and may independently move to a better execution environment. Load management emerges from the behaviour of collections of AMPs.</p> <p>The paper explores the extent of greedy effects by simulating collections of AMPs and proposes negotiating AMPs (NAMPs) to ameliorate the problem. We present the design of AMPs with a competitive negotiation scheme (cNAMPs), and compare their performance with AMPs by simulation. We establish new properties of balanced networks of AMPs, and use these to provide a theoretical analysis of greedy effects.</p&gt

A First Step Towards Automatically Building Network Representations

To fully harness Grids, users or middlewares must have some knowledge on the topology of the platform interconnection network. As such knowledge is usually not available, one must uses tools which automatically build a topological network model through some measurements. In this article, we define a methodology to assess the quality of these network model building tools, and we apply this methodology to representatives of the main classes of model builders and to two new algorithms. We show that none of the main existing techniques build models that enable to accurately predict the running time of simple application kernels for actual platforms. However some of the new algorithms we propose give excellent results in a wide range of situations.Afin de tirer le meilleur parti des grilles, les utilisateurs et les intergiciels doivent avoir connaissance de la topologie du réseau d’interconnexion de la plate-forme utilisée. Comme cette connaissance n’est généralement pas disponible a priori, on doit avoir recours à des outils construisant un modèle du réseau d’interconnexion à partir de mesures. Dans cet article nous définissons une méthodologie pour évaluer la qualité de ces outils de construction de modèles de réseau, et nous l’appliquons à des représentants des principaux types de reconstructeurs de topologies, ainsi qu’`à deux nouveaux algorithmes. Nous montrons qu’aucune des techniques existantes ne produit des modèles qui permettent de prédire avec précision le temps d’exécution sur les plates-formes actuelles de simples noyaux d’applications. Au contraire, un des nouveaux algorithmes obtient de très bons résultats dans des situations très variées

Scheduling Tightly-Coupled Applications on Heterogeneous Desktop Grids

International audiencePlatforms that comprise volatile processors, such as desktop grids, have been traditionally used for executing independent-task applications. In this work we study the scheduling of tightly-coupled iterative master-worker applications onto volatile processors. The main challenge is that workers must be simultaneously available for the application to make progress. We consider three additional complications: (i) one should take into account that workers can become temporarily reclaimed and, for data-intensive applications; (ii) one should account for the limited bandwidth between the master and the workers; and (iii) workers are strongly heterogeneous, with different computing speeds and availability probability distributions. In this context, our first contribution is a theoretical study of the scheduling problem in its off-line version, i.e., when processor availability is known in advance. Even in this case the problem is NP-hard. Our second contribution is an analytical approximation of the expectation of the time needed by a set of workers to complete a set of tasks and of the probability of success of this computation. This approximation relies on a Markovian assumption for the temporal availability of processors. Our third contribution is a set of heuristics, some of which use the above approximation to favor reliable processors in a sensible manner. We evaluate these heuristics in simulation. We identify some heuristics that significantly outperform their competitors and derive heuristic design guidelines

Mapping and Load-Balancing Iterative Computations on Heterogeneous Clusters

This paper is devoted to mapping iterative algorithms onto heterogeneous clusters. The application data is partitioned over the processors, which are arranged along a virtual ring. At each iteration, independent calculations are carried out in parallel, and some communications take place between consecutive processors in the ring. The question is to determine how to slice the application data into chunks, and to assign these chunks to the processors, so that the total execution time is minimized. One major difficulty is to embed a processor ring into a network that typically is not fully connected, so that some communication links have to be shared by several processor pairs. We establish a complexity result that assesses the difficulty of this problem, and we design a practical heuristic that provides efficient mapping, routing, and data distribution schemes

Mapping and Load-Balancing Iterative Computations on Heterogeneous Clusters with Shared Links

International audienceNo abstrac

Mapping and Load-Balancing Iterative Computations on Heterogeneous Clusters with Shared Links

International audienceNo abstrac