Evaluation of Reallocation Heuristics for Moldable Tasks in Computational Dedicated and non Dedicated Grids

Grid services often consist of remote sequential or rigid parallel application executions. However, moldable parallel applications, linear algebra solvers for example, are of great interest but requires dynamic tuning which has mostly to be done interactively if performances are needed. Thus, their grid execution depends on a remote and transparent submission to a possibly different batch scheduler on each site, and means an automatic tuning of the job according to the local load. In this report we study the benefits of having a middleware able to automatically submit and reallocate requests from one site to another when it is also able to configure the services by tuning their number of processors and their walltime. In this context, we evaluate the benefits of such mechanisms on four multi-cluster Grid setups, where the platform is either composed of several heterogeneous or homogeneous, dedicated or non dedicated clusters. Different scenarios are explored using simulations of real cluster traces from different origins. Results show that a simple scheduling heuristic is good and often the best. Indeed, it is faster and thus can take more jobs into account while having a small execution time. Moreover, users can expect more jobs finishing sooner and a gain on the average job response time between 10\% and 40\% in most cases if this reallocation mechanism combined to auto-tuning capabilities is implemented in a Grid framework. The implementation and the maintenance of this heuristic coupled to the migration mechanism in a Grid middleware is also simpler because less transfers are involved.L'appel à des services présents sur les grilles de calcul correspondent généralement à l'exécution d'une application séquentielle ou rigide. Cependant, il est possible d'avoir des applications parallèles moldables, telles que des solveurs linéaires, qui sont d'un grand intérêt, mais qui demandent une adaptation dynamique pour obtenir de bonnes performances. Leur exécution nécessite donc d'avoir un accès distant et transparent à différents gestionnaires de ressources, demandant donc une adaptation automatique de l'application en fonction de la charge locale. Dans ce rapport, nous étudions les bénéfices découlant de l'utilisation d'un intergiciel de grille capable de soumettre et de réallouer des requêtes d'un site à l'autre tout en configurant automatiquement les services en choisissant le nombre de processeurs ainsi que la durée d'exécution estimée. Dans ce contexte, nous évaluons les gains apportés par de tels mécanismes sur quatre grilles de calcul différentes où la plate-forme est composée de plusieurs grappes, homogène ou hétérogènes, dédiées ou non. Nous explorons différents scénarios par la simulation de traces de tâches provenant de réelles exécutions. Les résultats montrent que l'utilisation d'une heuristique d'ordonnancement simple est efficace, souvent amplement suffisante, voire la meilleure. En effet, elle est plus rapide à l'exécution et permet de prendre plus de requêtes en compte. Les utilisateurs peuvent espérer une majorité de requêtes terminant plus tôt si elle est utilisée, ainsi qu'une réduction du temps d'attente du résultat d'entre 10\% et 40\% dans la plupart des cas lorsque le mécanisme de réallocation couplé à l'adaptation automatique sont présents dans l'intergiciel. De plus, l'implantation et la maintenance de cette heuristique couplée au mécanisme de migration de tâches dans un intergiciel de grille est aussi plus facile car moins de tranferts sont nécessaires

QoS within Business Grid Quality of Service (BGQoS)

Differences in domain QoS requirements have been an obstacle to utilising Grid Computing for main stream applications. While the resource could potentially provide potentially vital services as well as providing significant computing and storage capabilities, the lack of high level QoS specification capabilities has proven to be a hindrance. Business Grid Quality of Service (BGQoS) is a QoS model for business-oriented applications on Grid computing systems. BGQoS defines QoS at a high level facilitating an easier request model for the Grid Resource Consumer (GRC) and eliminates confusion for the Grid Resource Provider in supplying the appropriate resources to meet the GRC requirements. It offers high level QoS specification within multi-domain environments in a flexible manner. Employing component separation and dynamic QoS calculation, it provides the necessary tools and execution environment for a scalable set of requirements tailoring to specific domain demands and requirements. Moreover, through reallocation, the model provides the insurance that all QoS requirements are met throughout the execution period, including migrating tasks to different resources if necessary. This process is not random and adheres to a set of conditions which ensures that task execution and resource allocation happen when and in accordance with execution requirements. This paper focuses on BGQoS’ flexibility and QoS capability. More specifically, the concentration is on core operations within BGQoS and the methods used in order to deliver a sustained level of QoS which meets the GRC’s requirements while being versatile and flexible such that it can be tailored to specific domains. This paper also presents an experimental evaluation of BGQoS. The evaluation investigates the behaviour and performance of the separate operations and components within BGQoS, and moreover, it presents an investigation and comparison between the different operations and their effect on the full model

Reducing the number of miscreant tasks executions in a multi-use cluster.

Exploiting computational resources within an organisation for more than their primary task offers great benefits – making better use of capital expenditure and provides a pool of computational power. This can be achieved through the deployment of a cycle stealing distributed system, where tasks execute during the idle time on computers. However, if a task has not completed when a computer returns to its primary function the task will be preempted, wasting time (and energy), and is often reallocated to a new resource in an attempt to complete. This becomes exacerbated when tasks are incapable of completing due to excessive execution time or faulty hardware / software, leading to a situation where tasks are perpetually reallocated between computers – wasting time and energy. In this work we investigate techniques to increase the chance of ‘good’ tasks completing whilst curtailing the execution of ‘bad’ tasks. We demonstrate, through simulation, that we could have reduce the energy consumption of our cycle stealing system by approximately 50%

Reducing the number of miscreant tasks executions in a multi-use cluster

Exploiting computational resources within an organisation for more than their primary task offers great benefits – making better use of capital expenditure and provides a pool of computational power. This can be achieved through the deployment of a cycle stealing distributed system, where tasks execute during the idle time on computers. However, if a task has not completed when a computer returns to its primary function the task will be preempted, wasting time (and energy), and is often reallocated to a new resource in an attempt to complete. This becomes exacerbated when tasks are incapable of completing due to excessive execution time or faulty hardware / software, leading to a situation where tasks are perpetually reallocated between computers – wasting time and energy. In this work we investigate techniques to increase the chance of ‘good’ tasks completing whilst curtailing the execution of ‘bad’ tasks. We demonstrate, through simulation, that we could have reduce the energy consumption of our cycle stealing system by approximately 50%

Resource provisioning in Science Clouds: Requirements and challenges

Cloud computing has permeated into the information technology industry in the last few years, and it is emerging nowadays in scientific environments. Science user communities are demanding a broad range of computing power to satisfy the needs of high-performance applications, such as local clusters, high-performance computing systems, and computing grids. Different workloads are needed from different computational models, and the cloud is already considered as a promising paradigm. The scheduling and allocation of resources is always a challenging matter in any form of computation and clouds are not an exception. Science applications have unique features that differentiate their workloads, hence, their requirements have to be taken into consideration to be fulfilled when building a Science Cloud. This paper will discuss what are the main scheduling and resource allocation challenges for any Infrastructure as a Service provider supporting scientific applications

Recent Advances in the Empirics of Organizational Economics

We present a survey of recent contributions in the empirical organizational economics, focusing on management practices and decentralization. Productivity dispersion between firms and countries has motivated the improved measurement of firm organization across industries and countries. There appears to be substantial variation in management practices and decentralization between countries, but especially within countries. Much of the poorer average management quality in countries like Brazil and India seems due to a "long tail" of poorly managed firms, which barely exist in the US. Many basic economic theories are supported by this new data. Some stylized facts include: (1) competition seems to foster improved management and decentralization; (2) larger firms, skillintensive plants and foreign multinationals appear better managed and are more decentralized; (3) family owned and managed firms appear to have worse management; (4) firms facing an environment of lighter labor market regulations, and more human capital intensive organizations specialize relatively more in "people management". There is evidence for complementarities between ICT, decentralization and management, but the relationship is complex and identification of the productivity effects of organizational practices remain a challenge for future research.productivity, organization, management, decentralization

Quality of service modeling for green scheduling in Clouds

International audienceBest known Cloud providers propose services under constraints of Service Level Agreement (SLA) definitions.The SLAs are composed of different Quality of Service (QoS) rules promised by the provider. Thus, the QoSin Clouds becomes more and more important. Precise definitions and metrics have to be explained. Thisarticle proposes an overview of Cloud QoS parameters as well as their classification, but also it defines usablemetrics to evaluate QoS parameters. Moreover, the defined QoS metrics are measurable and reusable in anyscheduling approach for Clouds. Energy consumption is an inherent objective in Cloud Computing, thus, it isalso considered. For evaluation purposes, two uncommon QoS parameters: Dynamism and Robustness are takeninto account in different Cloud virtual machines scheduling approaches. Validation is done through comparisonof common scheduling algorithms, including a genetic algorithm (GA), in terms of QoS parameters evolutionin time. Simulation results have shown that including various QoS parameters allows a deeper schedulingalgorithms analysi