Personal Volunteer Computing

We propose personal volunteer computing, a novel paradigm to encourage technical solutions that leverage personal devices, such as smartphones and laptops, for personal applications that require significant computations, such as animation rendering and image processing. The paradigm requires no investment in additional hardware, relying instead on devices that are already owned by users and their community, and favours simple tools that can be implemented part-time by a single developer. We show that samples of personal devices of today are competitive with a top-of-the-line laptop from two years ago. We also propose new directions to extend the paradigm

Flauncher and DVMS -- Deploying and Scheduling Thousands of Virtual Machines on Hundreds of Nodes Distributed Geographically

International audienceAlthough live migration of virtual machines has been an active area of research over the past decade, it has been mainly evaluated by means of simulations and small scale deployments. Proving the relevance of live migration at larger scales is a technical challenge that requires to be able to deploy and schedule virtual machines. In the last year, we succeeded to tackle such a challenge by conducting experiments with Flauncher and DVMS, two frameworks that can respectively deploy and schedule thousands of virtual machines over hundreds of nodes distributed geographically across the Grid'5000 testbed

Gestion adaptative de l'énergie pour les infrastructures de type grappe ou nuage

National audienceDans un contexte d'utilisation de ressources hétérogènes, la performance reste le critère traditionnel pour la planification de capacité. Mais, de nos jours, tenir compte de la variable énergétique est devenu une nécessité. Cet article s'attaque au problème de l'efficacité énergétique pour la répartition de charge dans les systèmes distribués. Nous proposons une gestion efficace en énergie des ressources par l'ajout de fonctionnalités de gestion des évènements liés à l'énergie, selon des règles définies par l'utilisateur. Nous implémentons ces fonctionnalités au sein de l'intergiciel DIET, qui permet de gérer la répartition de charge afin de mettre en évidence le cout des compromis entre la performance et la consommation d'énergie. Notre solution et son intérêt sont validés au travers d'expériences en évaluant la performance et la consommation électrique mettant en concurrence trois politiques d'ordonnancement. Nous mettons en avant le gain obtenu en terme énergétique tout en essayant de minimiser les écarts de performance. Nous offrons également à l'intergiciel responsable de l'ordonnancement une réactivité face aux variations énergétiques

Energy-Aware Server Provisioning by Introducing Middleware-Level Dynamic Green Scheduling

International audienceSeveral approaches to reduce the power consumption of datacenters have been described in the literature, most of which aim to improve energy efficiency by trading off performance for reducing power consumption. However, these approaches do not always provide means for administrators and users to specify how they want to explore such trade-offs. This work provides techniques for assigning jobs to distributed resources, exploring energy efficient resource provisioning. We use middleware-level mechanisms to adapt resource allocation according to energy-related events and user-defined rules. A proposed framework enables developers, users and system administrators to specify and explore energy efficiency and performance trade-offs without detailed knowledge of the underlying hardware platform. Evaluation of the proposed solution under three scheduling policies shows gains of 25% in energy-efficiency with minimal impact on the overall application performance. We also evaluate reactivity in the adaptive resource provisioning

Nu@ge: Towards a solidary and responsible cloud computing service

Best Paper AwardInternational audienceThe adoption of cloud computing is still limited by several legal concerns from companies. One of those reasons is the data sovereignty, as data can be physically host in sensible locations, resulting in a lack of control for companies. In this paper, we present the Nu@ge project aimed at building a federation of container-sized datacenter on the French territory. Nu@ge provides a software stack that enables companies to put independent datacenters in cooperation in a national mesh. Additionally, a prototype of a container-sized datacenter has been validated and patented

Parallel Differential Evolution approach for Cloud workflow placements under simultaneous optimization of multiple objectives

International audienceThe recent rapid expansion of Cloud computing facilities triggers an attendant challenge to facility providers and users for methods for optimal placement of workflows on distributed resources, under the often-contradictory impulses of minimizing makespan, energy consumption, and other metrics. Evolutionary Optimization techniques that from theoretical principles are guaranteed to provide globally optimum solutions, are among the most powerful tools to achieve such optimal placements. Multi-Objective Evolutionary algorithms by design work upon contradictory objectives, gradually evolving across generations towards a converged Pareto front representing optimal decision variables – in this case the mapping of tasks to resources on clusters. However the computation time taken by such algorithms for convergence makes them prohibitive for real time placements because of the adverse impact on makespan. This work describes parallelization, on the same cluster, of a Multi-Objective Differential Evolution method (NSDE-2) for optimization of workflow placement, and the attendant speedups that bring the implicit accuracy of the method into the realm of practical utility. Experimental validation is performed on a real-life testbed using diverse Cloud traces. The solutions under different scheduling policies demonstrate significant reduction in energy consumption with some improvement in makespan

Ensemble-based network edge processing

Estimating energy costs for an industrial process can be computationally intensive and time consuming, especially as it can involve data collection from different (distributed) monitoring sensors. Industrial processes have an implicit complexity involving the use of multiple appliances (devices/ sub-systems) attached to operation schedules, electrical capacity and optimisation setpoints which need to be determined for achieving operational cost objectives. Addressing the complexity associated with an industrial workflow (i.e. range and type of tasks) leads to increased requirements on the computing infrastructure. Such requirements can include achieving execution performance targets per processing unit within a particular size of infrastructure i.e. processing & data storage nodes to complete a computational analysis task within a specific deadline. The use of ensemblebased edge processing is identifed to meet these Quality of Service targets, whereby edge nodes can be used to distribute the computational load across a distributed infrastructure. Rather than relying on a single edge node, we propose the combined use of an ensemble of such nodes to overcome processing, data privacy/ security and reliability constraints. We propose an ensemble-based network processing model to facilitate distributed execution of energy simulations tasks within an industrial process. A scenario based on energy profiling within a fisheries plant is used to illustrate the use of an edge ensemble. The suggested approach is however general in scope and can be used in other similar application domains

Towards a methodology for building dynamic urgent applications on continuum computing platforms

International audienc

Pando: Personal Volunteer Computing in Browsers

The large penetration and continued growth in ownership of personal electronic devices represents a freely available and largely untapped source of computing power. To leverage those, we present Pando, a new volunteer computing tool based on a declarative concurrent programming model and implemented using JavaScript, WebRTC, and WebSockets. This tool enables a dynamically varying number of failure-prone personal devices contributed by volunteers to parallelize the application of a function on a stream of values, by using the devices' browsers. We show that Pando can provide throughput improvements compared to a single personal device, on a variety of compute-bound applications including animation rendering and image processing. We also show the flexibility of our approach by deploying Pando on personal devices connected over a local network, on Grid5000, a French-wide computing grid in a virtual private network, and seven PlanetLab nodes distributed in a wide area network over Europe.Comment: 14 pages, 12 figures, 2 table