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Predicting the performance of schedulers is a notoriously difficult task. As a consequence, grid users might be tempted to work around the standard grid middleware by designing specific strategies, which would be counterproductive if generally adopted. On the other hand, Machine Learning has been successfully applied to performance prediction in distributed and shared environments. This paper reports on experiments on predicting the basic parameters of scheduling in the EGEE framework.
Two recurrent questions often appear when solving numerous real world policy search problems. First, the variables defining the so called Markov Decision Process are often continuous, that leads to the necessity for discretization of the considered state/action space or the use of a regression model, often non-linear, to approach the Q-function nee- ded in the reinforcement learning paradigm. Second, the markovian hypothesis is made which is often strongly discutable and can lead to unacceptably suboptimal resulting policies. In this paper, the job scheduling problem in grid infrastructure is modeled as a continuous action-state space, multi-objective reinforcement learning problem, under realistic assumptions ; the high level goals of users, administrators, and shareholders are captured through simple utility functions. So, formalizin...
The increase in grid resources and workload requires more and more administrators to monitor and operate them. The preliminary step for a more autonomic [1-2] grid is to create concise and meaningful descriptions of the complex processes at work in the interaction of e-science requirements and grid middleware. These descriptions provide online, high-level, and information-rich monitoring of EGEE status and can be exploited in dashboards. This work is part of the Grid Observatory cluster.
Large scale production grids are an important case for autonomic computing. They follow a mutualization paradigm: decision-making (human or automatic) is distributed and largely independent, and, at the same time, it must implement the highlevel goals of the grid management. This paper deals with the scheduling problem with two partially conflicting goals: fairshare and Quality of Service (QoS). Fair sharing is a wellknown issue motivated by return on investment for participating institutions. Differentiated QoS has emerged as an important and unexpected requirement in the current usage of production grids. In the framework of the EGEE grid (one of the largest existing grids), applications from diverse scientific communities require a pseudo-interactive response time. More generally, seamless integration of the grid power into everyday...
Grids organize resource sharing, a fundamental requirement of large scientific collaborations. Seamless integration of grids into everyday use requires responsiveness, which can be provided by elastic Clouds, in the Infrastructure as a Service (IaaS) paradigm. This paper proposes a model-free resource provisioning strategy supporting both requirements. Provisioning is modeled as a continuous action-state space, multi-objective reinforcement learning (RL) problem, under realistic hypotheses; simple utility functions capture the high level goals of users, administrators, and shareholders. The model-free approach falls under the general program of autonomic computing, where the incremental learning of the value function associated with the RL model provides the so-called feedback loop. The RL model includes an approximation of the value f...
In the 70s, the transition from batch systems to interactive computing fueled the widespread diffusion of advances in integrated circuit technology. Grids are facing a similar challenge, namely the seamless integration of the grid power into everyday use. One critical component for this integration is responsiveness, the capacity to support on-demand computing and interactivity. A large contributor to responsiveness is the Quality of Service (QoS) for the job execution time. Grid scheduling is involved at two levels in order to provide QoS: the policy level and the implementation level. The main contributions of this paper are as follows. First, we present a detailed analysis of the performance of the EGEE grid with respect to responsiveness. Second, we define and demonstrate a virtualization scheme, which achieves QoS, schedulability ...
Grids are facing the challenge of moving from batch systems to interactive computing. In the 70s, standalone computer systems have met this challenge, and this was the starting point of pervasive computing. Meeting this challenge will allow grids to be the infrastructure for ambient intelligence and ubiquitous computing. This paper shows that EGEE, the largest world grid, does not yet provide the services required for interactive computing, but that it is amenable to this evolution through relatively modest middleware evolution. A case study on medical image analysis exemplifies the particular needs of ultra-short jobs.
Large scale production grids are a major case for autonomic computing. Following the classical definition of Kephart, an autonomic computing system should optimize its own behavior in accordance with high level guidance from humans. This central tenet of this paper is that the combination of utility functions and reinforcement learning (RL) can provide a general and efficient method for dynamically allocating grid resources in order to optimize the satisfaction of both endusers and participating institutions. The flexibility of an RLbased system allows to model the state of the grid, the jobs to be scheduled, and the high-level objectives of the various actors on the grid. RL-based scheduling can seamlessly adapt its decisions to changes in the distributions of inter-arrival time, QoS requirements, and resource availability. Moreover, ...
The first barrier to improved energy efficiency is the difficulty of collecting data on the energy consumption of individual components of data centers, and the lack of overall data collection. GCO collects monitoring data on energy consumption of a large computing center, and publish them through the Grid Observatory. These data include the detailed monitoring of the processors and motherboards, as well as the global site information, such as overall consumption and overall cooling. A second barrier is making the collected data usable. The difficulty is to make the data readily consistent and complete, as well as understandable for further exploitation. For this purpose, GCO opts for an ontological approach in order to rigorously define the semantics of the data (what is measured) and the context of their production (how are they acqu...
The goal of the Grid Observatory project (GO) is to contribute to an experimental theory of large grid systems by integrating the collection of data on the behaviour of the flagship European Grid Infrastructure (EGI) and its users, the development of models, and an ontology for the domain knowledge. The GO gives access to a database of grid usage traces available to the wider computer science community without the need of grid credentials. The paper presents the architecture of the digital curation process enacted by the GO and examples of their exploitation.
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