Environnement (informatique)

Résumé

En informatique, un environnement désigne, pour une application, l'ensemble des matériels et des logiciels système, dont le système d'exploitation, sur lesquels sont exécutés les programmes de l'application. On précise souvent le type d'environnement dont il s'agit. Ce sont généralement les suivants, par ordre d'apparition dans le cycle de vie :

L'environnement de développement, sur lequel sont développés les programmes de l'application,
L'environnement de qualification, sur lequel sont testés les programmes de l'application,
L'environnement de formation, sur lequel les utilisateurs sont formés à l'application,
L'environnement de production, sur lequel sont exécutés les programmes opérationnellement.

Un environnement est le plus souvent implanté sur un seul serveur, mais il peut y avoir des exceptions (cas de l'informatique distribuée). Il peut y avoir plusieurs environnements hébergés sur un même serveur (l'environnement de qualification et l'environnement de formation pa

Source officielle

https://fr.wikipedia.org/wiki/Environnement_(informatique)

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Publications associées (18)

Chargement

Afficher plus

Publications associées

Chargement

Personnes associées (3)

Personnes associées

Chargement

Unités associées (3)

Unités associées

Chargement

Concepts associés (1)

Concepts associés

Chargement

Cours associés (3)

The main objective of the course is to introduce the concept of space system design and engineering. The course will describe the various subsystems involved in the design of a satellite. It will also describe the techniques of systems engineering that are used to obtain a coherent satellite design.

The course will discuss classic material as well as recent advances in computer vision and machine learning relevant to processing visual data with a primary focus on embodied intelligence and vision for active agents.

This course focuses on goal-directed design and interaction design, two subjects treated in depth in the Cooper book (see reference below). To practice these two methods, we propose a design challenge, which is further divided into mini-projects evenly spaced throughout the semester.

Cours associés

Chargement

Séances de cours associées (4)

Séances de cours associées

Chargement

Publications associées (18)

PerfIso: Performance Isolation for Commercial Latency-Sensitive Services

Sameh Mohamed Elnikety, Calin Iorgulescu

Large commercial latency-sensitive services, such as web search, run on dedicated clusters provisioned for peak load to ensure responsiveness and tolerate data center outages. As a result, the average load is far lower than the peak load used for provisioning, leading to resource under-utilization. The idle resources can be used to run batch jobs, completing useful work and reducing overall data center provisioning costs. However, this is challenging in practice due to the complexity and stringent tail-latency requirements of latency-sensitive services. Left unmanaged, the competition for machine resources can lead to severe response-time degradation and unmet service-level objectives (SLOs). This work describes PerfIso, a performance isolation framework which has been used for nearly three years in Microsoft Bing, a major search engine, to colocate batch jobs with production latency-sensitive services on over 90,000 servers. We discuss the design and implementation of PerfIso, and conduct an experimental evaluation in a production environment. We show that colocating CPU-intensive jobs with latency-sensitive services increases average CPU utilization from 21% to 66% for off-peak load without impacting tail latency.

USENIX ASSOC2018

Chargement

The USENIX Association2018

Chargement

Dynamic instruction mixes form an important part of the toolkits of performance tuners, compiler writers, and CPU architects. Instruction mixes are traditionally generated using software instrumentation, an accurate yet slow method, that is normally limited to user-mode code. We present a new method for generating instruction mixes using the Performance Monitoring Unit (PMU) of the CPU. It has very low overhead, extends coverage to kernel-mode execution, and causes only a very modest decrease in accuracy, compared to software instrumentation. In order to achieve this level of accuracy, we develop a new PMU-based data collection method, Hybrid Basic Block Profiling (HBBP). HBBP uses simple machine learning techniques to choose, on a per basic block basis, between data from two conventional sampling methods, Event Based Sampling (EBS) and Last Branch Records (LBR). We implement a profiling tool based on HBBP, and we report on experiments with the industry standard SPEC CPU2006 suite, as well as with two large-scale scientific codes. We observe an improvement in runtime compared to software instrumentation of up to 76x on the tested benchmarks, reducing wait times from hours to minutes. Instruction attribution errors average 2.1%. The results indicate that HBBP provides a favorable tradeoff between accuracy and speed, making it a suitable candidate for use in production environments.

2018

Chargement

Afficher plus