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Canadian School District Expands, Simplifies Data Center with KVM Technology: With 24,000 Students and 55 Different Sites, Richmond School District No. 38 Needed the Flexibility to Manage Its Servers Remotely from Any Location at Any Time-And ATEN's Line of Enterprise KVM Solutions Met the Challenge

2005· article· en· W222960660 sur OpenAlexaboutno aff
Chad Dupuis

Notice bibliographique

RevueT.H.E. Journal Technological Horizons in Education · 2005
Typearticle
Langueen
DomaineEngineering
ThématiqueExperimental Learning in Engineering
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésIntranetData centerRevenueSchool districtCurriculumFlexibility (engineering)EngineeringComputer scienceBusinessWorld Wide WebThe InternetManagementSociologyOperating systemPedagogy
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Most people would not view Richmond, British Columbia, as a technology haven. However, with software behemoth Microsoft (www.microsoft.com) located only two hours away in Redmond, WA, some of that technology pixie dust has been sprinkled on a progressive Canadian school district that is utilizing KVM (keyboard, video, mouse) switch solutions to power its data center. Richmond School District No. 38 prides itself on providing a safe and caring environment for its 24,000 students, who come from various backgrounds and have different ability levels. The district offers a variety of programs--everything from academic and fine arts, to technical and athletics--to give this diverse group of students a well-rounded education, while relying on the latest technology to support student learning. Demonstrating its commitment to technology, the district's Technology and Information Services Department even does its own in-house development, creating custom applications that generate revenue for the district. This department recently completed the centralization of its technology, allowing it to administer 80 percent of the district's devices from its cutting-edge Technology Services Center. The Richmond School District is composed of 55 different sites, all connected via a wide area network (WAN) powered by the British Columbia Provincial Learning Network (PLNet), a secure, high-speed network that connects all of British Columbia's public schools and colleges in a centrally managed intranet. Our department provides IT support and services for K-12 schools, the continuing education program, and administration systems, including accounting, student records, etc. The Technology Services Center manages a majority of the district's servers, while the board office houses the required IT infrastructure that supports several administration systems. The center currently accommodates 18 Intel-based (www.intel.com) servers running Windows 2000 Server, one Apple (www.apple.com) XServe G5 running Mac OS X, and an Apple Xserve RAID for hosting data storage. These servers are split between two racks that are protected by UPS (uninterruptible power supply) systems. A second site hosts administration systems that consist of one rack with five servers running Windows 2000 Server, one XServe G5 running Mac OS X, and a UPS battery backup system. We have been primarily focused on implementing this technology in a centrally managed environment, and as more rack-mounted servers were added to the Technology Services Center, we determined the need for a KVM management solution. Without a KVM solution, we were restricted to remote access only through Microsoft's Terminal Services. That option did not help us diagnose a server that was not loading the operating system. If there was a configuration problem preventing the server from loading Windows, we would have been forced to manually pull it out of the rack to fix the problem. We knew that implementing a KVM solution would allow us to diagnose the server from a central console at the rack. The real driving force behind our decision to select a KVM vendor rested upon our need for remote access to manage servers from any location, even during off hours. Given this need, we opted for a KVM over IP solution. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,102
Score d'incertitude au seuil0,839

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,020
Tête enseignante GPT0,262
Écart entre enseignants0,242 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2005
Routes d'admission1
Résumé présentoui

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