A special issue from the international conference on performance engineering 2013
Notice bibliographique
Résumé
The International Conference on Performance Engineering (ICPE) took place in Prague in the Czech Republic on April 21–24, 2013. The conference was attended by over 160 delegates. The main research track for ICPE attracted 42 submissions from which 20 full papers and two short papers were accepted for presentation. The Program Committee comprised over 50 leading academics and industrialists from 12 countries. There was also an Industry and Experience track that focused on the application of research results and performance-related technologies to industrial performance-engineering problems and the reporting of insightful performance results based on industry experience. This track received 22 submissions of which eight were selected for presentation. The papers accepted to these two main tracks covered a range of topics addressing performance-driven software development and various flavors of modeling, including performance, survivability, and scalability modeling. The development of representative workloads and benchmarks was also well represented. There were then a number of papers that focused on performance aspects of cloud-related systems and more general aspects of scheduling and load balancing. ICPE also had a Vision/Work-in-Progress track that provided a forum for researchers to present and discuss newly-emerging ideas and showcase ongoing work that shows promise. This track received 18 submissions of which 10 were accepted for short presentations at the conference. At the end of the conference, the authors of 11 papers from the Research Track and the Industry and Experience Track were invited to submit extended versions of their papers for consideration in this special issue. The extended papers submitted went through a new round of reviews, and this led to the selection of the six contributions that appear here. We would like to thank the authors of all the extended papers for their diligence in preparing their manuscripts for consideration and the members of the special issue Program Committee for their many insightful and helpful suggestions. The contributions include a new analytical model for estimating the survivability of a smart power grid 1; a method for identifying (‘blaming’) software components and/or component compositions that result in performance errors in a component-based application 2; a new performance model for Hadoop's MapReduce framework that is based on modeling separately the generic and application-specific phases of the framework 3; a measurement-based study of the performance of web servers running on multi-core systems that can be used to understand the scaling behavior of a web server and to help find server configurations that optimize performance for a given workload 4; a method for dynamically switching between different time-series-based forecasting methods for an evolving workload trace whose behavior is controlled solely by user-defined forecasting objectives 5; a resampling technique that constructs a workload model from a supercomputer job trace that can then be used to conduct a simulation-based analysis of the performance of a parallel job scheduler 6. This rich diversity of research contributions to performance engineering reflects the evolution of the field and the roots of ICPE, which evolved from a performance evaluation workshop sponsored by the Standard Performance Evaluation Corporation (SPEC) and from the Workshop on Software and Performance (WOSP). While the SPEC workshop focused on benchmarking and encompassed many areas of software and hardware evaluation, the main focus of WOSP was the development of methodologies for software performance modeling, prediction, and engineering. Besides reflecting this diversity, the contributions in this special issue also point to the potential for fruitful collaborations and mutual learning between these two segments of the performance evaluation community. We are excited by the potential future opportunities that this union affords and hope that this special issue provides a useful insight into some of the current key topics.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,012 | 0,007 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,183 | 0,102 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».