Resource Management in Virtualized Clouds
Bibliographic record
Abstract
Resource management is of paramount importance in achieving high performance in Cloud environments.Different from traditional parallel and distributed systems, resource virtualization is a key feature in Cloud systems.Although researchers have been developing various strategies and techniques to address the resource management issues in virtualized Clouds, they, as this special issue shows, still face many new research challenges.This special issue aims to report the latest scientific advances in resource management techniques for virtualized Clouds, especially on the topics including VM consolidation strategies, scheduling techniques, techniques for managing various types of virtualized resource, and VM management for various types of application.This issue received 47 high quality submissions.After the rigorous review process, 23 papers were accepted in this issue, documenting the relevant research from China, Spain, USA, Australia, Korea, Mexico, Luxembourg, Russia, France, and so forth.The research presented in these 23 papers broadly covers the interesting scope of this special issue.Among these papers, scheduling strategies are proposed for various types of application and platform.W. Zheng et al. from Xiamen University in China proposed a randomization approach for scheduling stochastic workflows.J. M. Cortés-Mendoza et al. from CICESE Research Center in Mexico developed a new method for scheduling VoIP (Voice over Internet Protocol) tasks in Clouds.W. Lin et al. from South China University of Technology in China designed a task scheduling algorithm for heterogeneous virtual clusters.J. P. Orellana et al. from University of Castilla-La Mancha in Spain investigated scheduling strategies for FPGA devices equipped in Clouds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".