On Cloud computational models and the heterogeneity challenge
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Cloud computing is by far the most cost-effective technology for hosting Internet-scale services and applications. The MapReduce model, in particular, is largely used nowadays in Cloud infrastructures to meet the demand of large-scale data and computation intensive applications. Despite its success, the implications of MapReduce on the management of Cloud workload and cluster resources are still largely unstudied. In this article, we show that dealing with the heterogeneity of workloads and machine capabilities is a key challenge. In today’s cloud environment, workloads can have varied sizes, lengths, resource requirements, and arrival rates. The machines also have varied CPU, memory, I/O speed, and network bandwidth capacities. Jointly they pose difficult challenges pertaining, among others, to job scheduling, task and data placement, resource sharing and resource allocation. We analyze the heterogeneity challenge in these specific problem domains and survey the representative state-of-the-art works that try to address them. We found that although advances are made that partially address some of the outlined challenges, there are even more open challenges yet to be explored, and this topic at large is ripe for scientific contributions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it