Sketch-based data placement among geo-distributed datacenters for cloud storages
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
With the increasing demand of big data applications, a variety of problems on how to operate the supporting infrastructures more intelligently and efficiently have attracted much attention in the literature. To optimize the data placement among distributed network locations is one of the fundamental problems, which aims at facilitating the data storage and access. However, traditional schemes meet challenges on the running time and the overhead introduced due to the increasing scale of datasets. Therefore, we propose a novel data placement scheme based on sketches to overcome these challenges. We first justify the effectiveness of applying the hypergraph sparsification on the data placement problem, and then present the method of constructing sparsifiers through the sketches of request traffic. Besides, the scheme features on the support of aggregating distributed sketches to make the decision and capturing the pattern of recent traffic through sliding windows. Finally, we obtain numerical results through simulations which confirm that the proposed scheme can place data effectively while reducing the introduced overhead in terms of algorithm running time, space and network traffic.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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