Support for Provisioning and Configuration Decisions for Data Intensive Workflows
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
System provisioning, resource allocation, and configuration decisions for I/O-intensive workflow applications are complex even for expert users. Users face choices at multiple levels: allocating resources to individual sub-systems (e.g., the application layer, the storage layer) as well as configuring each of these optimally (e.g., replication level, chunk size, caching policies in case of storage) all having a large impact on the overall application performance. This paper presents a solution to address the problem of supporting these provisioning, allocation and configuration decisions for workflow applications. To enable selecting a good choice in a reasonable time, we propose an approach that accelerates the exploration of the configuration space based on a low-cost performance predictor that estimates total execution time of a workflow application in a given setup. We evaluate the predictor in a number of different scenarios including the Montage application: a workflow composed of over 7,500 tasks structured in 10 different stages with varying characteristics. Our evaluation shows that: (i) the predictor is effective in identifying the desired system configuration, (ii) it can scale to model a complex workflow application run on a 100-node cluster, while (iii) using orders of magnitude less resources than running the actual application. Additionally, we extend the predictor to estimate the energy usage of the system, and we present our experience with incorporating it in the development process of a distributed storage system.
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