A resource management technique for processing deadline-constrained multi-stage workflows
Why this work is in the frame
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Bibliographic record
Abstract
The use of cloud computing that provides resources on demand to various types of users, including enterprises as well as engineering and scientific institutions, is growing rapidly. An effective resource management middleware is necessary to harness the power of the underlying distributed hardware in a cloud. Two of the key operations provided by a resource manager are resource allocation (matchmaking) and scheduling. This paper concerns the problem of matchmaking and scheduling an open stream of multi-stage jobs (or workflows ) with Service Level Agreements (SLAs) on a cloud or cluster. Multi-stage jobs require service from multiple system resources and are characterized by multiple phases of execution. This paper presents a resource allocation and scheduling technique called RM-DCWF: Resource Management Technique for Deadline-constrained Workflows that can efficiently matchmake and schedule an open stream of multi-stage jobs with SLAs, where each SLA is characterized by an earliest start time, an execution time, and a deadline. A rigorous simulation-based performance evaluation of RM-DCWF is conducted using synthetic workloads derived from real scientific workflows. In addition, the impact of various system and workload parameters on system performance is investigated. The results of this performance evaluation demonstrate the effectiveness of RM-DCWF as captured in a low number of jobs missing their deadlines.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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