Offloading Dependent Tasks with Communication Delay and Deadline Constraint
Why this work is in the frame
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Bibliographic record
Abstract
We study the scheduling decision for an application consisting of dependent tasks, in a generic cloud computing system comprising a network of heterogeneous local processors and a remote cloud server. We formulate an optimization problem to find the offloading decision that minimizes the overall application execution cost, subject to an application completion deadline. Since this problem is NP-hard, we propose a heuristic algorithm termed Individual Time Allocation with Greedy Scheduling (ITAGS) to obtain an efficient solution. ITAGS first uses a binary-relaxed version of the original problem to allocate a completion deadline to each individual task, and then greedily optimizes the scheduling of each task subject to its time allowance. Through trace-based simulation using real applications, as well as various randomly generated task trees, we study the performance of ITAGS, highlighting the effect of the application deadline, communication delay, number of processors, and number of tasks. We further demonstrate the substantial performance advantage of ITAGS over existing alternatives.
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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 | 0.000 | 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