One-restart algorithm for scheduling and offloading in a hybrid cloud
Bibliographic record
Abstract
The hybrid cloud architecture utilizes both privately owned cloud servers and rented instances from public cloud providers, to offer flexible services that are particularly suited to enterprise computing. The task scheduler at a hybrid cloud decides both the selection of tasks to be offloaded to the public cloud and the scheduling of the remaining tasks on the processors at the private cloud. In this work, we consider the problem of minimizing a weighted sum of the makespan at the private cloud and the offloading cost to the public cloud. In contrast to prior works, we do not assume that the task processing times are known a priori. We show that the original problem can be solved by the same algorithms designed toward minimizing the maximum between the makespan and the weighted offloading cost, only with doubling of the competitive ratio. Furthermore, the latter problem can be equivalently transformed into a makespan minimization problem with unrelated processors. In the case where all tasks arrive at time zero, we propose a Greedy-One-Restart (GOR) algorithm based on online estimation of the unknown processing times, and one-time cancellation and rescheduling of tasks that turn out to require long processing times. We derive its competitive ratio and show that it is upper bounded on the order of the square root of the number of private processors, which is a substantial improvement over the best known algorithms in the literature. We present also a tight constant competitive ratio for the special two-processor case. In the case where tasks arrive dynamically with unknown arrival times, we extend GOR to Dynamic-GOR (DGOR) and find its competitive ratio. Further simulation results demonstrate that GOR and DGOR are favorable also in terms of average performance, in comparison with the well-known list scheduling algorithm and idealized offline algorithms.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".