Cloud server job selection and scheduling in mobile computation offloading
Bibliographic record
Abstract
In this paper we consider a system that uses computation offloading, where an infrastructure-based cloud server executes jobs on behalf of a set of mobile devices. In this type of system, mobile job completion times include the latency needed for uploading to the cloud server. Since the processed jobs are subject to hard deadline constraints, this can introduce energy unfairness where mobile devices with poor channel conditions do not fully benefit from computation offloading. This unfairness however, can be compensated for, by dynamic scheduling at the cloud server. We first derive an offline scheduler using an integer linear program which uses a min-max energy objective and non-preemptive cloud server scheduling. We then introduce three online scheduling algorithms. The first is referred to as First-Generated-First-Served (FGFS) where jobs that are generated earlier are given priority at the cloud server. A modified version, referred to as γ-Ratio Accepted FGFS (γ-FGFS) is proposed where acceptance of a job execution partition is subject to an energy threshold test. We also introduce a version of this algorithm, γ-Ratio Accepted Earliest Deadline First (γ-EDF) which uses earliest deadline first scheduling to test for job partition feasibility. Various performance results are presented which show the improvements in energy fairness possible with the proposed schedulers.
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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 | 0.000 | 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 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".