On the Placement of Edge Servers in Mobile Edge Computing
Bibliographic record
Abstract
With the wide deployment of mobile devices, a variety of different computation-intensive applications for mobile platforms, such as online games and virtual augmented reality, have emerged. However, due to the limited computation resources, mobile devices struggle to complete the required computation in time. One potential solution to the problem is mobile edge computing. Over the past years, there have been a series of studies on edge server placement. Most of the existing studies focus on the minimization of the delay between mobile devices and edge servers because many mobile applications are time-sensitive. However, workload balance is also an important metric because we do not prefer a scenario where some edge servers are idle while others are overloaded. In our research, we took both the delay and workload balance into account when we attempted to propose an effective edge server placement strategy. In addition, machine learning techniques were utilized to arrive at the appropriate placement method. Specifically, an innovative edge server placement strategy, which combines the advantages of cluster-based method and heuristic schemes, is proposed in the paper. Our experimental results indicate that, compared with the existing schemes, the proposed strategy can achieve lower communication delay and better workload balance simultaneously.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".