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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".