A Vacation Queue Based Optimization for Dynamic Application Placement in Edge Computing
Bibliographic record
Abstract
This paper studies the dynamic application placement for edge computing with a variety of random task arrivals (or task offloading requests). Since the storage capacity of the edge server is inherently limited, for better serving mobile devices with heterogeneous computation demands, the edge server is required to update its application placement, leading to a potential energy-latency paradox (i.e., frequent updates may introduce a high energy consumption while infrequent updates may result in the growth of latency). To this end, we propose a novel application placement policy, consisting of a response threshold and a waiting duration for installing and uninstalling the application for each type of task, respectively. Furthermore, we leverage the vacation queue for analyzing the performance of such system, and formulate a joint optimization problem for deriving the optimal configurations of application placement, along with the computation resource allocations, in minimizing the average service latency with a desired energy constraint. A branch-and-bound method integrating an inner convex approximation approach is proposed, and then evaluated with numerical simulations which demonstrate its superiority over counterparts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".