An Efficient Elastic Scaling, Service Deployment, and Task Allocation Algorithm for Mobile Edge Computing
Bibliographic record
Abstract
Mobile Edge Computing (MEC) can provide low-latency and workload-intensive computing services to user equipments. Elastic scaling, service placement, and task scheduling are key techniques affecting the performance of an MEC system. Elastic scaling is to determine the set of active servers and also the amount of computation resources allocated for each service deployed at a server, service deployment is to determine the set of services/applications to be deployed at each server, and task scheduling is to determine how tasks are assigned among different servers. In this paper, study an MEC system where user demands fluctuate spatially and temporally. Our objective is to minimize the total power consumption and task response time. We accordingly formulate the joint optimization of elastic scaling, service placement, and task scheduling in this case as a Mixed-Integer Nonlinear Programming (MINLP). Due to the hardness of the problem, we propose an efficient joint elastic scaling, service placement, and task scheduling algorithm. Simulation results show that our proposed algorithm can effectively reduce the system cost as compared with baseline algorithms.
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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 | 0.002 |
| 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) | 0.002 | 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".