Bibliographic record
Abstract
The problem of fair and efficient allocation of resources in Mobile Edge Computing (MEC) is of significant importance. Users in MEC, instead of utilizing the servers in the core network, can offload their tasks to the MEC-capable access points (APs) at the network's edge. Each user in MEC requires resources in a customized proportion to execute a task on a MEC server. Furthermore, The task input data and execution results are sent through shared wireless communication links to and from the MEC servers. Hence, MEC is characterized by allocating multiple communication and computation resources. Existing resource allocation rules generally consider Pareto Optimality (PO), Envy-Freeness (EF), Sharing Incentive (SI), and Strategy-Proofness (SP) as the most desirable fairness and efficiency properties. This dissertation proposes fair and efficient resource allocation mechanisms for three different MEC systems. We first consider the single-AP MEC systems, where the users must upload their tasks over a dedicated wireless communication link outside the computing servers. We propose a fair multi-resource allocation mechanism for this environment, termed Task Share Fairness with External Resource (TSF-ER), and prove that it satisfies all desirable fairness and efficiency properties. We then consider the multi-AP MEC systems where no user can establish multiple wireless connections simultaneously to multiple edge servers. Furthermore, each user is a selfish agent that chooses the single server for its job execution that maximizes its utility. We propose the Multi-resource Allocation Game Induced by the Kalai-Smorodinsky bargaining solution (MAGIKS) and prove that under discrete resource demands, it finds a Nash Equilibrium in polynomial time for any fixed server configuration. Furthermore, we prove that MAGIKS satisfies some desirable fairness and efficiency properties. Finally, we consider the multi-AP MEC systems where each user can establish wireless connection simultaneously to multiple edge servers and may experience different levels of wireless channel quality on different APs, so the user's channel bandwidth demand is not fixed. We show that the four fairness and efficiency properties are no longer compatible in this MEC environment. Hence, we propose a resource allocation rule, Maximum Task Product (MTP), that retains PO, EF, and SI.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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".