Intelligent Edge-Based Service Provisioning Using Smart Cloudlets, Fog and Mobile Edges
Bibliographic record
Abstract
The advancements of mobile devices and the rapid development of computing applications are encouraging a trend shift from traditional, centralized mobile cloud computing to decentralized mobile edge computing (e.g., cloudlet, fog, and multi-access edge computing) focused on the quality of service (QoS) of applications and quality of experience of user equipment (UEs). Such edge technologies enable service and resource provisioning in the vicinity of UEs, drastically reducing the network propagation delay and backhaul load. However, the emergence of computation-intensive functions, latency-sensitive tasks, and bandwidth-hungry activities make QoS-aware scheduling and resource allocation in the edge environment an open research issue. In this article, we discuss the edge-based service pro-visioning issues from the perspective of artificial intelligence (AI). We first summize the three main edge-enabling technologies. The core functioning and research challenges are presented in the context of a layered edge environment. A comparison is made among these technologies with the presentation of similarities and differences. The edge processing model is further elaborated for centralized architecture and distributed architecture, respectively. Then, we identify and categorize AI-based technologies for the UE layer, edge platform layer, and inter-edge layer regarding core edge-based service provisioning issues. Finally, we highlight open challenges and research directions, and we conclude that the number of potential issues in the edge can be properly solved by AI-based technologies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".