Proactive Migration for Dynamic Computation Load in Edge Computing
Bibliographic record
Abstract
The advent of the Internet-of-Things (IoT), which streams a wide range of computation-intensive applications with strict Quality of Service (QoS) requirements, has caused a paradigm shift from cloud computing to edge computing. Edge computing can drastically reduce latency and improve QoS. However, various dynamic changes can affect service continuity, thus requiring service migration. The dynamic computation load is one of the changes that are typically overlooked in service migration. In this paper, we propose the Dynamic Load-based Proactive Migration (DLPM) scheme. DLPM adopts a finite-state machine (FSM) that models the dynamic computation load, and proactively migrates computation tasks based on the associated transition probabilities. We formulate the service migration problem as an integer linear programming (ILP) optimization problem that aims to minimize the delay. We provide an analytical solution to the optimization problem using the KKT conditions and Lagrangian analysis. Performance evaluation shows that DLPM yields significant improvements in terms of delay and number of migrations compared to the reactive migration approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".