Proactive Migration for Dynamic Computation Load in Edge Computing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 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 it