Hybrid-MiGrror: An Extension to the Hybrid Live Migration to Support Mobility in Edge Computing
Bibliographic record
Abstract
User-Equipments (UEs) capable of working with cloud computing have grown exponentially in recent years, leading to a significant increase in the amount of data production. Moreover, upcoming Internet-of-Things (IoT) applications such as virtual and augmented reality, video streaming, intelligent transportation, and healthcare will require low latency, communications, and processing. Edge computing is a revolutionary criterion in which dispersed edge nodes supply resources near end devices because of the limited resources available on UEs. Rather than transmitting massive amounts of data to the cloud, edge nodes could filter, analyze, and process the data they receive using local resources. Mobile Edge Computing (MEC), in particular, when user mobility is considered, has the potential to significantly reduce processing delays and network traffic between UEs and servers. This research demonstrated a novel technique for migration that minimizes delay and downtime by utilizing edge computing. Our proposed method syncs more frequently than the pre-copy method which is the most used migration method that synchronizes (sync) the source and destination only based on multiple rounds. When compared to established migration methodologies, our results indicate that our mechanism has less latency, downtime, migration time, and packet loss. These results allow delay-sensitive applications that require ultra-low latency to function smoothly during migration.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".