Evaluating and Improving LXC Container Migration Between Cloudlets Using Multipath TCP
Bibliographic record
Abstract
The advent of the Cloudlet concept-a "small data center" close to users at the edge is to improve the Quality of Experience ( QoE) of end users by providing resources within a one-hop distance.Many researchers have proposed using virtual machines (VMs) as such service-provisioning servers.However, seeing the potentiality of containers, this thesis adopts Linux Containers (LXC) as Cloudlet platforms.To facilitate container migration between Cloudlets, Checkpoint and Restore in Userspace (CRIU) has been chosen as the migration tool.Since the migration process goes through the Wide Area Network (WAN), which may experience network failures, the Multipath TCP (MPTCP) protocol is adopted to address the challenge.The multiple subflows established within a MPTCP connection can improve the resilience of the migration process and reduce migration time.Experimental results show that LXC containers are suitable candidates for the problem and MPTCP protocol is effective in enhancing the migration process.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".