LXC Container Migration in Cloudlets under Multipath TCP
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 growing popularity of mobile devices and Internet of Things (IoT) has inspired the advent of the Cloudlet concept-a "small data center" close to users at the edge. It is believed that the Quality of Experience (QoE) of end users would greatly improve if they can access required resources within a one-hop distance from Cloudlet servers. Over the years, many researchers have proposed using virtual machines (VMs) as such service-provisioning servers. However, seeing the potentiality of containers-a lightweight virtualization tool, this paper adopts LXC containers as Cloudlet platforms. To facilitate container migration between Cloudlets, CRIU (Checkpoint/Restore in Userspace) has been chosen as the migration tool. Since the migration process goes through the Wide Area Network (WAN), which may experience congestion or network failures, this paper adopts the MPTCP (Multipath TCP) protocol to address the challenge. The multiple subflows established within a MPTCP connection can improve the resilience of the migration process and reduce migration time. We have conducted a number of experiments to validate the proposed approach. The experimental results show that LXC containers are suitable candidates for the problem and MPTCP protocol is effective in enhancing the migration process.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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