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Record W1813777996 · doi:10.1109/compsac.2015.31

Seamless Live Virtual Machine Migration with Cloudlets and Multipath TCP

2015· article· en· W1813777996 on OpenAlexaff
Fikirte Teka, Chung–Horng Lung, Samuel A. Ajila

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicIoT and Edge/Fog Computing
Canadian institutionsCarleton University
Fundersnot available
KeywordsCloudletComputer scienceMultipath TCPCloud computingComputer networkDowntimeVirtual machineMobile cloud computingMobile deviceLive migrationOperating systemMultipath propagationVirtualization

Abstract

fetched live from OpenAlex

Techniques using a nearby virtual machine (VM) based cloudlet have been proposed for mobile cloud computing (MCC) to enhance the performance of real-time resource-intensive mobile applications. After a mobile device (MD) discovers a cloudlet in the vicinity, it takes time to set up a VM inside the cloudlet before data offloading from the MD to the VM starts. The time between discovering the cloudlet and actual offloading of data is considered as a service initiation time. When multiple cloudlets are presented in a nearby geographical location, initiating a service with each cloudlet may be frustrating for cloudlet users when changing location. In order to eliminate the delay caused by the service initiation time after moving away from the source cloudlet, this paper proposed a seamless live VM migration between neighbour cloudlets. A seamless live VM migration is achieved with the prior knowledge of the migrating VM IP address in the destination cloudlet and more importantly with multipath TCP (MPTCP). We have performed a number of experiments to validate the proposed approach using the Linux KVM hyper visor. The experimental results demonstrate the feasibility of the proposed approach and also performance improvement. Specifically, there is zero downtime at the destination cloudlet after the migration is completed.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.979
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.221
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2015
Admission routes1
Has abstractyes

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