An Adaptive System to Allocate VM in Cloud for Secure Remote Access using Autoregression
Bibliographic record
Abstract
This thesis proposes an adaptive system to allocate virtual machines in a cloud environment to reduce clients' waiting time while reducing the idle resources for the service provider.Further, the thesis demonstrates the viability of the proposed system via a prototype built using the Citrix XenServer and a machine learning algorithm which makes the system capable of working with minimum human interactions.The proposed architecture is designed in collaboration with and based on the requirements of DLS Technology so that they can migrate their flagship product (vKey) to a cloud environment keeping security and performance as a priority.The incoming requests from clients are handled by a pool manager which takes smart decisions thus making the user experience seamless.A performance analysis of the prototype is carried out to prove the effectiveness of the proposed strategies.I, Jasmeet Singh, take this opportunity to express my deep sense of gratitude to all the people who have developed me in the successful completion of this thesis.As no task is a single man's feat, various factors, situations, and person integrate to provide the background to accomplish a task.Several persons with whom I have interacted significantly helped me to the successful completion of this thesis.I own a deep sense of gratitude to Professor Marc St-Hilaire and Professor Shikharesh Majumdar.I can't convey my thanks in words for there tremendous support, help, and motivation throughout the process.I would like to owe the same gratitude to Eric She, Jordan Kurosky and Sibyl Weng from DLS Technology, Ottawa for believing in me with their confidential work and providing enormous help.I would also like to convey my thanks to Hindal Mirza from Ontario Centers of Excellence
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".