Lightweight Robust Optimizer for Distributed Application Deployment in Multi-Clouds
Bibliographic record
Abstract
Cloud computing refers to the applications and services that run on a distributed network using virtualized resources and accessed by common Internet protocols and networking standards.In cloud computing, an edge cloud is close to some of the end users, to give faster service for very demanding applications.Transactions that require heavy processing capacity and longer processing times are better carried out at the core cloud.To deploy applications with many tasks across a cloud infrastructure, many goals must be satisfied, which poses a large and complex optimization problem.Meeting latency constraints is an important requirement in future cloud applications and is critical in task deployment.This thesis creates a new approach for task assignment in an edge-core multicloud architecture to reduce power consumption in service centers using multilevel graph partitioning technique.Multilevel graph partitioning has three phases of coarsening, refinement and uncoarsening.For the refinement phase, a new algorithm based on a modified Kernighan-Lin algorithm is proposed which takes into account multiple constraints, and that mitigates the problem of stopping at a local minimum.Once tasks are assigned to the edge and core, multidimensional bin-packing is used to deploy tasks to individual hosts so that power consumption can be calculated.The approach is validated by comparing it to extended simulated annealing and an extended modified Kernighan-Lin algorithm.The experiments show that our approach is fast and produces better results.It is also less prone to failure in finding a feasible deployment for given constraints.me grow and mature as a serious graduate researcher and made this thesis an enjoyable experience.I thank members of SAVI group for their valuable comments and ideas, which were indispensable in producing this thesis.I would like to acknowledge and thank my colleagues Adnan Faisal, Farhana Islam and Derek Hawker for their friendly, often philosophical, discussions which made me laugh and kept me engaged.I would also like to express my gratitude to the professors, staff and students at the department of Systems and Computer Engineering for making my work an enjoyable experience.I would like to thank the most supportive, affectionate and
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".