Optimal Computational Task Offloading to a Edge Server with Firm Deadlines
Bibliographic record
Abstract
A system consists of a signal processing unit that has limited signal processing capabilities.Signal data entering the system constitute multiple segments that all have a corresponding deadline to be successfully processed or expires.The processing unit is able to offload data to a cloudlet with intermittent availability for remote processing.The goal in this system is to describe an optimal offloading policy.A Markov Decision Process (MDP) is used to model the aforementioned system and a Dynamic Programming (DP) equation is used to describe an optimal policy.This DP equation suffers from the "Curse of Dimensionality" which renders its computations intractable.A result is presented that allows one to evaluate this DP equation over a finite subset of the system state space.Properties of the optimal policy are described which can further reduce the computational load.Finally, numerical results are presented to verify the theoretical results.I am deeply indebted to my colleague the post-doc research assistant Khai Doan for spending his time to discuss what to write in this thesis and for his incredible support as we worked tirelessly together to obtain the lean state result and several numerical results.Without him, this thesis would not have come to fruition.I would like to thank my supervisor Professor Ioannis Lambadaris for his subtle but deep insights in stochastic optimal control theory.Some small moments that may seem insignificant to him, such as when we discussed the representation of the state of the system, changed my perspective on how to think and approach problems involving the Markov Decision Process framework.He also helped improve the precision I need for thinking and writing.And lastly I would like to thank Professor Yannis Viniotis, Professor Evangelos Kranakis, and Thiago Da Silva Gomides.I would like to thank all three of them for meeting virtually, along with Professor Lambadaris and Khai, to discuss our research problems.I would like to thank Professor Kranakis for his major contribution for the proof that the number of possible reduced states is the Catalan number.And finally I would like to thank Professor Viniotis for dedicating hours of his personal time to provide his great wisdom and deep insights on discrete-time systems in order to address subtle problems that I and Khai had.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".