Decentralized Cache-aided Offloading in Edge Cloud Collaborative Environment using Deep Q Reinforcement Learning
Bibliographic record
Abstract
Many emerging applications such as augmented reality, facial recognition, autonomous cars, and e-health require heavy computation, and the processed results have to be available to the user in the order of milliseconds.Edge computing combined with cloud computing can address this challenge by distributing the load (offloading) on different connected computing resources.However, effective task offloading requires an efficient resource management framework.Many existing offloading methodologies consider only latency and energy consumption in a pre-defined network configuration implemented on a small scale.In addition, the effect of the location of the offloading algorithm has not been extensively studied.This thesis introduces a novel adaptive offloading framework using Online Deep Q reinforcement learning.The proposed framework considers strict latency constraints, high state space, rapidly changing user mobility, heterogeneous resources, and stochastic task arrival rate.The proposed research also highlights the importance of caching and introduces a novel concept called "container caching" that caches the dependencies of popular applications.Therefore, offloading decisions are taken to minimize energy consumption, latency, and caching costs.Moreover, the significance of deployment location of the offloading algorithm is also reviewed, and a distributed offloading method is proposed.Extensive simulations in a discrete event simulator implemented in Java using realistic profiles of tasks have been conducted.Simulation results and comparisons with existing benchmarking algorithms showed remarkable performance in terms of energy consumption, network traffic, task failures, remaining power on a large scale demonstrated the feasibility of the proposed approach.
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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) | 0.001 | 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".