A Comparative Study of Task Offloading Approaches in the Edge-Cloud Paradigm
Bibliographic record
Abstract
Cloud computing has been instrumental in expanding the reach and capabilities of computing, storage, and networking infrastructure to applications. However, its centralized architecture leads to increased latency, bandwidth consumption, and service interruptions, making it unsuitable for latency-sensitive applications such as autonomous vehicles, smart cities, and industrial automation. Edge computing mitigates these challenges by relocating computation closer to data sources, such as IoT devices and edge servers, thereby reducing response time and enhancing Quality of Service (QoS). Instead of replacing cloud computing, edge computing works together with it to create a hybrid edge cloud system that combines fast local processing with the cloud’s powerful storage and computing capacity. However, this integration imposes challenges in efficient task offloading and scheduling and resource management. This paper presents a comprehensive analysis of several existing task-offloading strategies of edge-cloud environments in terms of several performance metrics such as task failure, network delay, processing time and edge utilization. The fuzzy logic-based algorithm is found out to be the most effective task offloading algorithm among all considered algorithms as it considers a number of network and computational resources while making task offloading decisions.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".