Hierarchical Fog-Cloud Computing for IoT Systems: A Computation Offloading Game
Bibliographic record
Abstract
Fog computing, which provides low-latency computing services at the network edge, is an enabler for the emerging Internet of Things (IoT) systems. In this paper, we study the allocation of fog computing resources to the IoT users in a hierarchical computing paradigm including fog and remote cloud computing services. We formulate a computation offloading game to model the competition between IoT users and allocate the limited processing power of fog nodes efficiently. Each user aims to maximize its own quality of experience (QoE), which reflects its satisfaction of using computing services in terms of the reduction in computation energy and delay. Utilizing a potential game approach, we prove the existence of a pure Nash equilibrium (NE) and provide an upper bound for the price of anarchy. Since the time complexity to reach the equilibrium increases exponentially in the number of users, we further propose a near-optimal resource allocation mechanism and prove that in a system with N IoT users, it achieves an ε-NE in O(N/ε time. Through numerical studies, we evaluate the users' QoE as well as the equilibrium efficiency. Our results reveal that by utilizing the proposed mechanism, more users benefit from computing services in comparison to an existing offloading mechanism. We further show that our proposed mechanism significantly reduces the computation delay and enables low-latency fog computing services for delay-sensitive IoT applications.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".