Bibliographic record
Abstract
Fog computing is a paradigm in which resources are close to the end-users, complementing cloud computing and allowing the execution of workloads with reduced latency.Fog computing enables the deployment of new applications with low-latency requirements and can improve the execution of typical cloud applications.Fog computing relies on fog nodes, facilities with processing, networking, and storage resources placed in the continuum between end-users and the cloud.An early step in the design of a fog computing infrastructure is the location of fog nodes.This decision is crucial because end-users are mobile and, consequently, fog nodes must be deployed in different geographical regions to meet the latency requirements of applications.Moreover, users' demands are variable in time.Therefore, the location of fog nodes as well as their hardware configuration must take into account the variable demands of end-users in time and space.This thesis proposes solutions to the location of fog nodes considering different aspects of a fog computing infrastructure.First, a solution to reduce the capital expenditure of the infrastructure is proposed.Second, the location of fog nodes is decided so that the end-user devices can reduce their energy consumption.Third, solutions with mobile fog nodes mounted on unmanned aerial vehicles (UAVs) are investigated.Finally, a resource allocation mechanism for fog-cloud infrastructures is proposed.All solutions aim at providing the best infrastructure for end-users running workloads with low-latency requirements.Different solutions can be individually applied or combined.In this thesis, the fog node location problem is formulated as linear programming models, and different heuristic algorithms are proposed to deal with scenarios representing metropolitan areas.All evaluations were made using simulations.The evaluation of solutions in this thesis was made using simulations of metropolitan areas inhabited by millions of people.Fog nodes are characterized by their location and processing capacity.UAVs with operations limited by batteries are also simulated as fog nodes.Results show that, although dealing with variable demands is challenging, different solutions are possible to reduce underutilization of resources, such as slightly reducing the acceptance of requests to obtain large savings with the deployment costs, or employing UAVs to process peaks of demands.The proposed algorithms were shown to be scalable.The work in this thesis pushes the boundaries of the knowledge of the fog node location problem and can be adapted for future work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".