PRE-Fog: IoT trace based probabilistic resource estimation at Fog
Bibliographic record
Abstract
Lately, pervasive and ubiquitous computing services have been under focus of not only the research community, but developers as well. Different devices generate different types of data with different frequencies. Emergency, healthcare, and latency sensitive services require real-time responses. Also, it is necessary to decide what type of data has to be uploaded to the cloud, without burdening the core network and the cloud. For this purpose, the cloud on the edge of the network, known as Fog or Micro Datacenter (MDC), plays an important role. Fog resides between the underlying Internet of Things (IoTs) and the mega datacenter cloud. Its purpose is to manage resources, perform data filtration, preprocessing, and security measures. To achieve this, Fog requires an effective and efficient resource management framework, which we propose in this paper. Fog has to deal with mobile nodes and IoTs, which involves objects and devices of different types having a fluctuating connectivity behavior. All such types of service customers have an unpredictable relinquish probability, since any object or device can stop using resources at any moment. In our proposed methodology for resource estimation and management through Fog computing, we take into account these factors and formulate resource management on the basis of fluctuating relinquish probability of the customer, service type, service price, and variance of the relinquish probability. With the intent of showing practical implications of our method, we implemented it on Crawdad real trace and Amazon EC2 pricing. Based on various services, differentiated through Amazon's price plans and historical record of Cloud Service Customers (CSCs), the model determines the amount of resources to be allocated. More loyal CSCs get better services, while for the contrary case, the provider reserves resources cautiously.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".