An Approximation Mechanism for Elastic IoT Application Deployment
Bibliographic record
Abstract
Even though Internet of Things (IoT) applications are proliferating exponentially in recent years, there still exist several resource allocation problems in IoT application implementations with the traditional cloud computing. Because the number of IoT applications is increasing day by day with strict latency requirements. They become a burden to the cloud/datacenter platform in order to fulfill a huge number of real-time IoT services. As the emerging solution for latency requirements, the edges can bring processing power closer to datasource - the Thing in IoT. However, with the resource limitation at edges, the efficient resource allocation is a major concern to improve the performance of edge networks. In this work, we introduce the optimization model, named the Service-Oriented Resource Allocation (SORA) for IoT applications, which dynamically consolidates the system so as to reduce the system cost while improving the available resource at the edges. Unfortunately, SORA is unable to solve in polynomial time because it is NP-hard. Unlike the prior works that try to find solutions based on heuristic algorithms, we propose approximation algorithms to solve SORA with a near-optimal solution. Finally, we evaluate our model by providing several simulation cases, in which our proposed mechanisms show outstanding outcomes in terms of solving SORA and resource utilization.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".