Bibliographic record
Abstract
With the growing popularity of smart applications that contain computing-intensive tasks, the provision of radio and computing resources with high quality is becoming more and more challenging. Moreover, supporting network scalability is crucial to accommodate the massive numbers of connected devices. In this thesis, we present effective energy saving strategies that consider the utilization of network elements such as base stations and virtual machines, and implement on/off mechanisms taking into account the quality of service (QoS) required by mobile users. Moreover, we investigate the performance of a NOMA-based resource allocation scheme in the context of Internet of Things aiming to improve network scalability and reduce the energy consumption of mobile users. The system model is mainly built upon the M/M/k queueing system that has been widely used in most relevant works. First, the energy saving mechanism is formulated as a 0-1 knapsack problem where the weight and value of each small base station is determined by the utilization and proportion of computing tasks at that base station, respectively. The problem is then solved using the dynamic programming approach which showed significant energy saving performance while maintaining the cloud response time at desired levels. Afterwards, the energy saving mechanism is applied on edge computing to reduce the amount of under-utilized virtual machines in edge devices. Herein, the square-root staffing rule and the Halfin-Whitt function are used to determine the minimum number of virtual machines required to maintain the queueing probability below a threshold value. On the user level, reducing energy consumption can be achieved by maximizing data rate provision to reduce the task completion time, and hence, the transmission energy. Herein, a NOMA-based scheme is introduced, particularly, the sparse code multiple access (SCMA) technique that allows subcarriers to be shared by multiple users. Not only does SCMA help provide higher data rates but also increase the number of accommodated users. In this context, a power optimization and codebook allocation problems are formulated and solved using the water-filling and heuristic approaches, respectively. Results show that SCMA can significantly improve data rate provision and accommodate more mobile users with improved user satisfaction.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".