Mobile Telecom Cloud brokerage with orchestrated multi-tier resource pooling
Bibliographic record
Abstract
Mobile Telecom Cloud (MTC) refers to cloud services provided by mobile telecommunication companies. Since mobile network operators support the last-mile Internet access to users, they have advantages over other cloud providers by providing users with better mobile connectivity and required quality of service (QoS). Compared to other third-party cloud (TPC) providers such as Amazon and Google, however, MTC providers usually have a much smaller scale of computing resources. This paper presents an approach for MTC providers to take advantage of both MTC and TPC so that they can offer better and cheaper cloud services to their customers. The opportunity comes from the discounts that TPC normally offers to group buyers as well as the better QoS that MTC can support. Our solution is to allow MTC providers to act as a brokerage, called MTC brokerage, to broker TPC cloud resources and integrate the resources reserved from TPC with those of their own MTC. We address the technical challenges of optimally allocating users' cloud requests to MTC and TPC data centres to meet users' QoS requirement with minimum cost. We also study the price range that can be profitable to a MTC brokerage. We then investigate the resource reservation problem with dynamic request changes. We evaluate our solution using real Google traces collected over a 29-day period from a Google cluster. The results show that our solution results in cost savings for both MTC providers and their customers.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".