A Distributed Auction-based Framework for Scalable IaaS Provisioning in Geo-Data Centers
Bibliographic record
Abstract
This paper proposes a Cloud Infrastructure-as-a-Service (IaaS) framework that allows customers to have their high performance computing applications hosted efficiently and Cloud Service Providers (CSPs) to use their resources profitably. The solution introduces a distributed architecture that manages geographically distributed Data Centers (Geo-Data Centers) logically grouped in regions. This framework overcomes the challenges of traditional centralized provisioning approaches: (a) efficient provisioning of IaaS demand, (b) scale with respect to the growing number of IaaS requests, (c) guarantee of the stringent Quality of Service requirements of IaaS requests, and (d) efficient use of Cloud Geo-Data Center computing resources. Our architecture incorporates two decentralized approaches, hierarchical and distributed, that use auctions instead of a pay-as-you-go pricing scheme. The two approaches use a large-scale optimization technique for the allocation of Geo-Data Centers computing resources. The results of a simulation demonstrate an efficient use of computing resources and a significant reduction in computation time. This ensures adequate scalability to meet an exponential growth of IaaS demand. The auction-based approaches are also shown to provide monetary benefits to the participants.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".